CN116743791A - Cloud edge synchronization method, device and equipment for subway cloud platform and storage medium - Google Patents
Cloud edge synchronization method, device and equipment for subway cloud platform and storage medium Download PDFInfo
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- H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
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Abstract
The embodiment of the application discloses a cloud edge synchronization method, device and equipment of a subway cloud platform and a storage medium, which can be applied to the technical field of cloud and intelligent traffic, wherein the method comprises the following steps: pulling first working data from each station device in the rail transit system in an active detection mode, and determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data is used for indicating whether each station device in the rail transit system is in a working state or not; acquiring second abnormal data sent by station management equipment, wherein the second abnormal data is determined based on second working data of each station equipment in the rail transit system, and the second working data is used for indicating working index data of each station equipment in the rail transit system; and alarming based on the first abnormal data and the second abnormal data. By adopting the embodiment of the application, the efficiency of data alarm in the rail transit system can be improved.
Description
Technical Field
The application relates to the technical field of cloud technology or intelligent traffic, in particular to a cloud edge synchronization method, device and equipment for a subway cloud platform and a storage medium.
Background
Station-level equipment in the urban rail transit industry, such as station management equipment, is generally autonomous and can independently operate without being influenced by the outside, so that the station management equipment can form a self-closed-loop alarm mechanism once any equipment in a rail transit system corresponding to a station fails, or early warns and the like. However, when all the equipment of the station including the station management equipment also fails, operators cannot perceive that the station management equipment has problems at the first time. Therefore, the alarm mode has lower efficiency, and the data alarm efficiency is reduced to a certain extent.
Disclosure of Invention
The embodiment of the application provides a cloud edge synchronization method, device and equipment for a subway cloud platform and a storage medium, which can improve the efficiency of data alarming in a rail transit system.
In a first aspect, the application provides a cloud edge synchronization method of a subway cloud platform, which is applied to central management equipment, wherein the central management equipment is in communication connection with a rail transit system, the rail transit system comprises at least one station management equipment and a plurality of station equipment corresponding to each station management equipment, the central management equipment is used for monitoring the station management equipment and the plurality of station equipment corresponding to the station management equipment, and the station management equipment is used for monitoring the plurality of station equipment corresponding to the station management equipment; the cloud edge synchronization method of the subway cloud platform comprises the following steps:
Pulling first working data from each station device in the rail transit system in an active detection mode, and determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data is used for indicating whether each station device in the rail transit system is in a working state or not;
acquiring second abnormal data sent by station management equipment, wherein the second abnormal data is determined based on second working data of each station equipment in the rail transit system, and the second working data is used for indicating working index data of each station equipment in the rail transit system;
and alarming based on the first abnormal data and the second abnormal data.
In a second aspect, the application provides a cloud edge synchronization device of a subway cloud platform, which is deployed in central management equipment, wherein the central management equipment is in communication connection with a rail transit system, the rail transit system comprises at least one station management equipment and a plurality of station equipment corresponding to each station management equipment, the central management equipment is used for monitoring the station management equipment and the plurality of station equipment corresponding to the station management equipment, and the station management equipment is used for monitoring the plurality of station equipment corresponding to the station management equipment; this subway cloud platform cloud limit synchronizer includes:
The first acquisition unit is used for pulling first working data from each station device in the rail transit system in an active detection mode, determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data are used for indicating whether each station device in the rail transit system is in a working state or not;
the second acquisition unit is used for acquiring second abnormal data sent by the station management equipment, wherein the second abnormal data is determined based on second working data of each station equipment in the rail transit system, and the second working data is used for indicating working index data of each station equipment in the rail transit system;
and the data alarming unit is used for alarming based on the first abnormal data and the second abnormal data.
In a third aspect, the present application provides a computer device comprising: a processor, a memory, a network interface;
the processor is connected with the memory and the network interface, wherein the network interface is used for providing a data communication function, the memory is used for storing a computer program, and the processor is used for calling the computer program so that the computer equipment comprising the processor executes the cloud edge synchronization method of the subway cloud platform.
In a fourth aspect, the present application provides a computer readable storage medium, in which a computer program is stored, the computer program being adapted to be loaded and executed by a processor, so that a computer device having the processor performs the above-mentioned metro cloud platform cloud edge synchronization method.
In a fifth aspect, the present application provides a computer program product or a computer program, which comprises computer instructions, which when executed by a processor, implement the above described metro cloud platform cloud edge synchronization method.
In the embodiment of the application, the first working data is pulled from each station device in the rail transit system in an active detection mode to determine the first abnormal data, the second abnormal data sent by the station management device is obtained, and the warning is carried out based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent.
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 will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic architecture diagram of a cloud edge synchronization system of a subway cloud platform provided by an embodiment of the application;
fig. 2 is an application scenario schematic diagram of a cloud edge synchronization method of a subway cloud platform provided by an embodiment of the application;
fig. 3 is a schematic flow chart of a cloud edge synchronization method of a subway cloud platform provided by the embodiment of the application;
FIG. 4 is a schematic diagram of a data acquisition architecture according to an embodiment of the present application;
FIG. 5 is a flow chart of a method for data alerting according to an embodiment of the present application;
fig. 6 is a flow chart of a data display method according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a display interface provided by an embodiment of the present application;
FIG. 8 is a schematic diagram of another display interface provided by an embodiment of the present application;
FIG. 9 is a schematic diagram of an overall architecture of a system according to an embodiment of the present application;
Fig. 10 is a schematic diagram of a composition structure of a cloud edge synchronization device of a subway cloud platform provided by an embodiment of the application;
fig. 11 is a schematic diagram of a composition structure of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The intelligent transportation system (Intelligent Traffic System, ITS), also called intelligent transportation system (Intelligent Transportation System), is a comprehensive transportation system which uses advanced scientific technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operation study, artificial intelligence, etc.) effectively and comprehensively for transportation, service control and vehicle manufacturing, and enhances the connection among vehicles, roads and users, thereby forming a comprehensive transportation system for guaranteeing safety, improving efficiency, improving environment and saving energy.
The data related to the user information in the embodiment of the application are all data after the user authorization. The application can be applied to an intelligent transportation system, for example, the first working data of each station device in the rail transportation system can be acquired by using the intelligent transportation system, and the first abnormal data of the rail transportation system can be determined based on the first working data of each station device, so that the warning is realized. The technical scheme of the application is suitable for detecting the working data of each station device in the rail transit system and determining whether the rail transit system is abnormal or not, thereby being used in a scene of alarming based on the abnormality. The embodiment of the application can also be applied to various scenes, including but not limited to cloud technology, artificial intelligence, intelligent transportation, auxiliary driving and the like. For example, the embodiment of the application can be applied to a subway cloud platform to realize cloud edge synchronization of working data of each station device in a rail transit system, thereby better realizing management of each station device in the rail transit system.
Referring to fig. 1, fig. 1 is a network architecture diagram of a metro cloud platform cloud edge synchronization system provided by an embodiment of the present application, as shown in fig. 1, a central management device 11 may perform data interaction with a station management device 12, where the station management device 12 may be used to manage a plurality of station devices such as 121, 122, 123 in a rail transit system. The number of station management devices may be plural, and when the number of station management devices is plural, as shown in fig. 13, the station management device 13 may be used to manage plural station devices such as 131, 132, 133, etc. in the rail transit system. The central management apparatus 11 may employ the same management method for each station management apparatus. Taking the station management device 12 as an example, the central management device 11 may acquire first working data of each station device in the rail transit system, and determine first abnormal data of the rail transit system based on the first working data of each station device. Alternatively, the station managing apparatus 12 may acquire second work data of each station apparatus in the rail transit system, and determine second abnormality data of the rail transit system based on the second work data of each station apparatus. Further, the central management apparatus 11 may acquire the second abnormality data transmitted from the station management apparatus 12, and transmit the first abnormality data and the second abnormality data to the unified warning system 14, so that a warning may be made based on the first abnormality data and the second abnormality data. Alternatively, the unified alarm system 14 may be a part of the central management device 11, or may be an alarm system having an association relationship with the central management device 11.
And pulling the first working data from each station device in the rail transit system in an active detection mode to determine first abnormal data, acquiring second abnormal data sent by station management equipment, and alarming based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent.
It is understood that the central management device mentioned in the embodiments of the present application may refer to a computer device, which may include, but is not limited to, a terminal device or a server. The station managing device may also be a computer device. In other words, the computer device may be a server or a terminal device, or may be a system formed by the server and the terminal device. The above-mentioned terminal device may be an electronic device, including, but not limited to, a mobile phone, a tablet computer, a desktop computer, a notebook computer, a palm computer, a vehicle-mounted terminal, an intelligent voice interaction device, an augmented Reality (AR/VR) device, a head mounted display, a wearable device, a smart speaker, a smart home appliance, an aircraft, a digital camera, a camera, and other mobile internet devices (mobile internet device, MID) with network access capability, etc. The servers mentioned above may be independent physical servers, or may be server clusters or distributed systems formed by a plurality of physical servers, or may be cloud servers that provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, vehicle-road collaboration, content distribution networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. That is, the central management device or station management device may refer to a terminal device, or a server cluster composed of one server or a plurality of servers, or a system composed of a server and a terminal device.
Further, referring to fig. 2, fig. 2 is a schematic application scenario diagram of a cloud edge synchronization method for a metro cloud platform according to an embodiment of the present application. As shown in fig. 2, for example, the central management device 21 may pull the first working data from each station device including the device 22 (i.e., station management device), the device 23, the device 24, and the device 25 in the rail transit system by means of active probing, for example, the first working data may indicate whether the station device in the rail transit system is in a working state, and the first abnormality data is determined based on the first working data. Alternatively, the station management device 22 may acquire the second working data reported by each station device in the rail transit system, where the second working data includes the device 23, the device 24, and the device 25, and the second working data may indicate working index data of the station device in the rail transit system, for example, current server resource occupation condition, device service condition, process running condition, network running condition of the station device, and so on. Second anomaly data is determined based on the second work data. Further, the station management apparatus 22 may transmit the second abnormality data to the central management apparatus 21, and the central management apparatus 21 may alarm based on the first abnormality data and the second abnormality data.
Further, referring to fig. 3, fig. 3 is a schematic flow chart of a cloud edge synchronization method of a metro cloud platform according to an embodiment of the present application; as shown in fig. 3, the cloud edge synchronization method of the subway cloud platform can be applied to central management equipment, the central management equipment is in communication connection with a rail transit system, the rail transit system comprises at least one station management equipment and a plurality of station equipment corresponding to each station management equipment, the central management equipment is used for monitoring the station management equipment and the plurality of station equipment corresponding to the station management equipment, and the station management equipment is used for monitoring the plurality of station equipment corresponding to the station management equipment.
The cloud edge synchronization method of the subway cloud platform comprises the following steps:
s101, pulling first working data from each station device in the rail transit system in an active probing mode, and determining first abnormal data of the rail transit system based on the first working data of each station device.
In the embodiment of the application, the central management equipment can pull the first working data from each station equipment in the rail transit system in an active probing mode. Optionally, the central management device may also receive first working data sent by each station device in the rail transit system, or may also receive first working data sent by the station management device, where the first working data may be sent by each station device in the rail transit system to the station management device. The active probing may mean that the central management device initiates a transmission control protocol (Transmission Control Protocol, TCP) handshake, establishes connection with the station device, and the connection establishment success represents that the station device is online and the connection establishment failure represents that the station device is offline.
The central management equipment can monitor and control each station equipment in the rail transit system, and can give an alarm when determining that any station equipment in the rail transit system has abnormal conditions, so that the abnormal equipment is processed, and the normal operation of the rail transit system is ensured. Station equipment in a rail transit system may include, but is not limited to, all equipment within a station, for example, may include display screen related equipment on a train, train gate related equipment, station entrance gate related equipment, train door opening and closing control equipment, station power supply related equipment, station management equipment, and the like. Trains may refer to subways, light rails, maglev trains, and the like. Each station may correspond to a station management device, i.e. the station management device may manage all station devices in the rail transit system in the station. For example, 50 station apparatuses are included in the rail transit system in station a, of which 1 station apparatus is station management apparatus that can manage the other 49 station apparatuses and manage itself. The first operational data may include operational data of the 50 station apparatuses. The first working data may be used to indicate whether each station device in the rail transit system is in a working state, for example, the first working data may include, but is not limited to, a communication state and a communication time consumption of the station device. The connectivity status may include online and offline, and the connectivity time may be used to indicate the duration that the central management facility needs to consume to connect each station facility. Whether the station equipment is in a working state or not can be determined by acquiring the communication state of the station equipment, the network condition of the station equipment or the performance condition of the station equipment can be determined by acquiring the communication time consumption, and the like. Because station equipment in the rail transit system comprises station management equipment, the central management equipment can also manage the station management equipment, and the situation that abnormal reporting cannot be realized due to the fact that the station management equipment is abnormal is avoided, so that the data alarm efficiency can be improved.
Optionally, if the first working data includes a communication state and a communication time consumption of each station device, when the communication state of each station device is online, the central management device may determine that the rail transit system is in a normal working state, and no abnormality occurs in the station device. Or when the communication state of each station device is online and the communication time consumption of each station device is smaller than the time consumption threshold, the central management device can determine that the rail transit system is in a normal working state, and no abnormality occurs to the station device. Optionally, if the communication state of any station device is online and the communication time is greater than or equal to the time consumption threshold, the central management device may determine that the current network of the rail transit system is abnormal, or that the performance of the station device may be abnormal, and may determine the abnormal data as the first abnormal data. Or if the communication state of any station equipment in the rail transit system is offline or the communication time is greater than or equal to the time consumption threshold, determining that the station equipment is abnormal, and determining data for indicating the station equipment is abnormal as first abnormal data. The first anomaly data may include an identification of the station apparatus and a cause of anomaly of the station apparatus, such as an offline station apparatus, a time-consuming communication anomaly, and so on.
Optionally, when the station management device obtains the first working data, if the first working data indicates that each station device in the rail transit system is normal, working index data of each station device may be obtained, and whether each station device is abnormal is determined by further obtaining the working index data of each station device in the rail transit system. For example, the work index data may include at least one of server resource data, device service data, process operation data, network operation data, database operation data, and message queue operation data of each station device in the rail transit system. The server resource data of the device may include data used for comprehensively representing the health state of the device, such as a CPU, a memory, a disk, a network, and the like. The device service data may be used as related data indicating whether the device service belongs to a normal state. The process running data may include data used by a CPU, memory, disk, network, etc. to comprehensively represent the health status of the process. The network operation data may include data for monitoring network connectivity and IO load levels, etc., to represent the health of the network as a whole. The database operation data may include the cluster state of the relational database, the resource usage condition and other related data for reflecting the current health condition of the relational database, and may also include the state of the non-relational database cluster itself, the resource usage condition and other related data for reflecting the current health condition of the non-relational database. The message queue operation data may include the status of the message queue cluster itself, the resource usage, and relevant index data such as message backlog for reflecting the current health of the message queue.
Optionally, after the central management device obtains the working index data of each station device in the rail transit system, the first abnormal data of the rail transit system may be determined based on the working index data of each station device. Specifically, the central management device may acquire a data type of working index data of each station device in the rail transit system; matching abnormal data judgment rules based on the data types; determining whether the working index data is abnormal or not based on the abnormal data judging rule; and determining the work index data with the abnormality as first abnormal data.
The central management device can classify the working index data and determine the data type of the working index data. The data type of the work index data may include, but is not limited to, a status type index (i.e., status type data), a time-consuming type index (i.e., time-consuming type data), a resource type index (i.e., resource type data), a comprehensive type index (i.e., comprehensive type data), and so on. Optionally, if the number of the working index data is multiple, the central management device may determine the data type of each working index data, determine whether the corresponding working index data is abnormal based on the abnormal data determination rule based on the data type of each working index data matching the corresponding abnormal data determination rule, and determine at least one working index data having an abnormality in the multiple working index data as the first abnormal data. Optionally, if the number of pieces of work index data is one, the central management device may match the abnormal data judgment rule based on the data type of the piece of work index data; determining whether the piece of work index data is abnormal or not based on an abnormal data judging rule; and if the piece of work index data is abnormal, determining the piece of work index data as first abnormal data.
Optionally, if the data type of the working index data is a status type index, it may be determined that an abnormal data judgment rule matched with the status type index is a first judgment rule, where the first judgment rule is used to judge whether the working index data is in a preset status. For example, the working index data refers to a connected state of the device, the preset state may refer to an on-line state, if the connected state of the device is the on-line state, the working index data is normal, if the connected state of the device is the off-line state, the working index data is abnormal, and the abnormal working index data is determined as first abnormal data.
If the data type of the work index data is a time-consuming index, determining an abnormal data judging rule matched with the time-consuming index as a second judging rule, wherein the second judging rule is used for judging whether the work index data exceeds a specified duration range. For example, the working index data refers to the communication time consumption of the equipment, if the communication time consumption of the equipment exceeds a specified time length range, for example, the communication time consumption is greater than or equal to a time consumption threshold, and the communication time consumption exceeds the specified time length range, determining that the working index data of the equipment is abnormal, and determining the abnormal working index data as first abnormal data.
If the data type of the working index data is a resource type index, determining an abnormal data judging rule matched with the resource type index as a third judging rule, wherein the third judging rule is used for judging whether the use condition of the resources in the working index data exceeds a specified resource range. For example, the work index data refers to a memory space occupied by the station equipment, if the memory space occupied is larger than a specified resource range, it is determined that there is an abnormality in the work index data, and the abnormal work index data is determined as first abnormal data.
If the data type of the work index data is a comprehensive index, determining that the abnormal data judging rule matched with the comprehensive index is a fourth judging rule, wherein the fourth judging rule is used for comprehensively calculating whether the work index data belongs to the abnormal index according to the rule. The comprehensive index may be that there is an association relationship among a plurality of indexes, and a change of one index affects other indexes. For example, the integrated index includes a resource type index including a disk space occupation condition and a time-consuming type index including a CPU running speed. If the occupied space of the magnetic disk is smaller than the first occupied threshold, the running speed of the CPU is larger than or equal to the first speed threshold; if the disk occupation space is larger than or equal to the first occupation threshold, the CPU running speed is smaller than the first speed threshold. For example, if the disk space occupation does not exceed the specified resource range, the CPU running speed is less than the first speed threshold, indicating that there is an abnormality in the work index data, and determining the abnormal work index data as the first abnormal data. The first occupancy threshold belongs to a specified resource range. If the disk space occupation exceeds the specified resource range, the CPU running speed is smaller than the first speed threshold value, and the working index data can be indicated to be normal. That is, the CPU operation speed is reduced to some extent due to the change of the disk occupation space, and at this time, if the CPU operation speed is within the normal speed range, it may indicate that the operation index data of the device is normal.
Optionally, if the first working data indicates that any equipment in the rail transit system is abnormal, the first working data is determined to be the first abnormal data, and working index data of each station equipment in the rail transit system is not required to be acquired again to judge whether the abnormal data exists, so that the abnormal judging efficiency can be improved, and the data judging efficiency can be improved.
Alternatively, the first working data may also be used to indicate working index data of each station device in the rail transit system, and the first abnormal data of the rail transit system may be determined based on the working index data of each station device. For example, the abnormality determination rule may be matched based on the data type of the work index data, so that it is determined whether the first work data is the abnormality data based on the corresponding abnormality determination rule. If the first working data are used for indicating the working index data of each station device in the rail transit system, the abnormal condition of each station device in the rail transit system can be determined in a finer granularity, and the accuracy of abnormality determination is improved.
Alternatively, the central management apparatus may acquire the work index data in real time, and determine whether the work index data is abnormal based on the work index data and the abnormality data judgment rule. Or, the working index data in a period of time can be acquired, a summarizing algorithm such as summing, average, median and the like is performed on the working index data in a period of time, a certain type of working data at different moments is calculated, whether the type of working index data is abnormal or not is determined, and the accuracy of data processing can be improved by comprehensively judging the working index data in a period of time.
Optionally, the central management device may acquire the working index data of each station device in the rail transit system according to the target period. For example, a target period, that is, a data acquisition frequency, may be preset, and the central management device may pull the first working data from each station device in the rail transit system according to the data acquisition frequency, and by periodically acquiring the working data of the device, the reliability of the data may be improved.
Alternatively, the central management device may further acquire the first working data of each station device in the rail transit system in a case where the second abnormal data transmitted by the station management device is not acquired within the target period. Since the second abnormal data sent by the station management equipment is not obtained in the target time period, the second abnormal data may indicate that each station equipment in the rail transit system operates normally, or the abnormal station management equipment causes abnormal data to be unable to be sent, so that the central management equipment can judge and determine whether the equipment is abnormal by obtaining the first working data from each station equipment in the rail transit system, and the problem that the abnormal data cannot be sent due to the abnormal station management equipment, and the processing efficiency of the data is low is avoided. Under the condition that the second abnormal data sent by the station management equipment is not acquired in the target time period, the first working data of each station equipment in the rail transit system is triggered to be acquired, and the first working data of each station equipment in the rail transit system does not need to be acquired all the time, so that the resource consumption can be saved.
Optionally, the central management device may further acquire the first working data of each station device in the rail transit system when the indication information for indicating the normal operation of the rail transit system sent by the station management device is not acquired within the target time period. The station management equipment sends indication information for indicating that the track traffic system is normal to the central management equipment in the normal running process, and the central management equipment can know that the current track traffic system is in a normal running state. If the indication information is not received in the target time period, the station management equipment can be indicated to be abnormal, the station management equipment can be rapidly subjected to targeted abnormal processing, and the data alarm efficiency is improved. Under the condition that the indication information is not received in the target time period, the first working data of each station device in the rail transit system is triggered to be acquired, and the first working data of each station device in the rail transit system does not need to be acquired all the time, so that the resource consumption can be saved.
Optionally, the first working data may refer to working data of a device of a target class, the device of the target class may refer to a device with an importance level greater than a level threshold in the rail transit system, and the central management device may acquire the first working data of the device of the target class in the rail transit system, and determine whether abnormal data exists. Because the equipment of the target type is equipment with higher importance level in the track traffic system, the influence degree of the abnormality of the equipment of the target type on the track traffic system is larger than the equipment with importance level smaller than or equal to the level threshold value in the track traffic system, so that the equipment can be monitored. The equipment with lower importance level can be not monitored, so that the resource consumption can be reduced, and the data processing efficiency can be improved due to the reduction of the data quantity.
Alternatively, the central management device may acquire historical anomaly data, determine the target device based on the historical anomaly data, and then the central management device may acquire first working data of the target device in the rail transit system, and determine whether the anomaly data exists. The target device may be a device indicating that the frequency of occurrence of the abnormality is greater than the target frequency, or the target device may also be a device in which the cumulative operating period exceeds the target period, or a device in which the continuous operating period exceeds the target period. Because the probability of the occurrence of the abnormality of the target equipment is high, the working data of the target equipment can be monitored in a targeted manner, whether the target equipment is abnormal or not can be determined, the resource use can be saved, and the abnormality detection efficiency can be improved to a certain extent.
S102, acquiring second abnormal data sent by station management equipment.
In the embodiment of the application, the central management equipment can establish network connection with the station management equipment, so that the second abnormal data sent by the station management equipment can be acquired based on the network connection. Wherein the second anomaly data is determined based on second operational data of each station equipment in the rail transit system. The station management equipment can monitor each station equipment in the rail transit system, and when abnormal conditions exist in the equipment, abnormal data can be sent to the central management equipment, so that the abnormal equipment is managed, and normal operation of the rail transit system is ensured. Since the station equipment in the rail transit system includes station management equipment and other equipment in the station, the second work data also includes work data of the station management equipment. In other words, the station management device can also detect own working data to determine whether abnormal data exists, so that abnormal data of other station devices in the rail transit system cannot be reported to the central management device due to own abnormality is avoided, and the efficiency of data alarming can be improved.
The second working data may be used to indicate working index data of each station device in the rail transit system, where the working index data may include at least one of server resource data, device service data, process operation data, network operation data, database operation data, and message queue operation data of each station device in the rail transit system. The server resource data of the device may include data for comprehensively representing the health status of the device, such as a CPU, memory, disk, network, and the like. The device service data may be used as related data indicating whether the device service belongs to a normal state. The process running data may include data used by a CPU, memory, disk, network, etc. to comprehensively represent the health status of the process. The network operation data may include data for monitoring network connectivity and IO load levels, etc., to represent the health of the network as a whole. The database operation data may include the cluster state of the relational database, the resource usage condition and other related data for reflecting the current health condition of the relational database, and may also include the state of the non-relational database cluster itself, the resource usage condition and other related data for reflecting the current health condition of the non-relational database. The message queue operation data may include relevant indexes such as the status of the message queue cluster itself, the resource usage, and the message backlog for reflecting the current health of the message queue. That is, the first working data of each station device in the rail transit system acquired by the central management device may be the same as or different from the second working data of each station device in the rail transit system acquired by the station management device.
Referring to fig. 4, fig. 4 is a schematic diagram of a data collection architecture according to an embodiment of the present application, which mainly includes three parts, namely infrastructure-as-Service (Infrastructure as a Service, iaaS), platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). The IaaS may include server resource data, device service data, process operation data, and network operation data of the station device. The PaaS may include database operating data and message queue operating data, and the database operating data may include relational database operating data and non-relational database operating data. The SaaS may include a station management device, which may be configured to monitor and report service status and service resource related data of the station management device, and a central management device, which may be configured to provide a capability to support monitoring and controlling coverage.
Optionally, the server resource data may be data obtained by monitoring indexes related to the server resource, such as indexes of CPU, memory, disk, network, etc., where the indexes can comprehensively represent the health status of the server. The equipment service data can be related indexes of whether equipment service belongs to a normal state or not by monitoring, which is a key point in the aspect of rail transit, and the normal operation of a rail transit related system can be ensured only when station equipment is normal. The process running data can be indexes such as CPU, memory, disk, network and the like by monitoring the key service running state and resource related indexes, and the indexes can comprehensively reflect the health state of the process. The network operation data can be related indexes such as network connectivity, IO load degree and the like through monitoring. These indicators represent the overall health of the network, and if a problem occurs in the network, the problem that the service is not available or the response is not timely occurs. The relational database operating data may include databases such as Mysql, mariaDB, SQL Server, etc. The method mainly collects indexes such as cluster states, resource use conditions and the like of the relational database, and mainly collects relevant indexes capable of reflecting the current health condition of the relational database. The message queue operation data may include a message queue such as Pulsar, kafka, rocketMQ, and the collection of the data of the message queue mainly collects indexes such as status of the message queue cluster, resource usage condition, message backlog, and the like, and mainly collects related indexes capable of reflecting current health condition of the message queue. The non-relational database operational data may include operational data of a non-relational database such as Redis, mongoDB, elasticsearch, TDengine. The method mainly collects indexes such as the state of the non-relational database cluster, the use condition of resources and the like, and mainly collects related indexes capable of reflecting the current health condition of the non-relational database. By monitoring the layers, different data can be acquired from the layers, and the data can be further summarized to an index storage component of station management equipment, so that the subsequent processing of data uploading, data tracing and the like is facilitated.
Optionally, the station management device may establish a connection with each station device in the rail transit system, and each station device may actively report the second working data to the station management device, so that the station management device may obtain the second working data of each station device in real time, and determine the second abnormal data of the rail transit system based on the second working data. The station managing apparatus may acquire a data type of the second work data, and determine the second abnormal data based on the data type. Specifically, the station management device may acquire a data type of the second work data of each station device; matching abnormal data judgment rules based on the data types; determining whether the second working data is abnormal based on the abnormal data judging rule; and determining the second work data with the abnormality as second abnormal data. In the embodiment of the present application, the specific manner of determining the second abnormal data based on the data type may refer to the manner of determining the first abnormal data based on the data type in step S101, which is not described herein.
Alternatively, the central management device may determine an abnormal frequency of each station device in the rail transit system based on the first abnormal data and the second abnormal data, determine a probe frequency for each station device based on the abnormal frequency of each station device, and pull the first work data from each station device based on the probe frequency. For example, if the anomaly frequency is greater than the first anomaly frequency, then the probe frequency is greater than the first probe frequency; the abnormal frequency is smaller than or equal to the first abnormal frequency, and the probe activity frequency is smaller than or equal to the first probe activity frequency. That is, the higher the abnormal frequency, the higher the probe frequency, and the lower the abnormal frequency, the lower the probe frequency. That is, by determining the abnormal frequency of each station device in the rail transit system, the central management device may set a higher probe frequency for the station device with a higher abnormal frequency, and set a lower probe frequency for the station device with a lower abnormal frequency, so that when each station device in the rail transit system is actively probed, the working data of the corresponding station device is pulled in combination with the probe frequency to perform abnormal judgment, so that an alarm can be given under the condition of reducing resource consumption, and the abnormality detection efficiency can be improved to a certain extent.
Optionally, the central management device may further determine a probe frequency for each station device in the rail transit system, so as to probe each station device based on the probe frequency. Specifically, the central management device may acquire a probe activity factor for the rail transit system, and determine a probe activity frequency for each station device based on the probe activity factor; and pulling the first working data from each station equipment of the rail transit system in an active detection mode according to the detection frequency. The activity detection factors comprise one or more of departure frequency, people flow, environmental weather data, service duration of each station device and importance level of each station device.
The departure frequency may be determined based on a departure time interval of the train in the rail transit system, and the departure time interval of the train may refer to a time difference between departure of two trains. If the train departure time interval is smaller than the target interval, the departure frequency is larger than the first departure frequency, and the detection frequency is determined to be larger than the second detection frequency. If the train departure time interval is greater than or equal to the target interval, the departure frequency is less than or equal to the first departure frequency, and the detection frequency is determined to be less than or equal to the second detection frequency. Because the train departure time intervals are different, the corresponding departure frequencies are different, and when the train departure time intervals are larger, the use frequency of the corresponding station equipment is reduced, so that the lower detection frequency can be set to detect each equipment, and the resource space can be saved. When the train departure time interval is smaller, the use frequency of the corresponding station equipment is increased, so that higher detection frequency can be set to detect each equipment, and the efficiency and accuracy of abnormality determination can be improved.
The traffic can be determined by counting traffic corresponding to the station, for example, the number of people entering the station and the number of people exiting the station, the number of people working at the station, and the like. If the station people flow is greater than the first people flow, determining that the detection frequency is greater than the third detection frequency. If the flow of the station persons is smaller than or equal to the flow of the first persons, the detection frequency is determined to be smaller than or equal to the third detection frequency. Because under certain conditions such as early peak, late peak, holidays and the like, the traffic flow of the station is larger, the use frequency of station equipment is increased, and at the moment, the probability of abnormality occurrence of each station equipment in the rail transit system is larger, the higher detection frequency can be set for detecting each equipment, and the efficiency and the accuracy of abnormality detection are improved. Aiming at the condition that the traffic is less, the use frequency of station equipment is reduced, and the resource space can be saved by reducing the detection frequency of each equipment.
The environmental weather data can be determined based on the position of the station, and the weather condition and the environmental condition corresponding to the current station can be determined by acquiring the geographic position of the station, so that the detection frequency is determined. For example, when the station is currently in the first type of environment, it may be determined that the probe frequency is greater than the fourth probe frequency. When the station is currently in the second type of environment, the detection frequency can be determined to be smaller than or equal to the fourth detection frequency. The first type of environment can be the current weather which is severe weather such as storm, thunder, typhoon, hail and the like, or the situation that a station contains ponding and the like; the second type of environment may be a condition that the current weather is sunny, cloudy, rainy, and the like, and the station does not contain ponding, and the like. Because the frequency of the abnormality occurrence of station equipment in the rail transit system under the first type environment is higher than that of the abnormality occurrence under the second type environment, for example, the circuit abnormality of the rail transit system can be caused under the condition of station ponding, the higher detection frequency is set for detecting the activity of each equipment under the first type environment, and the efficiency and the accuracy of abnormality detection can be improved. And setting lower detection frequency to detect the activity of each device in the rail transit system in the second type of environment, so that the resource space can be saved.
The use duration of each station device may be determined based on a delivery date of each station device, may be determined based on an actual use date of each station device, or may be determined based on a continuous use duration of station devices. The corresponding detection frequency can be determined for each station device, so that the detection is performed based on the respective detection frequency. If the using time of the station equipment is longer than the first time, determining that the detection frequency of the station equipment is greater than the fifth detection frequency. If the using time length of the station equipment is smaller than or equal to the first time length, determining that the detection frequency of the station equipment is smaller than or equal to the fifth detection frequency. Because the using time length of each device is different, different probing frequencies are set for the using time length of each device, targeted probing can be realized, resource space is saved, and abnormality detection efficiency is improved to a certain extent.
The importance level of each station device may be determined based on the type of the station device, and if the station device belongs to the first type, it may be determined that the probe frequency for the station device is greater than the sixth probe frequency. If the station equipment belongs to the second type, the detection frequency for the station equipment can be determined to be smaller than or equal to the sixth detection frequency. The first type of station equipment may refer to equipment in the rail transit system with an importance level greater than a level threshold, and the first type of station equipment abnormality may affect the normal operation of the rail transit system, for example, the first type of station equipment may include station power supply related equipment, train gate related equipment, and the like. The second type of station equipment can be equipment with an importance level smaller than or equal to a level threshold value in the rail transit system, and the normal operation of the rail transit system is not affected by the abnormality of the second type of station equipment. For example, the second type of station apparatus may include display screen related equipment on a vehicle, station escalator related equipment, and so on. Because the functions of the equipment in the station are different, the functions in the rail transit system are different, and for the station equipment with higher importance level, the accuracy and efficiency of anomaly detection can be improved by setting higher detection frequency; for station equipment with lower importance level, space resources can be saved to a certain extent by setting lower detection frequency.
It can be understood that the central management device may determine the probe activity frequency for each device in the rail transit system based on any one of the departure frequency, the traffic volume, the environmental weather data, the use time of each station device, and the importance level of each station device, or may determine the probe activity frequency for each device in the rail transit system by combining two or more of them. For example, the detection frequency of each device in the rail transit system is determined based on the environmental weather data and the importance level of each station device, or the detection frequency of each device in the rail transit system is determined based on the environmental weather data, the use time length of each station device and the importance level of each station device, and so on.
Optionally, the central management device can also verify the identity of the station management device, determine the authenticity of the station management device, and improve the security of data interaction. Specifically, the central management device may acquire a data packet sent by the station management device, where the data packet includes second abnormal data and signature information, and the signature information is obtained by signing the second abnormal data with a private key of the station management device; acquiring a public key of station management equipment, and performing signature verification processing on signature information based on the public key; if the signature verification is successful, determining that the station management equipment is legal equipment and the second abnormal data has authenticity, and executing the step of alarming based on the first abnormal data and the second abnormal data. If the signature verification fails, determining that the station management equipment is illegal equipment or the second abnormal data does not have authenticity, and sending warning information for indicating that the station management equipment is risk equipment to the rail transit system.
The public key may refer to a public key in the asymmetric key pair, and the private key corresponds to the public key, that is, a key not disclosed in the asymmetric key. In a specific implementation, the station management device may use its own private key to sign the second abnormal data to obtain signature information, package the signature information and the second abnormal data into a data packet, and send the data packet to the central management device. The central management device may use the public key of the station management device to check the signature information in the data packet. If the signature verification is successful, the station management equipment is legal equipment, the second abnormal data has authenticity, namely the second abnormal data is sent by the station management equipment and is not tampered by other equipment, and an alarm can be given based on the first abnormal data and the second abnormal data.
Further, if the signature verification fails, it indicates that the station management device is an illegal device, that is, the second abnormal data is not sent by the station management device, or the second abnormal data does not have authenticity, for example, the second abnormal data is tampered with by other devices. The central management device may further send alert information to the rail transit system indicating that the station management device is a risk device. The risk device may mean that the station management device may be at risk, thereby prompting the relevant manager to check the station management device. By checking signature information, the identity of the equipment for sending the second abnormal data can be verified, the authenticity of the equipment and the authenticity of the second abnormal data are determined, the situation that a malicious terminal sends the data or falsifies the data and the management of a rail transit system is affected is avoided, and therefore the safety of data interaction can be improved.
S103, alarming is conducted based on the first abnormal data and the second abnormal data.
In the embodiment of the application, the central management device may include an alarm device, and when the first abnormal data and the second abnormal data are acquired, the alarm device alarms based on the first abnormal data and the second abnormal data. Alternatively, the central management apparatus may perform an alarm based on the first abnormal data when the first abnormal data is determined. Or, the central management device may perform an alarm based on the second abnormal data when acquiring the second abnormal data transmitted from the station management device. That is, the warning may be made based on the first abnormal data, or based on the second abnormal data, or based on the first abnormal data and the second abnormal data. The embodiment of the present application is not limited thereto.
Alternatively, when the first abnormal data is determined and the duration of the first abnormal data satisfies the abnormal threshold, the central management apparatus may alarm based on the first abnormal data. Alternatively, when the second abnormal data is determined and the duration of the second abnormal data satisfies the abnormal threshold, the station management system may transmit the second abnormal data to the central management apparatus. By judging the duration of the abnormal data, the influence caused by normal human operation factors such as equipment updating and the like can be avoided, and the accuracy of data alarming is improved.
Alternatively, when the station management apparatus determines the second abnormal data, the station management apparatus may also alarm based on the second abnormal data. Because a station corresponds to a station management device, the station management device of the station can be connected with the alarm center of the station, and when abnormal data occurs, the station management device can send abnormal information to the alarm center of the station so as to rapidly process the abnormality. Further, since a plurality of stations (for example, all stations) correspond to the same central management device, the method for processing the abnormal information corresponding to the central management device is more complete, for example, the central management device comprises more and more comprehensive abnormal processing schemes, so that the station management device can increase the processing force of abnormal data by sending the abnormal information to the central management device, and further improve the accuracy of abnormal processing.
Optionally, when the second abnormal data sent by the station management device is obtained, the central management device may further detect the relevant station device, so as to improve accuracy of abnormality determination. Specifically, when second abnormal data sent by the station management device is acquired, the central management device may send a connection request to an abnormal device corresponding to the second abnormal data in the rail transit system, where the connection request is used for establishing a connection with the abnormal device; if the connection with the abnormal equipment fails, alarming based on the second abnormal data; if the connection with the abnormal equipment is successful, the abnormal data of the abnormal equipment is acquired, and an alarm is given based on the abnormal data of the abnormal equipment. When the second abnormal data sent by the station management equipment is acquired, the central management equipment further connects the related station equipment to perform abnormal detection, so that the accuracy of the abnormal detection can be improved. If the connection between the first abnormal data and the abnormal equipment fails, determining the abnormal equipment, and alarming based on the second abnormal data; if the connection with the abnormal equipment is successful, the abnormal data of the abnormal equipment, such as the working data of the abnormal equipment, is further obtained, the working data of the abnormal equipment is subjected to abnormal judgment, if the abnormal working data is determined, the working data with the abnormality is determined to be the abnormal data, and the alarm is carried out based on the abnormal data of the abnormal equipment, so that the accuracy of the data alarm can be improved.
Optionally, the central management device may further acquire second working data sent by the station management device; and alarming by combining the second working data and the first working data pulled by the central management equipment in an active probing mode. Specifically, if the device corresponding to the first working data and the device corresponding to the second working data are the same device, and the first working data indicating device is normal and the second working data indicating device is abnormal, the second working data are determined to be second abnormal data, and an alarm is given based on the second abnormal data.
For example, if the second working data sent by the station management device is working data of the station device a, the second working data indicates that the station device a is in an abnormal working state. The central management equipment pulls first working data of the station equipment A in an active probing mode, and the first working data indicates that the station equipment A is in a normal working state. Since the first working data may be working data of coarse granularity of the pointer to the station equipment, for example, data for indicating whether the station equipment is in a working state, and the second working data may be working data of fine granularity of the pointer to the station equipment, for example, working index data including respective station equipment, it is possible to determine whether the station equipment a is abnormal based on the working data of fine granularity, and thus it is possible to improve accuracy of abnormality determination. For example, if the first working data indicates that the station equipment a is in a connected state, the station equipment a is in a normal working state, and the second working data indicates that the resource usage in the working index data of the station equipment a exceeds the specified resource range, an alarm can be given based on the second abnormal data, so that the resource usage of the station equipment a is adjusted, and the normal working of the station equipment is prevented from being influenced.
Further, under the condition that the first abnormal data and/or the second abnormal data are obtained, the central management device can be respectively matched with the corresponding alarm clusters based on the first abnormal data and/or the second abnormal data, so that the abnormal data are processed in a targeted manner, the alarm clusters can refer to abnormal processing teams, and the abnormalities processed by different abnormal processing teams are different. For example, when the first abnormal data and the second abnormal data are acquired, the central management device may match the alarm clusters based on the first abnormal data and the second abnormal data, respectively; the first abnormal data is sent to the alarm cluster matched with the first abnormal data for processing, and the second abnormal data is sent to the alarm cluster matched with the second abnormal data for processing.
Specifically, the central management device may preset a correspondence between an anomaly type of the anomaly data and the alert cluster, and when the anomaly data is acquired, the alert cluster is matched based on the anomaly type of the anomaly data. For example, the anomaly type is server resource anomaly, and can correspond to a server processing alarm cluster; the abnormality type is equipment service abnormality and can correspond to an equipment service alarm cluster; the abnormal type is abnormal operation of the process and can correspond to a process alarm cluster; the abnormal type is network operation abnormality and can correspond to a network alarm cluster; the abnormal type is abnormal operation of the database and can correspond to a database alarm cluster; the exception type is message queue exception and can correspond to message queue alarm clusters. The central management device may also determine a correspondence between the abnormal data and the alarm cluster based on other methods, so that the alarm cluster may be matched when the abnormal data is acquired. By respectively sending the first abnormal data to the alarm clusters matched with the first abnormal data and sending the second abnormal data to the alarm clusters matched with the second abnormal data, the staff corresponding to the alarm clusters can process the abnormality of the corresponding equipment, the abnormal data can be processed in a targeted mode, and the processing efficiency of the data is improved.
It can be understood that when an abnormality occurs in a certain device in the rail transit system, the central management device obtains the working data of the device and determines the first abnormality data, and the station management device obtains the working data of the device and determines the second abnormality data, so that the first abnormality data and the second abnormality data can be identical at this time. When the station management device is abnormal (for example, the connection between the station management device and the device B is abnormal, which results in untimely data update of the device B), the device B may refer to any one of the station devices, and the station management device sends the second abnormal data (for example, the current network signal of the device B is disconnected) to the central management device by acquiring the working data of the device B. The central management device determines that the working data of the device B is first abnormal data (e.g. the current network difference of the device B) by acquiring the working data of the device B, where the first abnormal data may be different from the second abnormal data. That is, the first and second abnormal data may be the same abnormal data for the same device or may be different abnormal data for the same device. Alternatively, the first and second anomaly data may also be anomaly data for different devices. For example, the first abnormal data is abnormal data indicating to the device B, and the second abnormal data is abnormal data for the device C.
Alternatively, the central management apparatus may determine the alarm cluster based on a similarity between the anomaly type of the first anomaly data and the anomaly type of the second anomaly data. Specifically, the central management apparatus may acquire an anomaly type of the first anomaly data and an anomaly type of the second anomaly data, calculate a similarity between the anomaly type of the first anomaly data and the anomaly type of the second anomaly data; if the similarity is larger than a similarity threshold, determining a first alarm cluster matched with the first abnormal data and the second abnormal data, and sending the first abnormal data or the second abnormal data to the first alarm cluster; if the similarity is smaller than or equal to the similarity threshold, determining a second alarm cluster matched with the first abnormal data, determining a third alarm cluster matched with the second abnormal data, sending the first abnormal data to the second alarm cluster, and sending the second abnormal data to the third alarm cluster.
The similarity between the anomaly type of the first anomaly data and the anomaly type of the second anomaly data can be calculated by acquiring the anomaly type of the first anomaly data and the anomaly type of the second anomaly data, for example, the similarity can be determined based on a similarity calculation method, such as extracting feature vectors of two anomaly types respectively, and calculating the similarity between the two anomaly types based on the feature vectors of the two anomaly types. If the similarity is greater than the similarity threshold, it indicates that the similarity between the two anomaly types is higher, and it may indicate that the first anomaly data and the second anomaly data are of the same anomaly type. If the similarity is less than or equal to the similarity threshold, it indicates that the similarity between the two anomaly types is low, and it may indicate that the first anomaly data and the second anomaly data are not of the same anomaly type. Optionally, the similarity between two types of abnormal data may also be calculated, and it is determined whether the two types of abnormal data are of the same abnormal type, so as to match the corresponding alarm clusters. By calculating the similarity between the two data, the accuracy of data judgment can be improved. Alternatively, the similarity calculation method may include, but is not limited to, a calculation method of euclidean distance, cosine distance, manhattan distance, hamming distance, pearson correlation coefficient, and the like.
Optionally, the central management device may further preset a correspondence between the anomaly type and the data, and when the first anomaly data and the second anomaly data are obtained, determine whether the first anomaly data and the second anomaly data are of a same anomaly type based on the correspondence, so as to match the alarm cluster. Because the alarm clusters can be matched by combining the abnormal types of the abnormal data, different alarm clusters can be matched for different types of the abnormal data, thereby realizing targeted abnormal processing and improving the abnormal processing efficiency. Aiming at the abnormal data of the same type, by determining one alarm cluster, the resource space can be saved, and the alarm efficiency can be improved.
Alternatively, the central management apparatus may alarm based on the alarm level of the abnormal data. Specifically, the central management apparatus may determine an alarm level of the first abnormal data and an alarm level of the second abnormal data, respectively; the method includes selecting a first alarm scheme for matching based on an alarm level of first abnormal data, and selecting a second alarm scheme for matching based on an abnormal level of second abnormal data, alarming based on the first alarm scheme, and alarming based on the second alarm scheme, so that alarms can be performed based on the respective alarm schemes. The alert scheme includes at least one of a telephone alert, a mail alert, and a short message alert. That is, the first alert scheme includes at least one of a telephone alert, a mail alert, and a short message alert, and the second alert scheme includes at least one of a telephone alert, a mail alert, and a short message alert.
The alarming based on the alarming scheme may refer to alarming based on at least one of telephone alarming, mail alarming and short message alarming, for example, the alarming based on mail may refer to sending abnormal data existing in the station equipment to a corresponding alarming cluster in a mail manner, so that the alarming cluster processes the abnormality existing in the station equipment, and normal operation of the equipment is ensured. If the first abnormal data and the second abnormal data are the same, the central management device can determine the alarm level of the first abnormal data, and select a matched alarm scheme based on the alarm level, so that an alarm is realized. If the first abnormal data and the second abnormal data are different, the central management equipment can determine the alarm level of the first abnormal data based on the first abnormal data and determine the alarm level of the second abnormal data based on the second abnormal data; selecting a first alarm scheme matched with the alarm level of the first abnormal data based on the alarm level of the first abnormal data; and selecting a second alarm scheme matched with the alarm grade of the second abnormal data based on the alarm grade of the second abnormal data, so that the alarm is respectively carried out on the first abnormal data and the second abnormal data, and the accuracy of data processing is improved. Alternatively, the central management apparatus may determine the alarm level of the abnormal data based on the abnormality type of the abnormal data.
Alternatively, if the first anomaly data and the second anomaly data are the same anomaly data for the same station apparatus, the alert level may be determined based on the first anomaly data or the second anomaly data, thereby processing the anomaly of the station apparatus. If the first and second anomaly data are different anomaly data for the same station equipment, an alert level may be determined based on the first and second anomaly data. That is, the alarm level is determined by combining the first abnormal data and the second abnormal data, for example, the alarm level of the abnormal data with higher emergency degree is used as the final alarm level to alarm. If the first abnormal data and the second abnormal data are abnormal data aiming at different station equipment, the alarm grade of the first abnormal data and the alarm grade of the second abnormal data can be respectively determined, and the respective alarm schemes are matched based on the respective alarm grades so as to alarm.
Optionally, the method for selecting the matched first alarm scheme by the central management device based on the alarm level of the first abnormal data may include: if the alarm level of the first abnormal data is the first level, determining that the first alarm scheme is telephone alarm, mail alarm and short message alarm; if the alarm level of the first abnormal data is the second level, determining that the first alarm scheme is at least two of telephone alarm, mail alarm and short message alarm; if the alarm level of the first abnormal data is the third level, determining that the first alarm scheme is mail alarm and short message alarm; and if the alarm level of the first abnormal data is the fourth level, determining that the first alarm scheme is mail alarm. The emergency degree corresponding to the first level is greater than the emergency degree corresponding to the second level, the emergency degree corresponding to the second level is greater than the emergency degree corresponding to the third level, and the emergency degree corresponding to the third level is greater than the emergency degree corresponding to the fourth level.
The emergency degree may refer to the influence degree on the rail transit system, and for example, the abnormal data may include display screen related equipment, train gate related equipment and station power supply related equipment on a train. The emergency degree corresponding to the station power supply related equipment is greater than the emergency degree corresponding to the train gate related equipment, and the emergency degree corresponding to the train gate related equipment is greater than the emergency degree corresponding to the display screen related equipment on the vehicle. It can also be understood that the influence degree of station power failure on the operation of the vehicle is greater than the influence degree of damage to the train gate (namely, the gate for preventing passengers from entering the track by mistake, the gate can enter the train door after being opened, and then enter the train carriage), and the influence degree of damage to the train door is greater than the influence degree of damage to the display screen (such as the advertisement display screen on the train) on the vehicle. Because the alarm grades corresponding to the abnormal data are different, for the abnormality with the emergency degree larger than the emergency threshold value, the alarm can be realized through the alarm scheme with higher alarm efficiency such as telephone alarm, etc., for the abnormality with the emergency degree smaller than the emergency threshold value, the alarm can be realized through the alarm scheme with lower alarm efficiency such as mail alarm or short message alarm, etc. than telephone alarm, and the reasonable utilization of resources can be realized.
It will be appreciated that the method of selecting the matched second alarm scenario by the central management apparatus based on the alarm level of the second abnormal data may refer to the method of selecting the matched first alarm scenario based on the alarm level of the first abnormal data, which will not be described herein.
Optionally, if the alert level is the second level, it may also be determined that the first alert scheme includes at least a telephone alert. That is, if the alert level is the second level, the first alert scheme may include a telephone alert and a mail alert, or the first alert scheme may include a telephone alert and a short message alert, or the first alert scheme may include a telephone alert, a short message alert, and a mail alert. Optionally, the first alarm scheme may further include other instant messaging programs, and the central processing system may further implement an alarm based on the other instant messaging programs.
Optionally, if the abnormal data includes a downtime of the device, determining an abnormal level corresponding to the abnormal data as a first level; if the abnormal data comprises 90% of the disk occupation, determining that the abnormal grade corresponding to the abnormal data is a second grade; if the abnormal data includes that the database access amount is greater than the preset threshold, it may be determined that the abnormal level corresponding to the abnormal data is a third level, and so on.
Optionally, the central processing system may further preset an alarm phone template, a short message template and a mail template, where the alarm template may include an alarm cluster, alarm content, an exception handling time limit, historical alarm data of the device, and a historical handling alarm cluster, and so on. When the abnormal data is acquired, the alarm template can be rapidly determined based on the alarm level of the abnormal data, so that the abnormal processing efficiency is improved.
Optionally, if no abnormal data exists in the first working data, the central management device may continuously acquire the first working data of each station device in the rail transit system, and when the abnormal data exists in the first working data, alarm is given. Optionally, if no abnormal data exists in the second working data, the station management device may continuously acquire the second working data of each station device in the rail transit system, and when the abnormal data exists in the second working data, send the abnormal data to the central management device to realize an alarm. By monitoring working data of each station device in the rail transit system in real time, abnormal data can be determined in time and abnormal processing can be carried out. Optionally, the central management device may send the alarm information to the alarm cluster through the public network, so that the alarm cluster may receive the alarm information, where the alarm information may include abnormal data, station equipment corresponding to the abnormal data, an identifier of a station where the abnormal data is located, and so on.
Referring to fig. 5, fig. 5 is a schematic flow chart of a method for data alarm according to an embodiment of the present application; as shown in fig. 5, the data alerting method may be applied to a central management device, and the data alerting method includes, but is not limited to, the following steps:
s201, collecting working data of each station device in the rail transit system.
The central management equipment can pull the working data of the station equipment from each station equipment in the rail transit system in an active probing mode.
S202, the abnormal data judging rule is matched based on the data type of the working data.
The data types of the working data comprise a state type, a time-consuming type, a resource type, a comprehensive type and the like, and different data types correspond to different abnormal data judging rules.
S203, if the data type of the working data is a state type, judging whether the state is normal.
For example, it may be determined whether the working data is in a preset state, and if the state in the working data is in the preset state, it is determined that the state is normal. If the state corresponding to the working data is normal, the working data is determined to be normal, and the working data of the station equipment is continuously acquired. If not, that is, if the state corresponding to the working data is abnormal, it is determined that the working data is abnormal, the working data is determined as abnormal data, and step S207 is performed.
S204, if the data type of the working data is time-consuming, judging whether the time consumption exceeds a specified duration range.
If yes, that is, if the time consumption corresponding to the working data exceeds the predetermined time period range, it is determined that the working data is abnormal, and the working data is determined as abnormal data and step S207 is performed. If not, namely the time consumption corresponding to the working data does not exceed the range of the specified time length, determining that the working data is normal, and continuously acquiring the working data of the station equipment.
S205, if the data type of the working data is the resource type, judging whether the resource use condition exceeds the specified resource range.
If yes, that is, if the resource usage corresponding to the working data exceeds the predetermined resource range, it is determined that the working data is abnormal, and the working data is determined as abnormal data and step S207 is executed. If not, namely the resource use condition corresponding to the working data does not exceed the specified resource range, determining that the working data is normal, and continuously acquiring the working data of the station equipment.
S206, if the data type of the working data is comprehensive, comprehensively calculating whether the working data belongs to abnormal indexes according to rules.
If yes, that is, if the work data belongs to the abnormality index according to the rule comprehensive calculation, the work data is determined as the abnormality data and step S207 is executed. If not, the working data is determined to be normal according to the rule comprehensive calculation, and the working data of the station equipment is continuously obtained.
S207, determining an alarm cluster based on the abnormal data.
The number of the alarm clusters can be multiple, and the abnormal data processed by each alarm cluster can be different, so that the abnormal data can be sent to the matched alarm clusters, and targeted abnormal processing is realized.
S208, determining the alarm level of the abnormal data, determining an alarm scheme corresponding to the alarm level, and transmitting the abnormal data to the alarm cluster based on the alarm scheme.
The alarm levels of the abnormal data include a first level alarm (i.e., a first level), a second level alarm (i.e., a second level), a third level alarm (i.e., a third level), a fourth level alarm (i.e., a fourth level), and so on. The emergency degree of the first-level alarm is larger than that of the second-level alarm, the emergency degree of the second-level alarm is larger than that of the third-level alarm, and the emergency degree of the third-level alarm is larger than that of the fourth-level alarm. The alert scheme may include at least one of a telephone alert, a short message alert, and a mail alert. Different alarm schemes are determined based on different alarm levels, so that reasonable use of resources can be realized. The specific implementation manner of step S201 to step S208 in the embodiment of the present application may refer to step S101 to step S103 in the embodiment corresponding to fig. 3, which is not described herein again.
In the technical scheme of the application, the central management equipment and the station management equipment are two independent systems, the central management equipment can refer to a network level basic platform and an application platform such as comprehensive management and the like relative to a station level platform, and the station management equipment can refer to the station level platform, so that the station level can carry out management alarm on a rail transit system and also carry out management alarm on the network level. By using two links to monitor each station device in the rail transit system, the problem that the station management device is down and alarm information cannot be sent can be solved, and the problem that the alarm center in the station cannot send the alarm information due to the down of the station management device can be solved. Further, the method and the system can solve the problem that after the network between the station management equipment and the central management equipment is interrupted, the warning cannot be realized due to the fact that the rail transit system of the station is abnormal, at the moment, the central management equipment starts the activity detection mechanism, abnormal data in the rail transit system can be rapidly determined, warning is realized, and the processing efficiency of the data is improved.
In the embodiment of the application, the first working data is pulled from each station device in the rail transit system in an active detection mode to determine the first abnormal data, the second abnormal data sent by the station management device is obtained, and the warning is carried out based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent.
Optionally, referring to fig. 6, fig. 6 is a flow chart of a data display method according to an embodiment of the application. The data presentation method can be applied to a central management device; as shown in fig. 6, the data presentation method includes, but is not limited to, the following steps:
s301, first working data of each station device in the rail transit system are obtained.
In the embodiment of the application, the central management equipment can acquire the first working data of each station equipment in the rail transit system. The specific manner of acquiring the first working data may refer to the manner of acquiring the first working data in step S101, which is not described herein.
S302, acquiring data display frequency, display style and data display conditions.
In the embodiment of the application, the central management equipment can be stored in the associated database under the condition of pulling the first working data, and when the data is required to be displayed, the data required to be displayed can be pulled from the database. The data presentation frequency may refer to a frequency of pulling presentation data from a database, or the data presentation frequency may refer to a data presentation period, or the like. The presentation style may include, but is not limited to, a line graph, a bar graph, a pie graph, a table, a parlay, and the like. The data presentation conditions may be used to screen the data to be presented. For example, the data display condition may include any one of an identification of a station (such as a station name, a station number, etc.), an identification of a device in a rail transit system (such as a device name, a device number, etc.), working data of a plurality of station devices at a certain period, working data of a certain class of devices, working data of an abnormal device, or may include at least two of them, and for example, the data display condition may include working data of a plurality of station devices at a period D with an identification of a station being station C.
Optionally, the central management device may detect different selection instructions, implement targeted data presentation based on the selection instructions, for example, implement targeted data presentation using any one of the following methods, or implement targeted data presentation in combination with the following methods:
in the first mode, when a first selection instruction aiming at the identification of the target station is detected, screening first working data based on the first selection instruction, and displaying the working data of equipment corresponding to the target station according to the data display frequency and the display style. The central management equipment is used for managing rail transit systems corresponding to a plurality of stations, so that the monitored data size is huge, working data to be displayed are screened and displayed through the identification of the target station, the data size can be reduced, and page display modes are enriched.
In a second mode, when a second selection instruction aiming at the target type equipment is detected, screening the first working data based on the second selection instruction, and displaying the working data corresponding to the target type equipment according to the data display frequency and the display style. The target type equipment can be any station equipment in the rail transit system, and the targeted data display and the rich data display modes can be realized by displaying the working data of the target type equipment.
In a third mode, when a third selection instruction aiming at the target time range is detected, screening the first working data based on the third selection instruction, and displaying the working data of the target time range according to the data display frequency and the display style. The target time range can be any time range, for example, the target time range can refer to working data of all station equipment within two hours, and the working data of the station equipment is screened by using time, so that the data volume of page display can be reduced, and targeted data display is realized.
Optionally, aggregation operation may be performed on each type of index, where the operation data is displayed, for example, the data type of the operation data is state, and the central management device pulls the operation data of the station device once every 10 seconds, so that the aggregation operation may also refer to operations such as summing up and averaging the time-consuming data by aggregating the operation data of the station device under multiple periods (for example, displaying once every 40 seconds), and displaying the aggregated operation data, which may simplify a data display page, so that page display is clearer.
S303, screening the first working data based on the data display conditions, and displaying the screened data according to the data display frequency and the display style.
In the embodiment of the application, since the data display condition is used for screening the first working data to be displayed, the screened data can be displayed according to the data display frequency and the display style. As shown in fig. 7, fig. 7 is a schematic diagram of a display interface provided by the embodiment of the present application, where 7a in fig. 7 may refer to screening and displaying first working data based on a station identifier, where the station name is station a, station a includes 5 station devices, connectivity may refer to a state of each station device, and time consumption may refer to a time period consumed for connecting each station device. 7b in fig. 7 may refer to screening and presenting the first working data based on a time period, where the time period may be the previous hour of the current time period. By displaying the first working data, the working state of the station equipment can be visually checked.
Optionally, for the station management device, the station management device may display second working data of the rail traffic device, and the station management device may screen the second working data to be displayed, and display the screened second working data in the display interface. As shown in fig. 8, fig. 8 is a schematic diagram of another presentation interface provided by an embodiment of the present application, where the area 1 in fig. 8 may include a resource overview of the host 1 and the associated item (i.e. the associated host of the host 1), and the area 1 includes an IP address, a host name, a runtime, a memory, a CPU core number, a CPU usage, a memory usage, a partition usage, a download bandwidth, an upload bandwidth, and so on of the host 1 and the associated host. Region 2 may include the run time of host 1, the number of CPU cores, total memory, total CPU usage, maximum partition usage, and so forth. The area 3 may refer to displaying the memory information of the host 1 by means of a line graph. The area 4 may refer to space data available to each partition in the host 1, including a host name, a file system, total space, available space, usage (i.e., partition usage), and so on. The area 5 may be used to reflect network broadband usage per second by the host 1. Zone 6 may refer to the usage of traffic per hour by host 1. It can be understood that more host information can be displayed in the display interface and various information of the host can be displayed by using more display styles, which is not limited in the embodiment of the present application. By respectively displaying various data of each station device in the rail transit system, the state change condition of the host can be visually checked, and further the station device can be better monitored.
Referring to fig. 9, fig. 9 is a schematic diagram of an overall architecture of a system according to an embodiment of the present application, where the number of stations may include N, where N is a positive integer greater than or equal to 1. Taking the station 1 as an example for illustration, the station 1 may include station management equipment 1 and M pieces of station equipment (i.e. station equipment 1 to M pieces of station equipment in a rail transit system), where M is a positive integer greater than or equal to 1, and the station management equipment 1 and the M pieces of station equipment report second working data to the station management equipment 1 in real time, and if the second working data are all normal, reporting the second working data is continuously performed. If the second working data is abnormal, generating alarm information, wherein the alarm information can comprise abnormal data, the identification of abnormal equipment and the like. Further, the alarm channel is selected based on the alarm level and the alarm level, and the alarm level may refer to an alarm scheme, and the alarm channel may include at least one of a telephone alarm, a short message alarm, and a mail alarm, and the alarm template is determined based on the alarm channel and sent to the central management device, and the central management device may send the alarm information to the corresponding alarm cluster (such as a processing team) according to the alarm template through the alarm channel. The station 1 may further include a plurality of station management devices, where each station management device may have the same function, or the plurality of station management devices may jointly implement processing of the first working data of the M devices in the station 1. It will be appreciated that for other N-1 stations of the N stations, an alert may be made in this manner. Further, the central management device can receive the alarm information sent by the station management device of each station to carry out alarm, and can monitor the first working data of the M station devices included in each station in the N stations in an active probing mode, if abnormal data exist in the first working data, the alarm information is generated, the alarm information is matched with the alarm grade based on the alarm information, an alarm channel is selected based on the alarm grade, an alarm template is determined based on the alarm channel, and then the alarm information can be sent to a corresponding alarm cluster (such as a processing team) according to the alarm template based on the alarm channel, so that alarm is realized.
Optionally, the central management device may further determine, based on the first anomaly data and the second anomaly data, at least one target anomaly device having an anomaly frequency greater than the target frequency, determine a display frequency of the at least one target anomaly device as the first frequency, and display the working data of the at least one target anomaly device according to the first frequency. Because the frequency of the occurrence of the abnormality of the target abnormal equipment is higher, the display frequency corresponding to the target abnormal equipment is set to be higher, the targeted equipment monitoring can be realized, and the accuracy of the data monitoring is improved. For other station equipment, for example, other station equipment with abnormal frequency smaller than the target frequency, the display frequency corresponding to the station equipment can be set to be lower because the abnormal frequency is lower, so that resources can be saved.
Optionally, after the alarm, the central management device may further acquire a target processing duration required for the exception processing, determine a target solution based on the target processing duration, and send the target solution to the station management device corresponding to the exception data, so that the station management device processes the exception data based on the target solution. If the target processing time length is smaller than the time length threshold value, the target scheme generates waiting prompt information for prompting the user to wait, and the waiting prompt information is sent to station management equipment; if the target processing time length is greater than or equal to the time length threshold value, the target scheme generates adjustment prompt information for prompting the user to adjust the travel mode.
The target processing duration may refer to a duration that is consumed to process the device exception, and the target processing duration may be determined based on a historical exception processing record, or may be reported by an alarm cluster. The target scheme may be used to process the exception data. For example, if the abnormal data includes that the door opening and closing control system of the train 1 is abnormal, and the door of the train 1 cannot be closed, the target scheme may refer to suspending operation of the train 1, temporarily replacing the train 1 with the train 2, and the train 2 may refer to a standby train. Alternatively, the target scheme may also be an adjustment scheme of the pointer to the travel route. For example, if all the rail transit systems in the station a are powered off, the target solution may refer to other travel routes that can bypass the station a, so as to prompt the user to adjust the travel mode. Through using the target scheme, can promote unusual processing efficiency, through the suggestion user, can make the user adjust the trip mode, promote trip efficiency, increase user experience.
In the embodiment of the application, the first working data is pulled from each station device in the rail transit system in an active probing mode to determine the first abnormal data, the second abnormal data sent by the station management device is obtained, and the warning is carried out based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent. On the other hand, first working data of each station device in the rail transit system are acquired; acquiring data display frequency, display style and data display conditions; and screening the first working data based on the data display conditions, and displaying the screened data according to the data display frequency and the display style, so that targeted data display can be realized. Further, as the obtained working data of each device in the rail transit system can be screened and displayed, for example, the targeted data can be displayed based on different selection instructions. If the working data of the equipment is screened according to the selection instruction of a specific station, the selection instruction of a specific type of equipment or the selection instruction of a specific time range, the data display mode can be enriched, the quick display is realized, and the working condition of any equipment can be quickly checked. And moreover, by using different display modes to display data, the data display modes can be further enriched, confusion caused by using the same data display mode to display is avoided, and the data readability is improved.
The method of the embodiment of the application is described above, and the device of the embodiment of the application is described below.
Referring to fig. 10, fig. 10 is a schematic diagram of a composition structure of a metro cloud platform cloud edge synchronization device provided by an embodiment of the present application, where the metro cloud platform cloud edge synchronization device may be a computer program (including program code) running in a terminal device; the subway cloud platform cloud edge synchronization device can be used for executing corresponding steps in the subway cloud platform cloud edge synchronization method provided by the embodiment of the application. Optionally, the cloud edge synchronization device of the subway cloud platform may be further disposed in a central management device, where the central management device establishes communication connection with a rail transit system, the rail transit system includes at least one station management device and a plurality of station devices corresponding to each station management device, the central management device is configured to monitor the station management device and the plurality of station devices corresponding to the station management device, and the station management device is configured to monitor the plurality of station devices corresponding to the station management device. For example, the metro cloud platform cloud edge synchronization device 100 includes:
a first obtaining unit 1001, configured to pull first working data from each station device in the rail transit system in an active probing manner, determine first abnormal data of the rail transit system based on the first working data of each station device, where the first working data is used to indicate whether each station device in the rail transit system is in a working state;
A second obtaining unit 1002, configured to obtain second abnormal data sent by a station management device, where the second abnormal data is determined based on second working data of each station device in the rail transit system, and the second working data is used to indicate working index data of each station device in the rail transit system;
a data alerting unit 1003 for alerting based on the first abnormal data and the second abnormal data.
Optionally, the first obtaining unit 1001 is specifically configured to:
if the second abnormal data sent by the station management equipment is not obtained in the target time period, the first working data are pulled from each station equipment in the rail transit system in an active probing mode; or,
if the indication information which is sent by the station management equipment and used for indicating the normal operation of the rail transit system is not obtained in the target time period, first working data are pulled from each station equipment in the rail transit system in an active probing mode;
optionally, the data alarm unit 1003 is specifically configured to:
when second abnormal data sent by the station management equipment is obtained, sending a connection request to abnormal equipment corresponding to the second abnormal data in the rail transit system, wherein the connection request is used for establishing connection with the abnormal equipment;
If the connection with the abnormal equipment fails, alarming based on the second abnormal data;
if the connection with the abnormal equipment is successful, the abnormal data of the abnormal equipment is acquired, and an alarm is given based on the abnormal data of the abnormal equipment.
Optionally, the data alarm unit 1003 is specifically configured to:
acquiring the second working data sent by the station management equipment;
if the equipment corresponding to the first working data and the equipment corresponding to the second working data are the same equipment, the first working data indicating equipment is normal, the second working data indicating equipment is abnormal, the second working data are determined to be second abnormal data, and warning is carried out based on the second abnormal data.
Optionally, the first abnormal data judging rule includes at least one of a first judging rule, a second judging rule, a third judging rule and a fourth judging rule; the first obtaining unit 1001 is specifically configured to:
if the first working data indicate that each station device in the rail transit system is normal, working index data of each station device are obtained, wherein the working index data comprise at least one of server resource data, device service data, process operation data, network operation data, database operation data and message queue operation data of each station device in the rail transit system;
Acquiring data types of the working index data of each station device, wherein the data types comprise at least one of a state index, a time-consuming index, a resource index and a comprehensive index;
if the data type is a state type index, a first judging rule is matched based on the state type index, whether the working index data is abnormal or not is determined based on the first judging rule, and the first judging rule is used for judging whether the working index data is in a preset state or not;
if the data type is a time-consuming index, a second judging rule is matched based on the time-consuming index, whether the working index data is abnormal or not is determined based on the second judging rule, and the second judging rule is used for judging whether the working index data exceeds a specified duration range or not;
if the data type is a resource type index, a third judging rule is matched based on the resource type index, whether the working index data is abnormal or not is determined based on the third judging rule, and the third judging rule is used for judging whether the resource use condition in the working index data exceeds a specified resource range or not;
if the data type is a comprehensive index, a fourth judgment rule is matched based on the comprehensive index, whether the working index data is abnormal or not is determined based on the fourth judgment rule, and the fourth judgment rule is used for comprehensively calculating whether the working index data belongs to an abnormal index according to the rule;
Determining work index data with abnormality as the first abnormality data;
and if the first working data indicate that any equipment in the rail transit system is abnormal, determining the first working data as the first abnormal data.
Optionally, the data alarm unit 1003 is specifically configured to:
acquiring an anomaly type of the first anomaly data and an anomaly type of the second anomaly data;
calculating the similarity between the anomaly type of the first anomaly data and the anomaly type of the second anomaly data;
if the similarity is larger than a similarity threshold, determining a first alarm cluster matched with the first abnormal data and the second abnormal data, and sending the first abnormal data or the second abnormal data to the first alarm cluster;
if the similarity is smaller than or equal to the similarity threshold, determining a second alarm cluster matched with the first abnormal data, determining a third alarm cluster matched with the second abnormal data, sending the first abnormal data to the second alarm cluster, and sending the second abnormal data to the third alarm cluster.
Optionally, the data alarm unit 1003 is specifically configured to:
respectively determining the alarm level of the first abnormal data and the alarm level of the second abnormal data;
The method comprises the steps of selecting a matched first alarm scheme based on the alarm level of the first abnormal data, selecting a matched second alarm scheme based on the alarm level of the second abnormal data, alarming based on the first alarm scheme, and alarming based on the second alarm scheme, wherein the alarm scheme comprises at least one of telephone alarm, mail alarm and short message alarm.
Optionally, the data alarm unit 1003 is specifically configured to:
if the alarm level of the first abnormal data is the first level, determining that the first alarm scheme is telephone alarm, mail alarm and short message alarm;
if the alarm level of the first abnormal data is the second level, determining that the first alarm scheme is at least two of telephone alarm, mail alarm and short message alarm;
if the alarm level of the first abnormal data is the third level, determining that the first alarm scheme is mail alarm and short message alarm;
if the alarm level of the first abnormal data is the fourth level, determining that the first alarm scheme is mail alarm, wherein the emergency degree corresponding to the first level is greater than the emergency degree corresponding to the second level, the emergency degree corresponding to the second level is greater than the emergency degree corresponding to the third level, and the emergency degree corresponding to the third level is greater than the emergency degree corresponding to the fourth level.
Optionally, the metro cloud platform cloud edge synchronization device 100 further includes a data display unit 1004, configured to:
acquiring data display frequency, display style and data display conditions, wherein the data display conditions are used for screening data to be displayed;
and screening the first working data based on the data display condition, and displaying the screened data according to the data display frequency and the display style.
Optionally, the data display unit 1004 is specifically configured to:
when a first selection instruction aiming at the identification of the target station is detected, screening the first working data based on the first selection instruction, and displaying the working data of equipment corresponding to the target station according to the data display frequency and the display pattern;
when a second selection instruction aiming at the target type equipment is detected, screening the first working data based on the second selection instruction, and displaying the working data corresponding to the target type equipment according to the data display frequency and the display style;
when a third selection instruction aiming at a target time range is detected, screening the first working data based on the third selection instruction, and displaying the working data of the target time range according to the data display frequency and the display pattern.
Optionally, the first obtaining unit 1001 is specifically configured to:
acquiring a detection activity factor aiming at the rail transit system, and determining detection activity frequency aiming at each station device based on the detection activity factor, wherein the detection activity factor comprises at least one of departure frequency, people flow, environmental weather data, use duration of each station device and importance level of each station device;
and pulling the first working data from each station equipment of the rail transit system in an active detection mode according to the detection frequency.
It should be noted that, in the embodiment corresponding to fig. 10, the content not mentioned may refer to the description of the method embodiment, and will not be repeated here.
In the embodiment of the application, the first working data is pulled from each station device in the rail transit system in an active detection mode to determine the first abnormal data, the second abnormal data sent by the station management device is obtained, and the warning is carried out based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent.
Referring to fig. 11, fig. 11 is a schematic diagram of a composition structure of a computer device according to an embodiment of the present application. As shown in fig. 11, the computer device 110 may include: a processor 1101, a memory 1102, and a network interface 1103. The processor 1101 is connected to the memory 1102 and the network interface 1103, for example, the processor 1101 may be connected to the memory 1102 and the network interface 1103 by a bus. The computer device may be a terminal device or a server.
The processor 1101 is configured to support the subway cloud platform cloud edge synchronization device to perform the corresponding functions in the subway cloud platform cloud edge synchronization method described above. The processor 1101 may be a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a hardware chip or any combination thereof. The hardware chip may be an Application-specific integrated circuit (ASIC), a programmable logic device (Programmable Logic Device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (Complex Programmable Logic Device, CPLD), a Field programmable gate array (Field-Programmable Gate Array, FPGA), general array logic (Generic Array Logic, GAL), or any combination thereof.
The memory 1102 is used for storing program codes and the like. Memory 1102 may include Volatile Memory (VM), such as random access Memory (Random Access Memory, RAM); the Memory 1102 may also include a Non-Volatile Memory (NVM), such as Read-Only Memory (ROM), flash Memory (flash Memory), hard Disk (HDD) or Solid State Drive (SSD); memory 1102 may also include a combination of the above types of memory.
The network interface 1103 is used to provide network communication functions.
The processor 1101 may call the program code to:
pulling first working data from each station device in the rail transit system in an active detection mode, and determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data is used for indicating whether each station device in the rail transit system is in a working state or not;
acquiring second abnormal data sent by station management equipment, wherein the second abnormal data is determined based on second working data of each station equipment in the rail transit system, and the second working data is used for indicating working index data of each station equipment in the rail transit system;
And alarming based on the first abnormal data and the second abnormal data.
In the embodiment of the application, the first working data is pulled from each station device in the rail transit system in an active detection mode to determine the first abnormal data, the second abnormal data sent by the station management device is obtained, and the warning is carried out based on the first abnormal data and the second abnormal data. The working states of all station equipment in the rail transit system can be monitored from two links, namely, one link is used for actively monitoring the working data of all station equipment to determine whether abnormal data exist, and the other link is used for acquiring the abnormal data sent by the station management equipment, namely, the station management equipment monitors the working states of all station equipment in the rail transit system, and the two links are not interfered with each other, so that the working states of the other link cannot be influenced when one link is abnormal. The method can avoid the situation that the warning cannot be realized due to the abnormality of station management equipment, improves the efficiency of data warning in a rail transit system, and improves the accuracy of the data warning to a certain extent.
It should be understood that the computer device 110 described in the embodiment of the present application may perform the description of the method in the embodiment corresponding to fig. 3, 5 and 6, and may also perform the description of the cloud edge synchronization device of the metro cloud platform in the embodiment corresponding to fig. 10, which are not described herein. In addition, the description of the beneficial effects of the same method is omitted.
The embodiments of the present application also provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a computer, cause the computer to perform a method as in the previous embodiments, the computer being part of a computer device as mentioned above. Such as the processor 1101 described above. As an example, the program instructions may be executed on one computer device or on multiple computer devices located at one site, or alternatively, on multiple computer devices distributed across multiple sites and interconnected by a communication network, which may constitute a blockchain network.
Embodiments of the present application also provide a computer program product or computer program comprising computer instructions which, when executed by a processor, implement some or all of the steps of the above-described method. Optionally, the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium and executed by the processor, such that the computer device performs the steps performed in the embodiments of the methods described above.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, may include processes of the embodiments of the methods as described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.
Claims (13)
1. The cloud edge synchronization method for the subway cloud platform is characterized by being applied to central management equipment, wherein the central management equipment is in communication connection with a rail transit system, the rail transit system comprises at least one station management equipment and a plurality of station equipment corresponding to each station management equipment, the central management equipment is used for monitoring the station management equipment and the plurality of station equipment corresponding to the station management equipment, and the station management equipment is used for monitoring the plurality of station equipment corresponding to the station management equipment; the cloud edge synchronization method of the subway cloud platform comprises the following steps:
Pulling first working data from each station device in the rail transit system in an active detection mode, and determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data is used for indicating whether each station device in the rail transit system is in a working state or not;
acquiring second abnormal data sent by the station management equipment, wherein the second abnormal data is determined based on second working data reported by each station equipment in the rail transit system, and the second working data is used for indicating working index data of each station equipment in the rail transit system;
and alarming based on the first abnormal data and the second abnormal data.
2. The method according to claim 1, wherein the pulling the first working data from each station device in the rail transit system by active probing comprises:
if the second abnormal data sent by the station management equipment is not obtained in the target time period, the first working data are pulled from each station equipment in the rail transit system in an active probing mode; or,
If the indication information which is sent by the station management equipment and used for indicating the normal operation of the rail transit system is not obtained in the target time period, the first working data are pulled from each station equipment in the rail transit system in an active probing mode.
3. The method according to claim 1, wherein the method further comprises:
when second abnormal data sent by the station management equipment is obtained, sending a connection request to abnormal equipment corresponding to the second abnormal data in the rail transit system, wherein the connection request is used for establishing connection with the abnormal equipment;
if the connection with the abnormal equipment fails, alarming based on the second abnormal data;
and if the connection with the abnormal equipment is successful, acquiring the abnormal data of the abnormal equipment, and alarming based on the abnormal data of the abnormal equipment.
4. The method according to claim 1, wherein the method further comprises:
acquiring the second working data sent by the station management equipment;
the alarming based on the first abnormal data and the second abnormal data comprises the following steps:
If the equipment corresponding to the first working data and the equipment corresponding to the second working data are the same equipment, the first working data indicating equipment is normal, the second working data indicating equipment is abnormal, the second working data are determined to be second abnormal data, and warning is carried out based on the second abnormal data.
5. The method of claim 1, wherein the first abnormal data judgment rule comprises at least one of a first judgment rule, a second judgment rule, a third judgment rule, and a fourth judgment rule;
the determining the first abnormal data of the rail transit system based on the first working data of the station equipment comprises the following steps:
if the first working data indicate that each station device in the rail transit system is normal, working index data of each station device are obtained, wherein the working index data comprise at least one of server resource data, device service data, process operation data, network operation data, database operation data and message queue operation data of each station device in the rail transit system;
acquiring data types of the working index data of each station device, wherein the data types comprise at least one of a state index, a time-consuming index, a resource index and a comprehensive index;
If the data type is a state type index, a first judging rule is matched based on the state type index, whether the working index data is abnormal or not is determined based on the first judging rule, and the first judging rule is used for judging whether the working index data is in a preset state or not;
if the data type is a time-consuming index, a second judging rule is matched based on the time-consuming index, whether the working index data is abnormal or not is determined based on the second judging rule, and the second judging rule is used for judging whether the working index data exceeds a specified duration range or not;
if the data type is a resource type index, a third judging rule is matched based on the resource type index, whether the working index data is abnormal or not is determined based on the third judging rule, and the third judging rule is used for judging whether the resource use condition in the working index data exceeds a specified resource range or not;
if the data type is a comprehensive index, a fourth judgment rule is matched based on the comprehensive index, whether the working index data is abnormal or not is determined based on the fourth judgment rule, and the fourth judgment rule is used for comprehensively calculating whether the working index data belongs to an abnormal index according to the rule;
Determining work index data with abnormality as the first abnormality data;
and if the first working data indicate that any equipment in the rail transit system is abnormal, determining the first working data as the first abnormal data.
6. The method of claim 1, wherein the alerting based on the first anomaly data and the second anomaly data comprises:
acquiring an anomaly type of the first anomaly data and an anomaly type of the second anomaly data;
calculating the similarity between the anomaly type of the first anomaly data and the anomaly type of the second anomaly data;
if the similarity is larger than a similarity threshold, determining a first alarm cluster matched with the first abnormal data and the second abnormal data, and sending the first abnormal data or the second abnormal data to the first alarm cluster;
if the similarity is smaller than or equal to the similarity threshold, determining a second alarm cluster matched with the first abnormal data, determining a third alarm cluster matched with the second abnormal data, sending the first abnormal data to the second alarm cluster, and sending the second abnormal data to the third alarm cluster.
7. The method of claim 1, wherein the alerting based on the first anomaly data and the second anomaly data comprises:
respectively determining the alarm level of the first abnormal data and the alarm level of the second abnormal data;
selecting a matched first alarm scheme based on the alarm level of the first abnormal data, selecting a matched second alarm scheme based on the alarm level of the second abnormal data, alarming based on the first alarm scheme, and alarming based on the second alarm scheme, wherein the alarm scheme comprises at least one of telephone alarm, mail alarm and short message alarm.
8. The method of claim 7, wherein the selecting a matching first alert scheme based on the alert level of the first anomaly data comprises:
if the alarm level of the first abnormal data is a first level, determining that the first alarm scheme is a telephone alarm, a mail alarm and a short message alarm;
if the alarm level of the first abnormal data is the second level, determining that the first alarm scheme is at least two of telephone alarm, mail alarm and short message alarm;
If the alarm level of the first abnormal data is a third level, determining that the first alarm scheme is mail alarm and short message alarm;
if the alarm level of the first abnormal data is a fourth level, determining that the first alarm scheme is mail alarm, wherein the emergency degree corresponding to the first level is greater than that corresponding to the second level, the emergency degree corresponding to the second level is greater than that corresponding to the third level, and the emergency degree corresponding to the third level is greater than that corresponding to the fourth level.
9. The method according to any one of claims 1-8, wherein the pulling the first working data from each station device in the rail transit system by means of active probing comprises:
acquiring a detection activity factor aiming at the rail transit system, and determining detection activity frequency aiming at each station device based on the detection activity factor, wherein the detection activity factor comprises at least one of departure frequency, people flow, environmental weather data, use duration of each station device and importance level of each station device;
and pulling the first working data from each station equipment of the rail transit system in an active detection mode according to the detection frequency.
10. The utility model provides a subway cloud platform cloud limit synchronizer, its characterized in that, subway cloud platform cloud limit synchronizer deploys in central management equipment, central management equipment establishes communication connection with the track traffic system, include at least one station management equipment and a plurality of station equipment that every station management equipment corresponds in the track traffic system, central management equipment is used for to station management equipment with a plurality of station equipment that station management equipment corresponds monitors, station management equipment is used for to a plurality of station equipment that station management equipment corresponds monitors, subway cloud platform cloud limit synchronizer includes:
the first acquisition unit is used for pulling first working data from each station device in the rail transit system in an active detection mode, and determining first abnormal data of the rail transit system based on the first working data of each station device, wherein the first working data is used for indicating whether each station device in the rail transit system is in a working state or not;
the second acquisition unit is used for acquiring second abnormal data sent by station management equipment, the second abnormal data are determined based on second working data reported by each station equipment in the rail transit system, and the second working data are used for indicating working index data of each station equipment in the rail transit system;
And the data alarming unit is used for alarming based on the first abnormal data and the second abnormal data.
11. A computer device, comprising: a processor, a memory, and a network interface;
the processor is connected to the memory, the network interface for providing data communication functions, the memory for storing program code, the processor for invoking the program code to cause the computer device to perform the method of any of claims 1-9.
12. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program adapted to be loaded and executed by a processor to cause a computer device having the processor to perform the method of any of claims 1-9.
13. A computer program product comprising computer instructions which, when executed by a processor, implement the method of any of claims 1-9.
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