CN115834343A - Fault equipment prediction method, device and storage medium - Google Patents
Fault equipment prediction method, device and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device and a storage medium for predicting fault equipment, relates to the technical field of communication, and can find equipment faults in time and ensure stable operation of 5G core network services. The method comprises the following steps: determining a spatial association degree and a temporal association degree of each of the first device and at least one second device, wherein the spatial association degree is used for representing a spatial position relationship between the first device and the second device, and the temporal association degree is used for representing a time interval of historical reported faults of the first device and the second device; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment; determining the fault association degree between the first equipment and each second equipment according to the space association degree and the time association degree; and determining the second equipment with the highest fault association degree with the first equipment as the target equipment with the fault. The embodiment of the application is applied to the equipment with the predicted fault.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and an apparatus for predicting a faulty device, and a storage medium.
Background
With the rapid development of information economy represented by the internet, cloud computing and big data, the number of 5G users is continuously increased, the scale of a cloud resource system is larger and larger, and the types and the number of devices carried by the cloud resource system are larger and larger. When one device in the cloud resource system fails, it may cause other devices to also fail. And as the types and the number of the devices carried by the cloud resource system are more and more, the fault alarm device can receive a large amount of fault information reported by different types of devices.
Therefore, how to find the device causing the reported fault from a large amount of reported fault information and ensure the stable operation of the 5G core network service is still a problem to be solved.
Disclosure of Invention
The application provides a method, a device and a storage medium for predicting fault equipment, which are used for discovering equipment faults in time and ensuring stable operation of 5G core network services.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for predicting a faulty device, including: the method comprises the steps that a fault equipment prediction device determines a spatial association degree and a time association degree of a first equipment and each second equipment in at least one second equipment, wherein the spatial association degree is used for representing a spatial position relation between the first equipment and the second equipment, and the time association degree is used for representing a time interval of historical reported faults of the first equipment and the second equipment; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment; the fault equipment prediction device determines the fault association degree between the first equipment and each second equipment according to the space association degree and the time association degree; and the failure equipment prediction device determines the second equipment with the highest third relevance degree with the first equipment as the failure target equipment.
With reference to the first aspect, in a possible implementation manner, the method further includes: the failure equipment prediction device processes the failure of the target equipment; the fault equipment prediction device determines whether the fault reported by the first equipment is successfully processed within a preset time period; if the processing is successful, the fault equipment prediction device increases the numerical values of the spatial correlation degree and the time correlation degree between the target equipment and the first equipment; if the processing is not successful, the failure device prediction apparatus reduces the spatial association degree between the target device and the first device, and the numerical value of the spatial association degree.
With reference to the foregoing first aspect, in a possible implementation manner, the method further includes: the method comprises the steps that a failure equipment prediction device obtains physical attribute information, network attribute information and link information of each piece of equipment in a plurality of pieces of equipment; the plurality of devices includes a first device and at least one second device; the failure equipment prediction device determines a space topological graph among a plurality of equipment according to the physical attribute information, the network attribute information and the link information of each equipment; the spatial topological graph is used for representing the spatial position relation among the devices in the plurality of devices; and the failure equipment prediction device determines the spatial association degree of the first equipment and each second equipment in the plurality of second equipment according to the spatial topological graph.
With reference to the first aspect, in a possible implementation manner, the method further includes: the method comprises the steps that a fault equipment prediction device obtains time information of historical reported faults of each piece of equipment in a plurality of pieces of equipment, and an index space-time diagram among the plurality of pieces of equipment is determined; the index space-time diagram is used for representing the time interval of historical reporting faults among the devices in the plurality of devices; the failure equipment prediction device determines the time relevance of the first equipment and each second equipment in the plurality of second equipment according to the index space-time diagram.
With reference to the first aspect, in a possible implementation manner, the method further includes: the fault equipment prediction device compares the numerical values of the spatial correlation degree and the time correlation degree; taking the correlation degree with the largest value in the spatial correlation degree and the time correlation degree as the fault correlation degree; or, the failure device prediction apparatus takes an average value of the numerical values of the spatial correlation degree and the temporal correlation degree as the failure correlation degree.
In a second aspect, an embodiment of the present application provides a failure device prediction apparatus, where the apparatus includes: the processing unit is configured to determine a spatial association degree and a temporal association degree of the first device and each of the at least one second device, where the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, and the temporal association degree is used to characterize a time interval of a historical reporting fault of the first device and the second device; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment; the processing unit is further used for determining the fault association degree between the first equipment and each second equipment according to the space association degree and the time association degree; and the processing unit is also used for determining a second device with the highest fault association degree with the first device as a target device with a fault.
With reference to the second aspect, in a possible implementation manner, the apparatus further includes: the processing unit is also used for processing the fault of the target equipment; determining whether the fault reported by the first equipment is successfully processed within a preset time period; if the processing is successful, increasing the numerical values of the spatial correlation degree and the time correlation degree between the target equipment and the first equipment; and if the processing is not successful, reducing the spatial association degree between the target equipment and the first equipment and the numerical value of the spatial association degree.
With reference to the second aspect, in a possible implementation manner, the apparatus further includes: an acquisition unit configured to acquire physical attribute information, network attribute information, and link information of each of a plurality of devices; the plurality of devices includes a first device and at least one second device; the processing unit is further used for determining a space topological graph among the plurality of devices according to the physical attribute information, the network attribute information and the link information of each device; the spatial topological graph is used for representing the spatial position relation among the devices in the plurality of devices; and determining the spatial association degree of the first device and each second device in the plurality of second devices according to the spatial topological graph.
With reference to the second aspect, in a possible implementation manner, the apparatus further includes: the acquisition unit is further used for acquiring time information of historical reported faults of each device in the multiple devices and determining an index space-time diagram among the multiple devices; the index space-time diagram is used for representing the time interval of historical reporting faults among the devices in the plurality of devices; and the processing unit is further used for determining the time association degree of the first equipment and each second equipment in the plurality of second equipment according to the index space-time diagram.
With reference to the second aspect, in a possible implementation manner, the apparatus further includes: the processing unit is also used for comparing the numerical values of the spatial correlation degree and the time correlation degree; taking the correlation degree with the largest value in the spatial correlation degree and the time correlation degree as the fault correlation degree; or, the average value of the numerical values of the spatial correlation degree and the temporal correlation degree is used as the fault correlation degree.
In a third aspect, an embodiment of the present application provides a failure device prediction apparatus, where the failure device prediction apparatus includes: a processor and a memory; wherein the memory is configured to store computer executable instructions that, when executed by the faulty device prediction apparatus, cause the faulty device prediction apparatus to perform the faulty device prediction method as described in any one of the possible implementations of the first aspect and the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which instructions are stored, and when executed by a processor of a faulty device prediction apparatus, enable the faulty device prediction apparatus to perform the faulty device prediction method as described in the first aspect and any possible implementation manner of the first aspect.
In the present disclosure, the names of the above-mentioned failure device prediction means do not limit the devices or function modules themselves, and in actual implementation, these devices or function modules may appear by other names. Insofar as the functions of the respective devices or functional modules are similar to those of the present disclosure, they are within the scope of the claims of the present disclosure and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
The scheme at least has the following beneficial effects: in this embodiment of the present application, since the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, when the first device is a device reporting a fault, the association degree between the first device and each second device, which causes the reporting fault, may be determined according to the spatial location relationship between the devices. Since the time association degree is used for representing the time interval of the historical reporting faults of the first device and the second device, when the first device is a device reporting the faults, the association degree which causes the reporting faults between the first device and each second device can be determined according to the time interval of the historical reporting faults of the first device and the second device. According to the method and the device, the fault association degree between the first equipment and each second equipment is determined according to the spatial association degree and the time association degree. Therefore, the second device with the highest fault association degree with the first device can be determined to be the target device with the fault according to the fault association degree between the first device and each second device. Therefore, the equipment causing the reported fault can be found from a large amount of reported fault information, and the stable operation of the 5G core network service is ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a failure device prediction apparatus according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for predicting a faulty device according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a method for predicting a faulty device according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a method for predicting a faulty device according to an embodiment of the present disclosure;
fig. 5 is a schematic flowchart of a method for predicting a faulty device according to an embodiment of the present disclosure;
fig. 6 is a schematic flowchart of another fault device prediction method according to an embodiment of the present application;
fig. 7 is a schematic flowchart of another fault device prediction method according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another fault device prediction apparatus according to an embodiment of the present application.
Detailed Description
The term "and/or" herein is merely an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
The technical solution of the embodiment of the present application may be applied to various communication systems, which may be a third generation partnership project (3 GPP) communication system, for example, a Long Term Evolution (LTE) system, a 5G mobile communication system, an NR system, a new radio to Internet (NR V2X) system, a LTE and 5G hybrid networking system, or a device-to-device (D2D) communication system, a machine to machine (M2M) communication system, an Internet of Things (Internet of Things, ioT), and other next generation communication systems, or a non-3 GPP communication system, without limitation.
The technical scheme of the embodiment of the application can be applied to various communication scenes, for example, one or more of the following communication scenes: enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC), machine Type Communication (MTC), massive Machine Type Communication (MTC), SA, D2D, V2X, ioT, and other communication scenarios.
The communication system and the communication scenario applicable to the present application are only examples, and the communication system and the communication scenario applicable to the present application are not limited thereto, and are described herein in a unified manner, and will not be described again below.
In some embodiments, the terminal device referred to in the present application may be a device for implementing a communication function. A terminal device may also be referred to as a User Equipment (UE), a terminal, an access terminal, a subscriber unit, a subscriber station, a Mobile Station (MS), a remote station, a remote terminal, a Mobile Terminal (MT), a user terminal, a wireless communication device, a user agent, or a user equipment, etc. The terminal device may be, for example, a wireless terminal or a wired terminal in an IoT, a V2X, D2D, M2M, 5G network, or a Public Land Mobile Network (PLMN) for future evolution. The wireless terminal can refer to a device with wireless transceiving function, which can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on the water surface (such as a ship and the like); and may also be deployed in the air (e.g., airplanes, balloons, satellites, etc.).
By way of example, the end devices may be drones, ioT devices (e.g., sensors, electricity meters, water meters, etc.), V2X devices, stations (STs) in a Wireless Local Area Network (WLAN), cellular phones, cordless phones, session Initiation Protocol (SIP) phones, wireless Local Loop (WLL) stations, personal Digital Assistant (PDA) devices, handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, vehicle mounted devices, wearable devices (also referred to as wearable smart devices), tablets or computers with wireless transceiving capabilities, virtual reality (virtual reality, VR) terminal, wireless terminal in industrial control (industrial control), wireless terminal in self driving (self driving), wireless terminal in remote medical (remote medical), wireless terminal in smart grid (smart grid), wireless terminal in transportation safety (transportation safety), wireless terminal in smart city (smart city), wireless terminal in smart home (smart home), vehicle-mounted terminal, vehicle with vehicle-to-vehicle (V2V) communication capability, smart net-linked vehicle, UAV with drone-to-drone (U2U) communication capability, and so on. The terminal may be mobile or fixed, and the present application is not limited thereto.
In order to implement the failure device prediction method provided in the embodiment of the present application, an embodiment of the present application provides a failure device prediction apparatus for executing the failure device prediction method provided in the embodiment of the present application, and fig. 1 is a schematic structural diagram of the failure device prediction apparatus provided in the embodiment of the present application. As shown in fig. 1, the faulty device prediction apparatus 100 includes at least one processor 101, a communication line 102, and at least one communication interface 104, and may further include a memory 103. The processor 101, the memory 103 and the communication interface 104 may be connected via a communication line 102.
The processor 101 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present application, such as: one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs).
The communication link 102 may include a path for communicating information between the aforementioned components.
The communication interface 104 is used for communicating with other devices or a communication network, and may use any transceiver or the like, such as ethernet, radio Access Network (RAN), WLAN, and the like.
The memory 103 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
In a possible design, the memory 103 may exist independently from the processor 101, that is, the memory 103 may be a memory external to the processor 101, in which case, the memory 103 may be connected to the processor 101 through the communication line 102, and is used for storing execution instructions or application program codes, and is controlled by the processor 101 to execute, so as to implement the faulty device prediction method provided by the following embodiments of the present application. In yet another possible design, the memory 103 may also be integrated with the processor 101, that is, the memory 103 may be an internal memory of the processor 101, for example, the memory 103 is a cache memory, and may be used for temporarily storing some data and instruction information.
As one implementation, processor 101 may include one or more CPUs, such as CPU0 and CPU1 in fig. 1. As another implementation, the faulty device prediction apparatus 100 may include a plurality of processors, such as the processor 101 and the processor 107 in fig. 1. As yet another implementation, the faulty device prediction apparatus 100 may further include an output device 105 and an input device 106.
With the rapid development of information economy represented by the internet, cloud computing and big data, the number of 5G users is continuously increased, the scale of a cloud resource system is larger and larger, and the types and the number of devices carried by the cloud resource system are larger and larger. Thus, the fault alarm device receives different fault information reported by different types of equipment.
When one device in the cloud resource system fails, it may cause other devices to also fail. And as the types and the number of the devices carried by the cloud resource system are more and more, the fault alarm device can receive a large amount of fault information reported by different types of devices. Therefore, how to find the device causing the reported fault from a large amount of reported fault information and ensure the stable operation of the 5G core network service is still a problem to be solved.
In order to solve the technical problems in the related art, in the embodiment of the present application, since the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, when the first device is a device reporting a fault, the faulty device predicting apparatus may determine, according to the spatial location relationship between the devices, the association degree between the first device and each second device, which causes the reporting fault. Since the time association degree is used to characterize the time interval of the historical reporting failure of the first device and the historical reporting failure of the second device, when the first device is a device reporting the failure, the failure device predicting apparatus may determine, according to the time interval of the historical reporting failure of the first device and the historical reporting failure of the second device, the association degree between the first device and each second device, which causes the reporting failure. According to the method and the device for predicting the fault equipment, the fault association degree between the first equipment and each second equipment is determined according to the spatial association degree and the time association degree. Therefore, the faulty equipment prediction device may determine, according to the fault association degree between the first equipment and each second equipment, the second equipment with the highest fault association degree with the first equipment, as the target equipment with the fault. Therefore, the equipment causing the reported fault can be found from a large amount of reported fault information, and the stable operation of the 5G core network service is ensured.
Hereinafter, a method for predicting a faulty device according to an embodiment of the present application is described in detail with reference to fig. 2, where as shown in fig. 2, the method for predicting a faulty device includes:
s201, the failure equipment prediction device determines the spatial association degree and the time association degree of the first equipment and each second equipment in at least one second equipment.
The spatial association degree is used for representing a spatial position relation between the first equipment and the second equipment, and the time association degree is used for representing a time interval of historical reporting faults of the first equipment and the second equipment; the first device is a device reporting a fault, and the second device is a device other than the first device.
Optionally, the first device is a network device or a server in a 5G core network; the second device comprises a network device or a server in the 5G core network except the first device.
Illustratively, the spatial association between the target device and the first device is 5, and the temporal association between the target device and the first device is 5.5.
S202, the fault equipment prediction device determines the fault association degree between the first equipment and each second equipment according to the space association degree and the time association degree.
Optionally, the association degree of the fault between the first device and each second device is used to characterize the association degree of each second device that causes the first device to report the fault.
Illustratively, the first device reports an offline failure on day 10, 15, 10/2022, then the Leaf switch upstream of the first device may fail port down at 10. There is a degree of fault association between the above devices.
Illustratively, the first device has a fault that the temperature of the CPU is too high, and since the air inlet and the air outlet of the first device belong to the same device as the CPU and the air inlet and the air outlet of the first device are very close to the CPU, the temperature of the air outlet of the first device may also have a fault. Because the temperature of the CPU of the first device is too high, the ambient temperature of the cabinet to which the first device belongs is too high, and the temperature of the adjacent cabinet of the cabinet is also too high, a fault that the temperature of the device in the adjacent cabinet of the cabinet to which the first device belongs is too high may occur. Thus, there is a degree of fault association between the first device and devices in an adjacent cabinet to the cabinet to which the first device belongs.
And S203, the failure equipment prediction device determines the second equipment with the highest failure association degree with the first equipment as the failure target equipment.
Optionally, the target device may be one device, or may be multiple devices, which is not limited in this disclosure.
The scheme at least has the following beneficial effects: in this embodiment of the present application, since the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, when the first device is a device reporting a fault, the association degree between the first device and each second device, which causes the reporting fault, may be determined according to the spatial location relationship between the devices. Since the time association degree is used for representing the time interval of the historical reporting faults of the first device and the second device, when the first device is a device reporting the faults, the association degree which causes the reporting faults between the first device and each second device can be determined according to the time interval of the historical reporting faults of the first device and the second device. According to the method and the device, the fault association degree between the first equipment and each second equipment is determined according to the spatial association degree and the time association degree. Therefore, the second device with the highest fault association degree with the first device can be determined to be the target device with the fault according to the fault association degree between the first device and each second device. Therefore, the equipment causing the reported fault can be found from a large amount of reported fault information, and the stable operation of the 5G core network service is ensured.
In a possible implementation manner, after S203, the faulty device prediction apparatus corrects the spatial association degree and the temporal association degree between the first device and each of the at least one second device. Hereinafter, a process of correcting the spatial association degree and the temporal association degree of the first device with each of the at least one second device by the faulty device prediction apparatus will be described.
With reference to fig. 2, as shown in fig. 3, the above-mentioned procedure of correcting the spatial association degree and the temporal association degree of the first device and each of the at least one second device by the faulty device prediction apparatus may be specifically implemented by the following steps S301 to S304.
S301, the failure equipment prediction device processes the failure of the target equipment.
In a possible implementation manner, the faulty equipment prediction device sends out alarm information and displays the target equipment to be repaired, so that the equipment maintenance personnel can timely process the fault of the target equipment according to the alarm information and the displayed target equipment to be repaired.
In another possible implementation manner, the faulty device prediction apparatus sends a reminding message to the device maintainer, where the reminding message includes the target device to be maintained, so that the device maintainer can timely handle the fault of the target device according to the reminding message.
When the target device is a plurality of devices, the faulty device prediction apparatus may process any one of the plurality of target devices.
S302, the failure equipment prediction device determines whether the failure reported by the first equipment is successfully processed within a preset time period.
In a possible implementation manner, the failure device predicting apparatus queries, within a preset time period, information from the first device, where the first query information is used to query whether a failure reported by the first device is successfully processed. Correspondingly, the first equipment receives the first equipment query information, and sends a first query response message to the failure equipment prediction device according to the first equipment query information and the current self state. And the fault equipment prediction device determines whether the fault reported by the first equipment is successfully processed in a preset time period.
In another possible implementation manner, when the fault reported by the first device has been successfully processed, the first device sends indication information to the fault reporting display apparatus, where the indication information is used to indicate that the fault reported by the first device has been successfully processed. And the fault reporting display device deletes the fault reported by the first equipment from the information stored in the fault reporting display device according to the indication information. And the fault equipment prediction device sends second query information to the fault reporting display device within a preset time period, wherein the second query information is used for querying whether the fault reported by the first equipment is successfully processed. And the fault reporting display device sends a second query response message to the fault equipment prediction device according to the information stored in the fault reporting display device and the second query information. And the failure equipment prediction device determines whether the failure reported by the second equipment is successfully processed within a preset time period.
S303, if the processing is successful, the faulty device prediction apparatus increases the values of the spatial association degree and the temporal association degree between the target device and the first device.
Illustratively, the spatial association between the target device and the first device is 5, and the temporal association between the target device and the first device is 5.5. If the fault reported by the first device is successfully processed within the preset time period, the faulty device prediction apparatus increases the spatial association degree between the target device and the first device to 6, and increases the temporal association degree between the target device and the first device to 6.5.
Optionally, the faulty device predicting apparatus stores the increased values of the spatial association degree and the temporal association degree between the target device and the first device in the database.
S304, if the processing is not successful, the fault equipment prediction device reduces the spatial association degree between the target equipment and the first equipment and the numerical value of the spatial association degree.
Illustratively, the spatial association between the target device and the first device is 5, and the temporal association between the target device and the first device is 5.5. If the fault reported by the first device is not successfully processed within the preset time period, the fault device predicting apparatus reduces the spatial association degree between the target device and the first device to 4, and reduces the temporal association degree between the target device and the first device to 4.5.
Optionally, the faulty device predicting apparatus stores the reduced values of the spatial association degree and the temporal association degree between the target device and the first device in the database.
The scheme at least has the following beneficial effects: in this embodiment of the present application, the faulty device predicting apparatus corrects the values of the spatial association degree and the temporal association degree between the target device and the first device by verifying whether the target device successfully processes the values reported by the first device. The corrected spatial correlation degree and the temporal correlation degree can more accurately determine the fault correlation degree between the first device and the target device. The database of the failure device prediction apparatus stores the corrected values of the spatial association degree and the temporal association degree between the target device and the first device. Therefore, the spatial association degree and the temporal association degree between the target device and the first device can be directly obtained through the database again.
In a possible implementation manner, referring to fig. 2 and as shown in fig. 4, the process of determining the spatial association degree between the first device and each of the at least one second device by the faulty device predicting apparatus in S201 may specifically be implemented by the following S401 to S403:
s401, the failure equipment prediction device obtains physical attribute information, network attribute information and link information of each of a plurality of equipment.
Wherein the plurality of devices includes a first device and at least one second device.
In one possible implementation, when the plurality of devices are network devices, the physical attribute information of the devices includes at least one of the following parameters: a Data Center (DC) to which the network equipment belongs, a machine room to which the network equipment belongs, a rack to which the network equipment belongs, a U bit to which the network equipment belongs, and a serial number of the network equipment; the network properties of the network device include at least one of the following parameters: a port MAC Address, a Media Access Control Address (MAC), a peer device serial number, a port MAC forwarding table, and a port Address Resolution Protocol (ARP) table.
In another possible implementation manner, when the plurality of devices are servers, the server attribute information includes at least one of the following parameters: the system comprises a DC to which a server belongs, a machine room to which the server belongs, a rack to which the server belongs and a U bit to which the server belongs; the server network attribute comprises at least one of the following parameters: the method comprises the steps of constructing an out-of-band management port topology and a service network port topology, wherein the equipment serial number and the port MAC address need to be acquired, and constructing the service network port topology and the equipment serial number and the network port MAC address need to be acquired.
Optionally, the link information includes: link information between network devices, link information between network devices and servers.
It should be noted that, when multiple devices are network devices, if the serial numbers of the peer devices and the MAC addresses of the peer ports between two network devices are the same, the two network devices have a link connection relationship. When a plurality of devices simultaneously include a network device and a server, if a port MAC forwarding table address of the network device is the same as a port MAC address of a BMC of the server, a Baseboard Management Controller (BMC) port link exists between the server and the network device. When a plurality of devices simultaneously comprise network devices and a server, if the opposite-end device serial number and the opposite-end port MAC address between the two devices are different from the device serial number and the port MAC address of the server, comparing a port ARP table between the two devices with the service port MAC address of the server; if the port ARP table between the two devices is the same as the MAC address of the service port of the server, a service port link exists between the server and the network device.
S402, the failure equipment prediction device determines a space topological graph among a plurality of equipment according to the physical attribute information and the network attribute information of each equipment.
The spatial topological graph is used for representing spatial position relations among the devices in the multiple devices.
In one possible implementation, the faulty device prediction apparatus determines a location topology map among the multiple devices according to the physical attribute information and the network attribute information of each device.
And S403, determining the spatial association degree of the first equipment and each second equipment in the plurality of second equipment by the failure equipment prediction device according to the spatial topological graph.
Optionally, if the device a in the second device and the first device belong to the same device, the spatial association degree between the device a and the first device is 5; if the device B in the second device is adjacent to the first device, the spatial association degree between the device B and the first device is 4; if the device C in the second device and the first device belong to devices in the same cabinet, the spatial association degree between the device C and the first device is 3; if the cabinet to which the D equipment belongs in the second equipment is adjacent to the cabinet to which the first equipment belongs, the spatial association degree of the D equipment and the first equipment is 2; the cabinet to which the E device in the second device belongs and the cabinet to which the first device belongs belong to the same row of cabinets, and then the spatial association degree between the E device and the first device is 1.
The scheme at least brings the following beneficial effects: in this embodiment of the application, the faulty device prediction apparatus may determine, according to the spatial topological graph, a spatial position relationship between the first device and each of the plurality of second devices, so as to accurately obtain, according to the spatial position relationship, a spatial association degree between the first device and each of the plurality of second devices.
In a possible implementation manner, referring to fig. 2 and as shown in fig. 5, the process of determining the time association degree between the first device and each of the at least one second device by the faulty device predicting apparatus in S201 may be specifically implemented by the following S501-S502:
s501, the fault equipment prediction device obtains time information of historical reported faults of each piece of equipment in the plurality of pieces of equipment, and determines index space-time diagrams among the plurality of pieces of equipment.
The index space-time diagram is used for representing time intervals of historical reporting faults among the devices.
Optionally, the index space-time diagram includes: equipment name, fault name and fault reporting time.
Illustratively, the failure name reported by the first device is CPU memory is too high, and the failure reporting time is 10 in 2022, 10 in month, 18 in day 10.
S502, the failure equipment prediction device determines the time association degree of the first equipment and each second equipment in the plurality of second equipment according to the index space-time diagram.
In a possible implementation manner, the failure device predicting apparatus determines the time association degree between the first device and each of the plurality of second devices according to a time interval between the first device and each of the plurality of second devices that have reported the failure historically.
Optionally, when the time interval between historical reporting failures of the F devices in the first device and the second device is less than or equal to 15S, the time association degree between the F devices in the first device and the second device is 5; when the time interval of the historical reported faults of the G equipment in the first equipment and the G equipment in the second equipment is less than or equal to 30S, the time association degree of the G equipment in the first equipment and the G equipment in the second equipment is 4; when the time interval of historical reported faults of the H equipment in the first equipment and the second equipment is less than or equal to 45S, the time association degree of the H equipment in the first equipment and the second equipment is 3; when the time interval of the I equipment historical reported faults in the first equipment and the second equipment is less than or equal to 60S, the time association degree of the I equipment in the first equipment and the second equipment is 2; when the time interval of the historical reported faults of the M devices in the first device and the second device is less than or equal to 75S, the time association degree of the M devices in the first device and the second device is 1; and when the time interval of the historical reported faults of the N devices in the first device and the second device is greater than 75S, the time association degree of the N devices in the first device and the second device is 0.
The scheme at least has the following beneficial effects: in this embodiment of the present application, the faulty device predicting apparatus may determine, according to the index space-time diagram, a time interval of historical reporting faults of the first device and each of the plurality of second devices, so as to accurately obtain, according to the time interval of reporting faults, a time association degree between the first device and each of the plurality of second devices.
In a possible implementation manner, referring to fig. 2 and as shown in fig. 6, the process of determining the fault association degree between the first device and each of the at least one second device by the faulty device predicting apparatus in S202 may be specifically implemented by the following S601-S602:
s601, the failure equipment prediction device compares the numerical values of the spatial correlation degree and the time correlation degree.
Illustratively, the spatial association degree of the first device with the a device of the second devices is 5, and the temporal association degree of the first device with the a device of the second devices is 3. Obviously, the value of the spatial association degree of the first device and the a device in the second device is larger than the time association degree.
And S602, the fault equipment prediction device takes the correlation degree with the largest value in the spatial correlation degree and the time correlation degree as the fault correlation degree.
Illustratively, since the spatial association degree of the first device and the a device of the second devices is 5, and the temporal association degree of the first device and the a device of the second devices is 3, the fault association degree of the first device and the a device of the second devices is 5.
The scheme at least brings the following beneficial effects: in the embodiment of the application, the failure device prediction apparatus uses the association degree with the largest value among the spatial association degree and the temporal association degree as the failure association degree, and can accurately determine the failure association degree between the first device and each second device.
In another possible implementation manner, with reference to fig. 2 and as shown in fig. 7, the process of determining the time association degree between the first device and each of the at least one second device by the faulty device predicting apparatus in S202 may specifically be implemented by the following S701:
and S701, the fault equipment prediction device takes the average value of the numerical values of the spatial correlation degree and the time correlation degree as the fault correlation degree.
Illustratively, the spatial association degree of the first device with the a device of the second devices is 5, the temporal association degree of the first device with the a device of the second devices is 3, and then the fault association degree of the first device with the a device of the second devices is 4.
In the embodiment of the application, the failure device prediction apparatus uses the average value of the numerical values of the spatial relevance degree and the temporal relevance degree as the failure relevance degree, and can accurately determine the failure relevance degree between the first device and each second device.
It can be seen that the technical solutions provided in the embodiments of the present application are mainly introduced from the perspective of methods. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed in hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiment of the present application, the functional modules of the failure device prediction apparatus may be divided according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Optionally, the division of the modules in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 8 is a schematic structural diagram of a faulty device prediction apparatus 80 according to an embodiment of the present disclosure. The faulty device prediction apparatus 80 includes: a processing unit 801 and an acquisition unit 802.
A processing unit 801, configured to determine a spatial association degree and a temporal association degree of each of the first device and at least one second device, where the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, and the temporal association degree is used to characterize a time interval of a historical reporting fault of the first device and the second device; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment; the processing unit 801 is further configured to determine a fault association degree between the first device and each second device according to the spatial association degree and the temporal association degree; the processing unit 801 is further configured to determine a second device with the highest fault association degree with the first device, as a target device with a fault.
Optionally, the processing unit 801 is further configured to process a failure of the target device; determining whether the fault reported by the first equipment is successfully processed within a preset time period; if the processing is successful, increasing the numerical values of the spatial correlation degree and the time correlation degree between the target equipment and the first equipment; and if the processing is not successful, reducing the spatial association degree between the target equipment and the first equipment and the numerical value of the spatial association degree.
Optionally, the obtaining unit 802 is configured to obtain physical attribute information, network attribute information, and link information of each of the multiple devices; the plurality of devices includes a first device and at least one second device; the processing unit 801 is further configured to determine a spatial topology map among multiple devices according to the physical attribute information, the network attribute information, and the link information of each device; the spatial topological graph is used for representing the spatial position relation among the devices in the plurality of devices; and determining the spatial association degree of the first device and each second device in the plurality of second devices according to the spatial topological graph.
Optionally, the obtaining unit 802 is further configured to obtain time information of a failure reported by each device in the multiple devices in a history manner, and determine an index space-time diagram between the multiple devices; the index space-time diagram is used for representing the time interval of historical reporting faults among the devices in the plurality of devices; the processing unit 801 is further configured to determine, according to the index space-time diagram, a time association degree between the first device and each of the plurality of second devices.
Optionally, the processing unit 801 is further configured to compare the spatial correlation degree and the temporal correlation degree; taking the correlation degree with the largest value in the spatial correlation degree and the time correlation degree as the fault correlation degree; or, the average value of the numerical values of the spatial correlation degree and the temporal correlation degree is used as the fault correlation degree.
The processing unit 801 may be a processor or a controller, among others. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. A processor may also be a combination of computing functions, e.g., comprising one or more microprocessors in conjunction with a DSP or microprocessors, a combination of DSPs and microprocessors, or the like. The communication unit may be a transceiver circuit or a communication interface, etc. The storage module may be a memory. When the processing unit 801 is a processor, the obtaining unit 802 is a communication interface, and the storage module is a memory, the faulty device prediction apparatus according to the embodiment of the present application may be the faulty device prediction apparatus shown in fig. 1.
Through the description of the above embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the foregoing function distribution may be completed by different functional modules according to needs, that is, the internal structure of the network node is divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the module and the network node described above, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer executes each step in the method flow shown in the above method embodiment.
The embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is configured to run a computer program or instructions to implement the fault device prediction method in the foregoing method embodiment.
Embodiments of the present application provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of fault device prediction in the above-described method embodiments.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, and a hard disk. Random Access Memory (RAM), read-Only Memory (ROM), erasable Programmable Read-Only Memory (EPROM), registers, a hard disk, an optical fiber, a portable Compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium, in any suitable combination, or as appropriate in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Since the apparatus, the device, the computer-readable storage medium, and the computer program product in the embodiments of the present invention may be applied to the method described above, for technical effects obtained by the apparatus, the computer-readable storage medium, and the computer program product, reference may also be made to the method embodiments described above, and details of the embodiments of the present application are not repeated herein.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A method of predicting a malfunctioning device, the method comprising:
determining a spatial association degree and a temporal association degree of each of a first device and at least one second device, wherein the spatial association degree is used for representing a spatial position relationship between the first device and the second device, and the temporal association degree is used for representing a time interval of historical reporting faults of the first device and the second device; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment;
determining a fault association degree between the first equipment and each second equipment according to the space association degree and the time association degree;
and determining the second equipment with the highest fault association degree with the first equipment as the target equipment with the fault.
2. The method according to claim 1, wherein after determining that the second device with the highest fault association degree with the first device is the target device with the fault, the method further comprises:
processing a failure of the target device;
determining whether the fault reported by the first equipment is successfully processed within a preset time period;
if the processing is successful, increasing the numerical values of the spatial association degree and the time association degree between the target equipment and the first equipment;
and if the processing is not successful, reducing the spatial association degree between the target equipment and the first equipment and the numerical value of the spatial association degree.
3. The method of claim 1, wherein determining the spatial association of the first device with each of the plurality of second devices comprises:
acquiring physical attribute information, network attribute information and link information of each device in a plurality of devices; the plurality of devices includes the first device and the at least one second device;
determining a spatial topological graph among the plurality of devices according to the physical attribute information, the network attribute information and the link information of each device; the spatial topological graph is used for representing spatial position relations among the devices in the plurality of devices;
and determining the spatial association degree of the first equipment and each second equipment in a plurality of second equipment according to the spatial topological graph.
4. The method of claim 1, wherein determining a degree of temporal association of the first device with each of the plurality of second devices comprises:
acquiring time information of historical reported faults of each device in a plurality of devices, and determining an index space-time diagram among the devices; the index space-time diagram is used for representing the time interval of historical reporting faults among the devices;
and determining the time association degree of the first equipment and each second equipment in a plurality of second equipment according to the index space-time diagram.
5. The method according to any one of claims 1 to 4, wherein the determining the fault association degree between the first device and each of the second devices according to the spatial association degree and the temporal association degree comprises:
comparing the values of the spatial correlation degree and the temporal correlation degree; taking the correlation degree with the largest value in the spatial correlation degree and the temporal correlation degree as the fault correlation degree;
or the like, or, alternatively,
and taking the average value of the numerical values of the spatial correlation degree and the time correlation degree as the fault correlation degree.
6. A faulty device prediction apparatus, characterized in that the apparatus comprises a processing unit:
the processing unit is configured to determine a spatial association degree and a temporal association degree of a first device and each of at least one second device, where the spatial association degree is used to characterize a spatial location relationship between the first device and the second device, and the temporal association degree is used to characterize a time interval of a historical reporting fault of the first device and the second device; the first equipment is equipment for reporting faults, and the second equipment is equipment except the first equipment;
the processing unit is further configured to determine a fault association degree between the first device and each of the second devices according to the spatial association degree and the temporal association degree;
the processing unit is further configured to determine a second device with a highest fault association degree with the first device, as a target device with a fault.
7. The apparatus of claim 6, further comprising:
the processing unit is further used for processing the fault of the target equipment; determining whether the fault reported by the first equipment is successfully processed within a preset time period; if the processing is successful, increasing the numerical values of the spatial association degree and the time association degree between the target equipment and the first equipment; and if the processing is not successful, reducing the spatial association degree between the target equipment and the first equipment and the numerical value of the spatial association degree.
8. The apparatus of claim 6, further comprising: an acquisition unit:
the acquiring unit is used for acquiring physical attribute information, network attribute information and link information of each device in a plurality of devices; the plurality of devices includes the first device and the at least one second device;
the processing unit is further configured to determine a spatial topological graph among the multiple devices according to the physical attribute information, the network attribute information, and the link information of each device; the spatial topological graph is used for representing spatial position relations among the devices in the plurality of devices; and determining the spatial association degree of the first equipment and each second equipment in a plurality of second equipment according to the spatial topological graph.
9. The apparatus according to claim 6, wherein the obtaining unit is further configured to obtain time information of a history reported fault of each device in the multiple devices, and determine an index space-time diagram among the multiple devices; the index space-time diagram is used for representing the time interval of historical reporting faults among the devices;
the processing unit is further configured to determine, according to the index space-time diagram, a time association degree between the first device and each of the plurality of second devices.
10. The apparatus according to any one of claims 6 to 9, wherein the processing unit is further configured to compare the values of the spatial correlation and the temporal correlation; taking the correlation degree with the largest value in the spatial correlation degree and the temporal correlation degree as the fault correlation degree; or, taking the average value of the numerical values of the spatial correlation degree and the time correlation degree as the fault correlation degree.
11. A faulty equipment prediction apparatus, comprising: a processor and a memory; wherein the memory is configured to store computer-executable instructions that, when executed by the faulty device prediction apparatus, are executed by the processor to cause the faulty device prediction apparatus to perform the faulty device prediction method of any one of claims 1-5.
12. A computer-readable storage medium, comprising instructions that, when executed by a faulty device prediction apparatus, cause the computer to perform the faulty device prediction method according to any one of claims 1-5.
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