CN115442216B - Network slice fault self-healing method, device, equipment and computer storage medium - Google Patents

Network slice fault self-healing method, device, equipment and computer storage medium Download PDF

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CN115442216B
CN115442216B CN202110628791.2A CN202110628791A CN115442216B CN 115442216 B CN115442216 B CN 115442216B CN 202110628791 A CN202110628791 A CN 202110628791A CN 115442216 B CN115442216 B CN 115442216B
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self
healing
action
slice
fault
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CN115442216A (en
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邢彪
丁东
冯杭生
陈嫦娇
陈向荣
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China Mobile Communications Group Co Ltd
China Mobile Group Zhejiang Co Ltd
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China Mobile Communications Group Co Ltd
China Mobile Group Zhejiang Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Environmental & Geological Engineering (AREA)
  • Test And Diagnosis Of Digital Computers (AREA)

Abstract

The invention discloses a network slice fault self-healing method, a device, equipment and a computer program product, wherein the method comprises the following steps: acquiring multi-dimensional index data of a network slice, and detecting the multi-dimensional index data; when abnormal data are detected, generating a slice self-healing action and a slice fault action according to the multidimensional index data and judging the slice self-healing action and the slice fault action to determine a behavior score of the generated slice self-healing action and the slice fault action; and carrying out iterative optimization on the generated self-healing actions of the slice and the fault actions of the slice according to the behavior scores so as to determine a target self-healing action capable of self-healing the fault of the network slice and carry out fault repair on the network slice. The invention respectively generates the slice self-healing action and the slice fault action to form countermeasures by the double intelligent agents, forces the generated slice self-healing action to continuously improve the fault repairing capability of the slice self-healing action, improves the performance of the fault self-healing strategy, and further improves the fault self-healing recovering capability of the network slice.

Description

Network slice fault self-healing method, device, equipment and computer storage medium
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a network slice fault self-healing method, apparatus, device, and computer program product.
Background
The existing network slice fault self-healing method needs to rely on experience of technicians to manually set a self-healing strategy, the self-healing strategy mainly realizes triggering of slice fault self-healing action by simply setting thresholds for various index data, and when the self-healing strategy is realized by relying on manual setting rules, the self-healing efficiency of faults is low, errors are easy to occur, and when the network slice changes, the strategy cannot be timely adjusted. In the existing slice fault self-healing process, firstly, the fault type and the fault influence range are required to be analyzed, then, a proper fault processing process is determined, fault repair or fault self-healing is realized based on an artificially defined strategy, the self-healing process is longer, more time is spent on fault analysis and positioning, and service influence time is longer, so that user experience is influenced, and therefore, the existing network slice fault self-healing strategy relying on manual setting rules is poor in fault self-healing performance.
Disclosure of Invention
The invention mainly aims to provide a network slice fault self-healing method, device, equipment and computer program product, which aim to solve the technical problem that the existing network slice fault self-healing strategy dependent on manual setting rules is poor in fault self-healing performance.
In addition, in order to achieve the above purpose, the present invention also provides a network slice fault self-healing method, which includes the following steps:
acquiring multi-dimensional index data of the network slice, and detecting the acquired multi-dimensional index data;
when abnormal index data exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the abnormal index data;
judging the generated self-healing actions and fault actions of the slice to determine the action scores of the self-healing actions and the fault actions of the slice;
and performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action.
Optionally, the step of generating a slice self-healing action and a slice fault action according to the multidimensional index data includes:
inputting the multi-dimensional index data into a preset target self-healing model, wherein the target self-healing model is obtained by performing iterative training on the preset self-healing model to be trained by utilizing the historical multi-dimensional index data of a network slice, and the target self-healing model comprises an action generator;
And generating a slice self-healing action and a slice fault action by using an action generator of the target self-healing model according to the multi-dimensional index data.
Optionally, the action generator includes a self-healing action generator and a fault action generator, and the step of generating a slice self-healing action and a slice fault action by using the action generator of the target self-healing model according to the multidimensional index data includes:
determining a current full information state of the network slice according to the multi-dimensional index data, wherein the full information state comprises a first state observable by the self-healing action generator and a second state observable by the fault action generator;
inputting a first state in the full information states into the self-healing action generator, and generating a slicing self-healing action by using the self-healing action generator;
and inputting a second state in the all-information states into the fault action generator, and generating a slicing fault action by using the fault action generator.
Optionally, the target self-healing model further includes a self-healing action evaluator and a fault action evaluator, and the step of evaluating the generated slice self-healing action and slice fault action to determine a behavior score of the slice self-healing action and the slice fault action includes:
Inputting a first state in the full information state, the slice self-healing action and the slice fault action into a self-healing action judging device in the target self-healing model so as to judge the slice self-healing action and determine a behavior score of the slice self-healing action;
determining a fault destruction radius according to the slice fault action;
and inputting a second state in the full information state, the fault destruction radius, the slice self-healing action and the slice fault action into a fault action judging device in the target self-healing model so as to judge the slice fault action and determine a behavior score of the slice fault action.
Optionally, the step of iteratively optimizing the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action and performing fault repair on the network slice by using the target self-healing action includes:
feeding back the behavior score of the slice self-healing action to the self-healing action generator, feeding back the behavior score of the slice fault action to the fault action generator, returning to and executing the step of generating the slice self-healing action by using the self-healing action generator in the target self-healing model and generating the slice fault action by using the fault action generator in the target self-healing model, so as to perform iterative optimization on the slice self-healing action and the slice fault action until the behavior score of the slice self-healing action and the behavior score of the slice fault action meet preset conditions, and obtaining the target self-healing action;
And switching the state of the network slice according to the target self-healing action so as to switch the network slice from the current full-information state to the target full-information state corresponding to the target self-healing action, and repairing the fault of the network slice.
Optionally, after the step of iteratively optimizing the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action and performing fault repair on the network slice by using the target self-healing action, the method includes:
inputting the target self-healing action into a preset rewarding function to obtain a rewarding value corresponding to the target self-healing action;
and generating an experience playback data set according to the reward value, and updating model parameters of the target self-healing model according to the experience playback data set.
Optionally, before the step of generating the slice self-healing action and the slice fault action according to the target index data, the method further includes:
acquiring historical index data of a network slice and preprocessing the historical index data to obtain sample data;
obtaining model architecture parameters, and establishing a basic self-healing model according to the model architecture parameters;
And carrying out iterative training on the basic self-healing model by using the sample data to obtain a target self-healing model.
In addition, to achieve the above object, the present invention further provides a network slice fault self-healing device, including:
the data detection module is used for acquiring the multi-dimensional index data of the network slice and detecting the acquired multi-dimensional index data;
the action generating module is used for generating a slice self-healing action and a slice fault action according to the multi-dimensional index data when abnormal data exist in the multi-dimensional index data of the network slice, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action;
and the dual-agent countermeasure module is used for carrying out iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score so as to determine a target self-healing action and carrying out fault repair on the network slice by utilizing the target self-healing action.
In addition, to achieve the above object, the present invention further provides a network slice fault self-healing device, including: the system comprises a memory, a processor and a network slice fault self-healing program which is stored in the memory and can run on the processor, wherein the network slice fault self-healing program realizes the steps of the network slice fault self-healing method when being executed by the processor.
Furthermore, to achieve the above object, the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the network slice fault self-healing method as described above.
The embodiment of the invention provides a network slice fault self-healing method, device, equipment and a computer program product. Compared with the existing network slice fault self-healing method with poor self-healing performance of the self-healing strategy fault, the embodiment of the invention acquires the multidimensional index data of the network slice and detects the acquired multidimensional index data; when abnormal data are detected to exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the multi-dimensional index data, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action; and performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action. When detecting that the most index data of the network slice are abnormal, the fault is actively manufactured through the action of generating the slice fault and is opposed to the generated slice self-healing action, so that the self-healing action of the generated slice is forced to improve the self-healing capacity of the fault, the self-healing performance of the network slice self-healing strategy is further improved, the fault problem in the network slice can be timely identified and repaired, the fault self-healing is recovered before the fault of the network slice is interrupted, serious consequences are avoided, and the self-healing capacity of the network slice is improved.
Drawings
Fig. 1 is a schematic hardware structure of an implementation manner of a network slice fault self-healing device provided by an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a network slice fault self-healing method according to the present invention;
FIG. 3 is a schematic diagram illustrating a countermeasure process of the action generator in a second embodiment of the network slice fault self-healing method according to the present invention;
FIG. 4 is a schematic diagram of a neural network hierarchy of an action generator and an action evaluator in a third embodiment of a network slice fault self-healing method according to the present invention;
fig. 5 is a schematic functional block diagram of an embodiment of a network slice fault self-healing device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present invention, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.
The main solution of the embodiment of the invention is as follows: the method comprises the steps of obtaining multi-dimensional index data of a network slice, and detecting the obtained multi-dimensional index data; when abnormal data are detected to exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the multi-dimensional index data, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action; and performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action. The fault is actively manufactured, so that the dual-agent generating the self-healing action of the slice and the fault action of the slice are opposed, the fault self-healing performance of the network slice self-healing strategy is improved, the fault problem in the network slice is timely identified and repaired, the fault self-healing is recovered before the fault of the network slice is interrupted, serious consequences are avoided, and the self-healing recovery capacity of the network slice is further improved.
The embodiment of the invention relates to main technical terms:
network Slice (NS): network slicing refers to customizing different logical networks according to different service requirements on a physical or virtual network infrastructure. The network slice may be a complete end-to-end network comprising terminal equipment, access network, transport network, core network and application server, capable of providing complete communication services, with a certain network capability. The network slice may also be any combination of terminal devices, access networks, transport networks, core networks and application servers.
Chaotic engineering (Chaos Engineering): chaotic engineering is a technical means capable of ensuring the availability of a system and improving the elastic capability of a technical architecture, and aims to kill a fault in swaddling, namely, identify the fault before the fault causes interruption, and actively identify and repair the fault problem by actively manufacturing the fault and testing the behavior of the system under various pressures so as to avoid serious consequences.
NSMF: and a network slice management function (Network Slice Management Function) for receiving network slice requirements, managing the life cycle, performance, faults and the like of the network slices, arranging the composition of the network slices, decomposing the network slice requirements into requirements of each network slice subnet or network function, and sending a network slice subnet management request to each NSSMF.
NSSMF: a network slice subnet management function (Network Slice Subnet Management Function) receives network slice subnet deployment requirements issued from the NSMF, manages the network slice subnets, orchestrates the composition of the network slice subnets, maps SLA (Service Level Agreement ) requirements of the network slice subnets to QoS (Quality of Service ) requirements of the network services, and issues deployment requests for the network services.
According to the embodiment of the invention, in the existing related scheme, the network slice fault self-healing strategy depends on manually set rules, the self-healing recovery process is long in time consumption and easy to make mistakes, and when the slice fault changes, more time is used for fault analysis and positioning, so that the self-healing strategy cannot be adjusted in time. In order to solve the problems, chaotic engineering is introduced into a self-healing strategy of the network slice, and the fault problem is actively identified and repaired by actively manufacturing faults and testing the behaviors of the system under various pressures, so that serious consequences are avoided. However, the existing network slice fault self-healing method based on chaotic engineering mainly depends on manual manufacturing faults, and the manufacturing fault rule is easy to learn by a self-healing system, so that the generalization capability and the self-healing recovery capability of a self-healing strategy are reduced. Therefore, the existing network slice fault self-healing strategy generally has the problem of poor fault self-healing performance.
Therefore, the embodiment of the invention provides a solution, when the abnormality of the multidimensional index data of the network slice is detected, the fault is actively manufactured through the action of generating the slice fault, and the fault is opposed to the generated slice self-healing action, so that the generated slice self-healing action is forced to continuously improve the self-healing capacity of the fault, the performance of the fault self-healing strategy is further improved, the fault is identified and repaired before the interruption caused by the network slice fault, the self-healing capacity of the network slice is improved, and serious consequences are avoided.
Specifically, referring to fig. 1, fig. 1 is a schematic diagram of a functional module of a terminal device (also called a terminal or a device) to which a network slice fault self-healing device according to an embodiment of the present invention belongs, where the terminal device may be a PC, or may be a mobile terminal device with a data processing function, such as a smart phone, a tablet computer, and a portable computer.
As shown in fig. 1, the terminal device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and so on. Among other sensors, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile terminal is stationary, and the mobile terminal can be used for recognizing the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like, which are not described herein.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a network slice fault self-healing program may be included in memory 1005 as a computer program product.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to invoke a network slice fault self-healing program stored in the memory 1005, which when executed by the processor, implements the operations in the network slice fault self-healing method provided in the embodiments described below.
Based on the hardware structure of the equipment, the embodiment of the network slice fault self-healing method is provided.
Referring to fig. 2, in a first embodiment of the network slice fault self-healing method of the present invention, the network slice fault self-healing method includes:
step S10, multi-dimensional index data of a network slice are obtained, and the obtained multi-dimensional index data are detected;
with the development of communication technology, particularly the popularization and application of the fifth generation mobile communication (5G) technology, the concept of network slicing is introduced to cope with the difference of network performance requirements of different communication services. In order to ensure the continuity of network services, the network slice needs to have certain self-healing recovery capability, and before network interruption is caused by the network slice fault, the fault self-healing is carried out.
The invention provides a network slice fault self-healing method applicable to 5G network slices, which is based on reinforcement learning and chaotic engineering of double intelligent agents, generates fault actions and self-healing actions through active manufacturing faults, improves the generalization capability of slice self-healing by utilizing the countermeasures between the fault actions and the self-healing actions, forces the generated self-healing actions to continuously improve the self-healing capability of the network slices, and further improves the self-healing recovery capability of the network slices. Specifically, first, multi-dimensional index data of a network slice is acquired, and the acquired multi-dimensional index data is detected to determine whether an abnormality exists in the network slice. It is known that NSMF and NSSMF are generally disposed in a network environment where a network slice is located, where the NSMF is responsible for managing the network slice and is mainly responsible for monitoring multidimensional index data of the network slice, where the network slice generally includes a terminal device, an access network, a transmission network, a core network and an application server, and where the access network, the transmission network and the core network in the network slice include a plurality of network sub-slices managed by the NSSMF, such as a radio access network sub-slice, a transmission network sub-slice, a core network sub-slice, and so on. In this embodiment, when multi-dimensional index data of a network slice is acquired, KPI (Key Performance Indicator, key performance index or target quantization management index) data of a network sub-slice is mainly acquired from an NSMF, including KPIs of network sub-slices such as a radio access network sub-slice, a transmission network sub-slice, a core network sub-slice, etc., where the KPIs of the radio access network sub-slice include a radio access network transmission delay, an average throughput rate of uplink/downlink users, an average throughput rate of uplink/downlink cells, an average CPU occupancy rate, an online user number, a QoS flow establishment success rate, a call establishment success rate, etc.; the KPI of the transmission network sub-slice comprises transmission delay, bandwidth utilization rate, packet loss rate, data transmission quantity, bit error rate and the like of the transmission network; the KPI of the core network sub-slice comprises core network transmission delay, virtualized storage resource utilization rate, virtualized network resource utilization rate, virtualized computing resource utilization rate, error code number, request success rate and the like. And detecting the acquired KPI data, and when abnormal KPI data is detected, for example, the value of a certain KPI is obviously beyond the normal range of the KPI, indicating that a fault exists in a network slice or a potential fault exists, and identifying and automatically repairing the fault before the service interruption caused by the fault, so that the fault of the network slice is self-healed, and serious consequences such as downtime are avoided.
Step S20, when abnormal data are detected to exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the multi-dimensional index data, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action;
in this embodiment, the network slice fault self-healing method is provided with a self-healing model, and when it is detected that abnormal data exists in the acquired multi-index data, the model can generate a slice self-healing action and a slice fault action according to the acquired multi-dimensional index data, and then evaluate the generated fault action and self-healing action, so as to determine the behavior scores of the generated slice self-healing action and the slice fault action. When the slice fault action is generated, the model is converged towards the direction of high action score of the slice fault action so as to generate fault action which makes the network slice difficult to repair, and when the slice self-healing action is generated, the model is converged towards the direction of high action score of the slice self-healing action so as to generate self-healing action which can repair the network slice fault. And judging the generated self-healing action and fault action of the slice, and according to the action scores of the self-healing action and the fault action of the slice, countering the self-healing action and the fault action of the slice, so that the self-healing action of the generated slice is forced to continuously improve the fault repairing capability of the self-healing action of the slice.
The refinement of the step S20 comprises the steps of A1-A2:
step A1, inputting the multi-dimensional index data into a preset target self-healing model, wherein the target self-healing model is obtained by performing iterative training on the preset self-healing model to be trained by utilizing the historical multi-dimensional index data of a network slice, and the target self-healing model comprises an action generator;
and step A2, generating a slicing self-healing action and a slicing fault action by using an action generator of the target self-healing model according to the multidimensional index data.
Further, when the self-healing action and the fault action of the slice are generated, the acquired multidimensional index data can be preprocessed, the preprocessing includes but is not limited to normalization processing, taking normalization processing as an example, the acquired index data is scaled and mapped to a smaller interval in a unified mode, that is, the data is scaled to be between a given minimum value and a given maximum value, usually between 0 and 1, so as to obtain target index data, and then when the obtained target index data is processed, the convergence speed of the data and the precision of data processing can be improved. The multi-dimensional index data obtained through pretreatment is input into a preset target self-healing model, the target self-healing model is obtained by performing iterative training on a basic self-healing model to be trained by utilizing historical multi-dimensional index data of a network slice, the model comprises an action generator of a double agent, and slice self-healing actions and slice fault actions can be respectively generated according to the input multi-dimensional index data.
Further, the action generator includes a self-healing action generator and a fault action generator, and in step A2, the step of generating the slice self-healing action and the slice fault action by the action generator of the target self-healing model according to the multidimensional index data is refined, including steps a21-a23:
step A21, determining the current full information state of the network slice according to the multi-dimensional index data, wherein the full information state comprises a first state observable by the self-healing action generator and a second state observable by the fault action generator;
step A22, inputting a first state in the full information state into the self-healing action generator, and generating a slicing self-healing action by using the self-healing action generator;
and step A23, inputting a second state in the all-information state into the fault action generator, and generating a slicing fault action by using the fault action generator.
In this embodiment, the action generator of the target self-healing model includes a self-healing action generator (actor 1) and a fault action generator (actor 2), and when the slice self-healing action and the slice fault action are generated by using the action generator of the preset target self-healing model, the current full information state of the network slice is first determined according to the input target index data, where the full information state includes a first state (s 1 ) And a second state observable by actor2 (s 2 ). Then using actor1 to observe the state s of the network slice according to the method 1 Generating a slice self-healing action, and utilizing an actor2 to observe the state s of the network slice according to the slice self-healing action 2 And generating a slice fault action.
And step S30, performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action.
In the target self-healing model, different action generators generate corresponding actions according to the part states of the network slice which can be observed by the action generators, judge the generated slice self-healing actions and slice fault actions, determine the action score of the generated actions, and perform iterative optimization on the generated slice self-healing actions and slice fault actions according to the action score, so that the self-healing action generators are forced to generate slice self-healing actions with higher fault repairing capability, and further determine the target self-healing actions capable of enabling the network slice fault to self-heal. And performing fault restoration on the network slice by utilizing the target self-healing action so as to self-heal the fault of the network slice. The fault repairing method for the network slice includes, but is not limited to, switching the state of the network slice according to the determined target self-healing action.
In the embodiment, the multi-dimensional index data of the network slice is acquired, and the acquired multi-dimensional index data is detected; when abnormal data are detected to exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the multi-dimensional index data, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action; and performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action. When detecting that the most index data of the network slice are abnormal, the fault is actively manufactured through the action of generating the slice fault and is opposed to the generated slice self-healing action, so that the self-healing action of the generated slice is forced to improve the self-healing capacity of the fault, the self-healing performance of the network slice self-healing strategy is further improved, the fault problem in the network slice can be timely identified and repaired, the fault self-healing is recovered before the fault of the network slice is interrupted, serious consequences are avoided, and the self-healing capacity of the network slice is improved.
Further, on the basis of the above embodiment of the present invention, a second embodiment of the network slice fault self-healing method of the present invention is provided.
The present embodiment is a step of refining step S20 in the first embodiment, and the difference between the present embodiment and the above embodiment of the present invention is that the target self-healing model in the present embodiment further includes a self-healing action judging device and a fault action judging device, and in step S20 in the above embodiment, the generated slice self-healing action and slice fault action are judged to determine refinement of the action scores of the slice self-healing action and the slice fault action: comprises the steps of B1-B3:
step B1, inputting a first state in the full information state, the slicing self-healing action and the slicing fault action into a self-healing action judging device in the target self-healing model so as to judge the slicing self-healing action and determine a behavior score of the slicing self-healing action;
step B2, determining a fault destruction radius according to the slice fault action;
and B3, inputting the second state, the fault destruction radius, the slicing self-healing action and the slicing fault action in the full information state into a fault action judging device in the target self-healing model so as to judge the slicing fault action and determine the action score of the slicing fault action.
Based on the above embodiment, in the present embodiment, when the generated slice self-healing action and the slice fault action are evaluated, the full information state of the network slice, the generated slice self-healing action, the generated slice fault action, and the like are input into the evaluator of the target self-healing model to be evaluated, so as to determine the behavior scores of the generated slice self-healing action and the generated slice fault action. Specifically, the target self-healing model further comprises a self-healing action judging device and a fault action judging device, and the generated slice self-healing action (a 1 ) The state s of the network slice that can be observed by the self-healing action generator actor1 1 The generated slice failure action (a 2 ) Inputting into a self-healing action judging device (critic 1) to judge the self-healing action of the slice to obtain a self-healing action of the slice 1 Behavior score Q of (2) 1 (s 1 ,a 1 ,a 2 ). Determining a failure radius (c) from the generated slice failure action 2 ) The state s of the network slice which can be observed by the fault action generator actor2 is calculated 2 Failure radius c 2 Action of slicing failure a 2 Slice self-healing action a 1 Inputting into a fault action judging device (critic 2) to judge the slice fault action to obtain a slice fault action a 2 Behavior score Q of (2) 2 (s 2 ,c 2 ,a 2 ,a 1 )。
Further, the refinement of step S30 in the above embodiment further includes steps C1-C2:
Step C1, feeding back the behavior score of the slicing self-healing action to the self-healing action generator, feeding back the behavior score of the slicing fault action to the fault action generator, returning back and executing the step of generating the slicing self-healing action by using the self-healing action generator in the target self-healing model and generating the slicing fault action by using the fault action generator in the target self-healing model so as to optimally update the slicing self-healing action and the slicing fault action until the behavior score of the slicing self-healing action and the behavior score of the slicing fault action meet preset conditions, and obtaining the target self-healing action;
and C2, switching the network slice from the current full information state to the target full information state corresponding to the target self-healing action so as to repair the fault of the network slice.
Further, after determining the behavior scores of the self-healing actions and the fault actions of the slice, determining the target self-healing actions according to the behavior scores, so that when the fault repair is carried out on the network slice, the behavior scores of the self-healing actions and the fault actions of the slice are required to be fed back to the corresponding action generators actor1 and actor2 respectively, the step of generating the self-healing actions of the slice by using the self-healing action generators in the target self-healing model is returned and executed, and the step of generating the fault actions of the slice by using the fault action generators in the target self-healing model is carried out, so that new self-healing actions and fault actions of the slice are generated. And then judging the newly generated self-healing action and fault action of the slice, and continuously optimizing and updating the generated self-healing action and fault action of the slice through a DNQ (Deep Q-Learning) circulating structure to form the countermeasure of the two agents of the actor1 and the actor2 and improve the stability and the convergence of the self-healing model. And determining the target self-healing action until the generated slice self-healing action and slice fault action meet preset conditions, wherein the preset conditions can be the highest behavior score of the slice self-healing action, and can enable the fault generated by the slice fault action to be repaired.
Referring to FIG. 3, FIG. 3 is a schematic diagram showing the countermeasure process of the slice self-healing action and the slice fault action generated by the target self-healing model in the present embodiment, in FIG. 3, the network slice outputs a full information state s all Then, the actor1 and the actor2 can only acquire part of state information which can be observed by themselves, and the critic1 and the critic2 can acquire the full information state, and can also acquire strategy actions adopted by two agents (agents), namely the actor1 and the actor 2. That is, although all the information cannot be obtained by different actors and the policies of other actors cannot be obtained, the judging device corresponding to each actor can observe all the information and instruct the corresponding actor to optimize its policy. Specifically, the behavioural scores derived by the critic1 and critic2 judges, namely the value Q 1 And Q 2 And respectively feeding back to the action generators actor1 and actor2, wherein the action generators actor1 and actor2 determine whether the action generated at present is an optimal strategy according to the determined action strategy corresponding to the current action score, and if not, generating a new action to perform optimization updating on the strategy of the action generator. Wherein, the action generator actor1 and actor2 determine action strategies, namely DPG (Deterministic Policy Gradient, deterministic strategy gradient), so that the action of each step of the action generator actor can directly obtain a determined value through a function mu, and the DPG is shown in the following formula 1:
a t ＝μ(s t |θ μ ) (1)
In formula 1, a t Action selected for time t, s t For the state of the environment at time t, θ μ For the weight value, the function μ is an optimal behavior strategy, training is performed to obtain a deterministic optimal behavior strategy function, and a deterministic strategy gradient formula is shown in the following formula 2:
wherein,,representing the gradient, it is noted that the present inventionThe deterministic strategy in (a) is DDPG (Deep Deterministic Policy Gradient, depth deterministic strategy gradient) based on DPG, and a deep learning neural network is fused with a strategy learning method of DPG, namely, a cost function and a strategy function are expressed through the neural network. Compared with DPG, DDPG adopts a neural network to simulate strategy functions mu and Q, namely a strategy network and a Q network, and then trains the neural network by using a deep learning method to obtain a deterministic strategy gradient of action generated by selection of action generators actor1 and actor2 of the target self-healing model.
Further, the deterministic strategy gradient shown in the above formula 1-2 is only one preferred strategy of the present embodiment, and is used for illustrating the embodiment of the present invention, not limiting the present invention. In this embodiment, the generated slice self-healing action and slice fault action are optimized and updated according to the behavior score obtained by the judging device, and after the target self-healing action is determined, the network slice is switched from the current full-information state to the target full-information state according to the target full-information state of the network slice corresponding to the target self-healing action, so as to complete fault repair of the network slice. Wherein the switching of the network slice full information state is, for example, wireless network sub-slice: switching to a standby traffic channel; transmission net slice: switching to a standby logic port; core network sub-slice: switch to a standby virtual machine, etc.
In the embodiment, the self-healing action and the fault action of the slice are generated by controlling the double-agent, the generated self-healing action and the generated fault action of the slice are judged by utilizing the judging device, the generated self-healing action and the generated fault action of the slice are optimally updated according to the judged action score based on the deterministic strategy gradient, so that the actions of the double-agent of the action generator generate the countermeasure action, the self-healing action generator is forced to generate the self-healing action with higher fault repairing capability, the optimal action strategy, namely the target self-healing action, is further determined, the generalization capability of the self-healing strategy is improved, the state of the network slice is switched according to the determined target self-healing action, and the fault self-healing capability of the network slice is improved.
Based on the above embodiment, a third embodiment of the network slice fault self-healing method of the present invention is provided, where in the above embodiment, the steps after step S30 further include steps D1-D2 after step S30:
step D1, inputting the target self-healing action into a preset rewarding function to obtain a rewarding value corresponding to the target self-healing action;
and D2, generating an experience playback data set according to the reward value, and updating model parameters of the target self-healing model according to the experience playback data set.
Based on the above embodiment, in this embodiment, after performing fault repair on a network slice according to a determined target self-healing action, the target self-healing action is input into a preset reward function, so as to obtain a reward value corresponding to the target self-healing action, an experience playback data set is generated according to the reward value, and model parameters of a target self-healing model are updated by using the generated experience playback data set.
Specifically, the action generator and the evaluator of the target self-healing model both comprise an estimation network and a target network, and the generated empirical playback data such as (s, a, r, s '), where s is the full information state of the network slice before the target self-healing action is switched, s' is the full information state of the network slice after the target self-healing action is switched, a= (a) 1 ，a 2 ) Is the result (s, a, r, s') of the action, the reward, and the next state in each state of the network slice recorded in the generated experience playback data set, based on the action performed by the action 1 and the action 2 in the full information state of the network slice, and r represents the benefit obtained from the environment after the action is performed by the action, namely, the reward value. When the experience playback data set has a certain storage capacity, the first stored data can be covered when the data is full.
When updating model parameters by using experience playback data set, the model parameters are mainly updated to an estimated network, the parameters of a target network can be obtained by tracking the estimated network parameters, and when updating the estimated network, an action generator actor network mu (sθ μ ) And a critic network Q (s, a|θ) Q ) The initialization weight values are respectively theta μ And theta Q Then initializing a target network Q' =q (s, a|θ Q ) And μ' =μ (s|θ μ ) Randomly selecting N pieces of experience playback data from the experience playback data set, and updating parameters of the judging device according to an objective function shown in the following formula 3:
y i ＝r i +γQ'(s i+1 ,μ'(s i+1 |θ μ′ |θ Q′ )) (3)
wherein y is i Representing a target network r i Represents the prize value, theta μ' And theta Q' Representing the target weight value, γ represents the discount factor, and the evaluator parameters are updated by minimizing the loss function shown in the following equation 4:
in equation 4, L is a loss function, and in this embodiment, the Q value is obtained based on the square loss of the real and estimated Q values, the estimated Q value is obtained by inputting the current state s and the motion a output from the motion estimation network into the estimation network, the real Q value is obtained by adding the real prize r and the discount value of the Q value, and the real Q value is obtained by inputting the state s 'at the next time and the motion a' obtained from the motion reality network into the reality network. The evaluator parameters are updated by minimizing the loss function. For the update of the action generator actor, based on the deterministic strategy gradient shown in the above formula 1, the deterministic strategy gradient is used to update the actor network, and the purpose of the actor is to obtain an action with a high Q value as much as possible, so that the loss of the actor can be simply understood as that the larger the obtained feedback Q value is, the smaller the obtained feedback Q value is, the larger the obtained feedback Q value is, and the parameter gradient of the actor is utilized Action gradient combined with judging device>According to the following equation 5Gradient update mode of (a) for an actor network>And updating the parameters of the reactor in a direction more likely to acquire a larger Q value.
Finally, the target network parameters are updated according to the following formula 6 by tracking and learning the parameter update of the estimated network, wherein the formula 6 is a weight update mode of the critic and actor target networks:
θ′ i ←τθ i +(1-τ)θ′ i (6)
in this embodiment, τ is set to a number very close to 1, so that the parameter θ of the target network does not fluctuate greatly, thereby improving the stability of the model.
Before step S20, steps E1 to E3 are further included:
step E1, acquiring historical index data of a network slice and preprocessing the historical index data to obtain sample data;
e2, obtaining model architecture parameters, and establishing a basic self-healing model according to the model architecture parameters;
and E3, performing iterative training on the basic self-healing model by using the sample data to obtain a target self-healing model.
Still further, based on the above embodiment, the model needs to be pre-trained before the slice self-healing action and the slice failure action are generated from the processed target index data. When the model is pre-trained, firstly, historical multidimensional index data of a network slice is obtained and preprocessed to obtain model pre-trained sample data, then, a strategy function, an evaluation function and model architecture parameters are obtained, a basic self-healing model is built, and then, the sample data is utilized to carry out iterative training on the basic model to obtain a target self-healing model. The architecture parameters of the model comprise the hierarchical number of the neural network of the model, the number of neurons arranged in each layer of the neural network and the like.
Based on the target self-healing model in the above embodiment, the basic self-healing model built in the present embodiment is a dual-agent DDPG model, and includes two action generators (actors) and two critics (critic), where each actor or critic includes two neural networks with the same structure, i.e., a target network (target_net) and an estimation network (eval_net), and only the parameter update frequencies are different. Taking the above actor1 and critic1 as examples, the built basic self-healing model is shown in fig. 4, fig. 4 is a schematic diagram of a neural network hierarchy of a self-healing action generator and a self-healing action judging device of the built basic self-healing model to be trained, and in fig. 4, an input layer of actor1 is used for inputting a state s of a current slicing network 1 The hidden layer contains 3 fully connected layers (Dense), with 256 and 128 neurons set up, respectively. A rejection layer is introduced after each fully connected layer to effectively avoid model overfitting, where the rejection layer rejects neurons with probability p and leaves other neurons with probability q=1-p, in this embodiment the rejection probability p=0.2 is set, i.e. 20% of neurons are randomly ignored, rendering them ineffective. The output layer is the full connection layer (Dense): setting 3 neurons, and outputting self-healing actions of network subslice, including wireless network subslice (switching to standby service channels), transmission network subslice (switching to standby logic ports) and core network subslice (switching to standby virtual machines).
The critic1 of the judging device is respectively provided with two input layers, one input layer 1 is used for receiving the full information state of the network slice in the latest T time period, the other input layer 2 is used for receiving the generated slice self-healing action and the slice fault action, two full connection layers (Dense) are respectively arranged below the input layer 1, 256 and 128 neurons are respectively arranged below the input layer 1, 1 full connection layer (Dense) is arranged below the input layer 2, 16 neurons are arranged, then the actions and the states are combined through a combining layer (merge), and finally, a full connection layer containing 128 neurons and an output layer containing only 1 neuron are arranged for finally outputting and evaluating Q selected by the action 1 (s 1 ,a 1 ,a 2 ) Values.
At the neural network hierarchy level of the built basic self-healing modelAfter construction, training the built and self-healing models by using the acquired sample data, and respectively training two intelligent agent actors during training, specifically, performing multi-dimensional KPI(s) on historical network slices including wireless access network sub-slices, transmission network sub-slices and core network sub-slices 1 ) Inputting the self-healing actions into an actor1 formed by a fully-connected neural network, and outputting the self-healing actions corresponding to the generated network slices. The network slice's status KPI (s 1 ) Generated slice self-healing action (a) 1 ) Action of slicing failure (a) 2 ) Input into a critic1 formed by a multi-branch fully-connected neural network, and output and evaluate the Q value Q selected by the action 1 (s 1 ,a 1 ,a 2 ). Will Q 1 The value is fed back to actor1, and actor1 is according to Q 1 The self-healing action capable of minimizing the business influence to the greatest extent is selected by the value, and the actor1 model weight after convergence is trained to be used as a slicing self-healing action generator.
Historical network slice multidimensional KPIs(s) including radio access network sub-slices, transport network sub-slices, and core network sub-slice KPIs are then processed 2 ) Failure radius (c) 2 ) Input to an actor2 composed of a fully connected neural network, and output the generated slice fault generation operation (a 2 ). Network slice status KPI(s) 2 ) Failure radius (c) 2 ) Generated slice self-healing action (a) 1 ) Action of slicing failure (a) 2 ) Input into a critic2 consisting of a multi-branch fully-connected neural network, and output and evaluate the Q value Q selected by the action 2 (s 2 ,c 2 a 2 ,a 1 ). Will Q 2 The value is fed back to the action generator, and actor2 is used for controlling the action according to Q 2 The value is used for selecting the fault action of the slice which can make the slice difficult to self-heal to the greatest extent under a certain fault breaking radius. And training the converged actor2 model weight to be used as a slice fault action generator. In the model pre-training process, the experience playback data (s, a, r, s') of all stages in the training process are saved to form an experience playback data set, a basic data set for parameter updating in the model operation process is formed, and the sample data is utilized to complete the self-building of the foundation And (5) performing iterative training on the healing model to obtain the target self-healing model.
In the embodiment, the built basic self-healing model is subjected to iterative training by utilizing the historical multi-dimensional index data of the network slice to obtain the target self-healing model, and after the fault repair of the network slice is completed according to the target self-healing action, an experience playback data set is generated by utilizing the target self-healing action, so that model parameters are updated, and the self-healing performance of the target self-healing model is improved.
In addition, referring to fig. 5, an embodiment of the present invention further provides a network slice fault self-healing device, where the network slice fault self-healing device includes:
the data detection module 10 is used for acquiring the multi-dimensional index data of the network slice and detecting the acquired multi-dimensional index data;
the action generating module 20 is configured to generate a slice self-healing action and a slice fault action according to the multi-dimensional index data when abnormal data exists in the multi-dimensional index data of the network slice, and judge the generated slice self-healing action and slice fault action to determine a behavior score of the slice self-healing action and the slice fault action;
the dual-agent countermeasure module 30 is configured to iteratively optimize the generated slice self-healing action and slice fault action according to the behavior score, so as to determine a target self-healing action, and perform fault repair on the network slice by using the target self-healing action.
Optionally, the action generating module 20 is further configured to:
inputting the multi-dimensional index data into a preset target self-healing model, wherein the target self-healing model is obtained by performing iterative training on the preset self-healing model to be trained by utilizing the historical multi-dimensional index data of a network slice, and the target self-healing model comprises an action generator;
and generating a slice self-healing action and a slice fault action by using an action generator of the target self-healing model according to the target index data.
Optionally, the action generating module 20 is further configured to:
determining a current full information state of the network slice according to the multi-dimensional index data, wherein the full information state comprises a first state observable by the self-healing action generator and a second state observable by the fault action generator;
inputting a first state in the full information states into the self-healing action generator, and generating a slicing self-healing action by using the self-healing action generator;
and inputting a second state in the all-information states into the fault action generator, and generating a slicing fault action by using the fault action generator.
Optionally, the action generating module 20 is further configured to:
Inputting a first state in the full information state, the slice self-healing action and the slice fault action into a self-healing action judging device in the target self-healing model so as to judge the slice self-healing action and determine a behavior score of the slice self-healing action;
determining a fault destruction radius according to the slice fault action;
and inputting a second state in the full information state, the fault destruction radius, the slice self-healing action and the slice fault action into a fault action judging device in the target self-healing model so as to judge the slice fault action and determine a behavior score of the slice fault action.
Optionally, the dual agent countermeasure module 30 is further configured to:
feeding back the behavior score of the slice self-healing action to the self-healing action generator, feeding back the behavior score of the slice fault action to the fault action generator, returning to and executing the step of generating the slice self-healing action by using the self-healing action generator in the target self-healing model and generating the slice fault action by using the fault action generator in the target self-healing model so as to optimally update the slice self-healing action and the slice fault action until the behavior score of the slice self-healing action and the behavior score of the slice fault action meet preset conditions, thereby obtaining the target self-healing action;
And switching the network slice from the current full information state to the target full information state corresponding to the target self-healing action so as to repair the fault of the network slice.
Optionally, the network slice fault self-healing device further includes a self-healing policy updating module, configured to:
inputting the target self-healing action into a preset rewarding function to obtain a rewarding value corresponding to the target self-healing action;
and generating an experience playback data set according to the reward value, and updating model parameters of the target self-healing model according to the experience playback data set.
Optionally, the network slice fault self-healing device further comprises a model training module for:
acquiring historical index data of a network slice and preprocessing the historical index data to obtain sample data;
obtaining model architecture parameters, and establishing a basic self-healing model according to the model architecture parameters;
and carrying out iterative training on the basic self-healing model by using the sample data to obtain a target self-healing model.
Furthermore, an embodiment of the present invention also proposes a computer program product, which includes a computer program, where the computer program when executed by a processor implements the operations in the network slice fault self-healing method provided in the foregoing embodiment.
Embodiments of the apparatus and computer program product of the present invention may refer to embodiments of the network slice fault self-healing method of the present invention, and will not be described herein.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity/operation/object from another entity/operation/object without necessarily requiring or implying any actual such relationship or order between such entities/operations/objects; the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The apparatus embodiments described above are merely illustrative, in which the units illustrated as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the objectives of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above, including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the network slice fault self-healing method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (9)

1. The network slice fault self-healing method is characterized by comprising the following steps of:
acquiring multi-dimensional index data of the network slice, and detecting the acquired multi-dimensional index data ；
When abnormal data are detected to exist in the multi-dimensional index data of the network slice, generating a slice self-healing action and a slice fault action according to the multi-dimensional index data, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action;
the step of generating a slice self-healing action and a slice fault action according to the multidimensional index data comprises the following steps:
inputting the multi-dimensional index data into a preset target self-healing model, wherein the target self-healing model is obtained by performing iterative training on the preset self-healing model to be trained by utilizing the historical multi-dimensional index data of a network slice, and the target self-healing model comprises an action generator;
generating a slicing self-healing action and a slicing fault action by using an action generator of the target self-healing model according to the multidimensional index data;
and performing iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score to determine a target self-healing action, and performing fault repair on the network slice by using the target self-healing action.
2. The network slice fault self-healing method of claim 1, wherein the action generator comprises a self-healing action generator and a fault action generator, the step of generating slice self-healing actions and slice fault actions using the action generator of the target self-healing model according to the multi-dimensional index data comprising:
determining a current full information state of the network slice according to the multi-dimensional index data, wherein the full information state comprises a first state observable by the self-healing action generator and a second state observable by the fault action generator;
inputting a first state in the full information states into the self-healing action generator, and generating a slicing self-healing action by using the self-healing action generator;
and inputting a second state in the all-information states into the fault action generator, and generating a slicing fault action by using the fault action generator.
3. The network slice fault self-healing method of claim 2, wherein the target self-healing model further comprises a self-healing action evaluator and a fault action evaluator, the step of evaluating the generated slice self-healing action and slice fault action to determine a behavioral score of the slice self-healing action and the slice fault action comprising:
Inputting a first state in the full information state, the slice self-healing action and the slice fault action into a self-healing action judging device in the target self-healing model so as to judge the slice self-healing action and determine a behavior score of the slice self-healing action;
determining a fault destruction radius according to the slice fault action;
and inputting a second state in the full information state, the fault destruction radius, the slice self-healing action and the slice fault action into a fault action judging device in the target self-healing model so as to judge the slice fault action and determine a behavior score of the slice fault action.
4. The network slice fault self-healing method of claim 3, wherein the step of iteratively optimizing the generated slice self-healing actions and slice fault actions according to the behavioral scores to determine a target self-healing action and utilizing the target self-healing action to perform fault remediation on the network slice comprises:
feeding back the behavior score of the slice self-healing action to the self-healing action generator, feeding back the behavior score of the slice fault action to the fault action generator, returning to and executing the step of generating the slice self-healing action by using the self-healing action generator in the target self-healing model and generating the slice fault action by using the fault action generator in the target self-healing model, so as to perform iterative optimization on the slice self-healing action and the slice fault action until the behavior score of the slice self-healing action and the behavior score of the slice fault action meet preset conditions, and obtaining the target self-healing action;
And switching the state of the network slice according to the target self-healing action so as to switch the network slice from the current full-information state to the target full-information state corresponding to the target self-healing action, and repairing the fault of the network slice.
5. The network slice fault self-healing method of claim 4, wherein the step of iteratively optimizing the generated slice self-healing actions and slice fault actions according to the behavioral scores to determine a target self-healing action and utilizing the target self-healing action to perform fault remediation on the network slice comprises:
inputting the target self-healing action into a preset rewarding function to obtain a rewarding value corresponding to the target self-healing action;
and generating an experience playback data set according to the reward value, and updating model parameters of the target self-healing model according to the experience playback data set.
6. The network slice fault self-healing method of claim 1, wherein prior to the step of generating slice self-healing actions and slice fault actions from the multi-dimensional index data, further comprising:
acquiring historical index data of a network slice and preprocessing the historical index data to obtain sample data;
Obtaining model architecture parameters, and establishing a basic self-healing model according to the model architecture parameters;
and carrying out iterative training on the basic self-healing model by using the sample data to obtain a target self-healing model.
7. A network slice fault self-healing device, characterized in that the network slice fault self-healing device comprises:
the data detection module is used for acquiring the multi-dimensional index data of the network slice and detecting the acquired multi-dimensional index data;
the action generating module is used for generating a slice self-healing action and a slice fault action according to the multi-dimensional index data when abnormal data exist in the multi-dimensional index data of the network slice, and judging the generated slice self-healing action and slice fault action to determine the action scores of the slice self-healing action and the slice fault action; inputting the multi-dimensional index data into a preset target self-healing model, wherein the target self-healing model is obtained by performing iterative training on the preset self-healing model to be trained by utilizing the historical multi-dimensional index data of a network slice, and the target self-healing model comprises an action generator; generating a slicing self-healing action and a slicing fault action by using an action generator of the target self-healing model according to the multidimensional index data;
And the dual-agent countermeasure module is used for carrying out iterative optimization on the generated slice self-healing action and slice fault action according to the behavior score so as to determine a target self-healing action and carrying out fault repair on the network slice by utilizing the target self-healing action.
8. A network slice fault self-healing device, the network slice fault self-healing device comprising: memory, a processor and a network slice fault self-healing program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the network slice fault self-healing method according to any one of claims 1 to 6.
9. A storage medium having stored thereon a computer program which, when executed by a processor, implements the network slice fault self-healing method according to any one of claims 1-6.
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