CN111835548B - Artificial intelligence model processing method and device in O-RAN system - Google Patents
Artificial intelligence model processing method and device in O-RAN system Download PDFInfo
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- CN111835548B CN111835548B CN202010135512.4A CN202010135512A CN111835548B CN 111835548 B CN111835548 B CN 111835548B CN 202010135512 A CN202010135512 A CN 202010135512A CN 111835548 B CN111835548 B CN 111835548B
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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
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The application discloses a method and a device for processing an artificial intelligence model in an O-RAN system, wherein the method comprises the following steps: the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit; the real-time intelligent processing unit evaluates the performance of the artificial intelligent model and feeds back a request for processing the artificial intelligent model to the non-real-time intelligent processing unit based on a threshold configured by the non-real-time intelligent processing unit. Wherein the request comprises: the deployed artificial intelligence model works normally and requests to continue using the model; requesting to update and deploy a new artificial intelligence model when the deployed artificial intelligence model is in a poor state; an artificial intelligence model that has been deployed needs to be stopped, requesting a fallback to a traditional state where no artificial intelligence model is applied.
Description
Technical Field
The invention relates to a data processing technology, in particular to a method and a device for processing an artificial intelligence model in an O-RAN system.
Background
The O-RAN is a generic term for an open radio access network, which means that open, intelligent radio access devices are designed and developed. In an O-RAN system, there are mainly 2 modules that can embody the intelligentization function: Non-Real Time Radio Intelligent Controller (Non-Real Time RIC Controller, Non-RT RIC; also called Non-Real Time RIC), and Near Real Time Radio Intelligent Controller (Near-RT RIC; also called Near Real Time RIC).
The non-real-time RIC has the following functions:
1. training a machine learning model;
2. micro-services and policy management;
3. and analyzing the performance of the wireless network.
The near real-time RIC has the following functions:
1. machine learning model reasoning;
2. configuring wireless resources;
3. wireless side performance data collection.
Currently, the interaction between non-real-time RIC and near-real-time RIC and the related feedback decision mechanism are under study, and there is no relevant published technical data.
Disclosure of Invention
In view of the above, the present invention provides the following technical solutions.
1. An artificial intelligence model processing method in an O-RAN system, which is applied to an intelligent processing unit in the O-RAN system, and is characterized by comprising the following steps:
the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit;
the real-time intelligent processing unit evaluates the performance of the artificial intelligent model;
based on the threshold configured by the non-real-time intelligent processing unit, the real-time intelligent processing unit determines the application state of the artificial intelligent model;
the real-time intelligent processing unit feeds back the application state of the artificial intelligent model to the non-real-time intelligent processing unit.
2. The method of claim 1, wherein the state of the artificial intelligence model application comprises:
continuing to apply the deployed artificial intelligence model, updating the deployed artificial intelligence model, terminating the deployed artificial intelligence model, and employing conventional data processing methods.
3. The conventional data processing method according to claim 2, wherein:
and processing data by adopting a non-artificial intelligence model method.
4. The method of claim 1, wherein the intelligence processing unit is a unit for processing data using artificial intelligence techniques, and comprises an O-RAN defined non-real time RIC and a near real time RIC.
5. The method of claim 1, wherein the non-real-time intelligent processing unit determines whether to terminate the deployment and update of the artificial intelligence model to the real-time intelligent processing unit, and signals the instruction.
6. The signaling of claim 5, at least comprising terminating the deployment of the artificial intelligence model to the real-time intelligent processing unit, terminating the updating of the artificial intelligence model information.
7. An artificial intelligence model processing device in an O-RAN system, which is applied to an intelligent processing unit in the O-RAN system, and is characterized by comprising the following steps:
the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit;
the real-time intelligent processing unit evaluates the performance of the artificial intelligent model;
based on the threshold configured by the non-real-time intelligent processing unit, the real-time intelligent processing unit determines the application state of the artificial intelligent model;
the real-time intelligent processing unit feeds back the application state of the artificial intelligent model to the non-real-time intelligent processing unit.
8. The apparatus of claim 7, wherein the state of the artificial intelligence model application comprises:
continuing to apply the deployed artificial intelligence model, updating the deployed artificial intelligence model, terminating the deployed artificial intelligence model, and employing conventional data processing methods.
9. The conventional data processing method of claim 8, wherein:
and processing data by adopting a non-artificial intelligence model method.
10. The artificial intelligence model processing apparatus in an O-RAN system of claim 7, wherein the intelligence processing unit is a unit for processing data using artificial intelligence techniques, and comprises an O-RAN defined non-real time RIC and a near-real time RIC.
11. The apparatus of claim 7, wherein the non-real-time intelligent processing unit determines whether to terminate the deployment and update of the artificial intelligence model to the real-time intelligent processing unit, and signals the instruction.
12. The signaling of claim 11, at least comprising terminating deployment of the artificial intelligence model to the real-time intelligent processing unit, terminating updating of the artificial intelligence model information.
Through the technical scheme, compared with the prior art, the embodiment of the invention discloses an artificial intelligence model processing method and device in an O-RAN system, and the method comprises the following steps: the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit; the real-time intelligent processing unit evaluates the performance of the artificial intelligent model, processes the artificial intelligent model based on the threshold configured by the non-real-time intelligent processing unit, and sends a state feedback signaling to the non-real-time intelligent processing unit. The signaling comprises the following steps: if the real-time intelligent processing unit judges that the current artificial intelligence model operates normally, the real-time intelligent processing unit requests to continue using the deployed artificial intelligence model; if the real-time intelligent processing unit judges that the deployed artificial intelligence model generally operates, the real-time intelligent processing unit requests to update and redeploy the artificial intelligence model; and if the real-time intelligent processing unit judges that the deployed artificial intelligent model has poor operability, the real-time intelligent processing unit requests to stop using the artificial intelligent model and returns to the traditional data processing state. In addition, the real-time intelligent processing unit also comprises a near real-time intelligent processing unit, for example, the intelligent processing unit for processing 10ms delay service is classified as a real-time intelligent processing unit; the intelligent processing unit processes the 15ms time delay service and is classified as a near real-time intelligent processing unit; the intelligent processing unit for processing the delay service of 100ms or more is classified as a non-real-time intelligent processing unit. The above division of the real-time intelligent processing unit/near real-time intelligent processing unit and the non-real-time intelligent processing unit is only an example, and can be flexibly completed in different ways according to standard definition, product implementation, signaling configuration and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for processing an artificial intelligence model in an O-RAN system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an architecture diagram and a fallback to conventional mode of artificial intelligence model processing disclosed in an embodiment of the present invention;
FIG. 3 is a schematic diagram of an architecture diagram of an artificial intelligence model process and a request for updating an artificial intelligence model as disclosed in an embodiment of the invention;
FIG. 4 is a schematic diagram of an architecture diagram of an artificial intelligence model process and a continued application of an artificial intelligence model that has been deployed as disclosed in an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an artificial intelligence model processing apparatus in an O-RAN system (applied to near real-time RIC) according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an artificial intelligence model processing apparatus in an O-RAN system (applied to non-real-time RIC) according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating a method for processing an artificial intelligence model in an O-RAN system according to an embodiment of the present invention, where the method illustrated in fig. 1 is applied to a wireless intelligence control unit, such as an RIC, in the O-RAN system. Referring to fig. 1, the method may include:
step 101: the real-time intelligent processing unit evaluates the performance of the artificial intelligent model;
the intelligent processing unit may be an existing intelligent unit in the O-RAN system or an intelligent unit that may appear in the future, and the intelligent unit may be a unit that processes data in real time by using an artificial Intelligence technique, and a unit that has an intelligent function and is defined in the O-RAN system, such as RIC (Radio intelligent Controller, etc.);
step 102: based on a threshold configured by the non-real-time intelligent processing unit, the real-time intelligent processing unit determines whether to return to a state without applying the artificial intelligent model;
the judgment of the rollback state may be based on collected network performance data;
step 103: the real-time intelligent processing unit feeds back the application state of the artificial intelligent model to the non-real-time intelligent processing unit;
the feedback of the application state of the artificial intelligence model comprises the steps of continuously applying the deployed artificial intelligence model, requiring to update the deployed artificial intelligence model, terminating the deployed artificial intelligence model and adopting a traditional data processing method;
step 104: the non-real-time intelligent processing unit decides whether to terminate the deployment and update of the artificial intelligence model to the real-time intelligent processing unit.
In the artificial intelligence model processing method in the O-RAN system according to this embodiment, a non-real-time intelligent processing unit configures a threshold of an artificial intelligence model processing policy to a real-time intelligent processing unit; the real-time intelligent processing unit evaluates the performance of the artificial intelligence model and, based on a threshold configured by the non-real-time intelligent processing unit, requests the non-real-time intelligent processing unit to continue using the deployed artificial intelligence model, updates the deployed artificial intelligence model, terminates using the artificial intelligence model, and reverts to a conventional state in which the artificial intelligence model is not used. Through the operation, performance degradation or system crash caused by applying an artificial intelligence technology can be avoided, and normal operation of the O-RAN system is ensured. In the above embodiments, the near-real-time RIC/the non-real-time RIC is taken as an example of the real-time intelligent processing unit/the non-real-time intelligent processing unit, and the near-real-time RIC performs a corresponding action according to the performance of the artificial intelligence model, for example, if the performance of the artificial intelligence model is better, the near-real-time RIC continues to execute the model and notifies the non-real-time RIC; if the performance of the artificial intelligence model is poor, e.g., throughput is less than a threshold X1 (X1 > X2), the near real-time RIC will continue to execute the model, but at the same time will request the non-real-time RIC to update the artificial intelligence model; if the performance of the artificial intelligence model is very poor, e.g., the throughput is less than the threshold X2, the near real-time RIC may stop executing the artificial intelligence model, fall back to the conventional mode in which the artificial intelligence model is not applied, and may notify the non-real-time RIC to stop updating the artificial intelligence model. Until the non-real-time RIC sends a new instruction (with higher priority) requiring the application of the artificial intelligence model to be restarted, the near-real-time RIC receives the relevant artificial intelligence model and performs a new operation. Wherein X1, X2 are configured by the non-real time RIC in the form of signaling to the near-real time RIC.
Fig. 2 is an architecture diagram of the artificial intelligence model processing and an embodiment of the artificial intelligence model processing to revert to the conventional mode, which is disclosed in the embodiment of the present invention, and in conjunction with fig. 2, the near real-time RIC collects data of a wireless communication unit, such as CU/DU/RU (centralized processing unit/distributed processing unit/radio frequency unit), and then determines the performance of the artificial intelligence model by analyzing the wireless network performance. In this embodiment, the near real-time RIC evaluates that the artificial intelligence model performs poorly (e.g., network throughput < X2 = 100M bps), terminates the artificial intelligence model application, and reverts to the traditional data processing mode (e.g., based on traditional resource allocation methods or default resource allocation parameters). The near real-time RIC feeds back the decision to the non-real-time RIC, which then confirms to the near real-time RIC that application of the artificial intelligence model is terminated. Alternatively, the near real-time RIC may request a non-real-time RIC before terminating application of the artificial intelligence model. Near real-time RIC does not terminate application of the artificial intelligence model until non-real-time RIC allows termination of application of the artificial intelligence model. Wherein the values of X1, X2 are configured by the non-real time RIC in the form of signaling to the near-real time RIC.
FIG. 3 is an architectural diagram of an artificial intelligence model process and an embodiment of a request to update an artificial intelligence model as disclosed in embodiments of the present invention. The near real-time RIC collects data of wireless communication units such as CUs/DUs/RUs (centralized processing unit/distributed processing unit/radio frequency unit), and then judges the performance of the artificial intelligence model by analyzing the wireless network performance. In this embodiment, the near real-time RIC evaluates that the artificial intelligence model is performing poorly (e.g., network throughput < X1 = 500M bps), and then requests the non-real-time intelligent processing unit to update the artificial intelligence model. And after receiving the request, the non-real-time RIC updates the model by using the acquired wireless data, and then deploys the updated model to the near-real-time RIC. Wherein the values of X1, X2 are configured by the non-real time RIC in the form of signaling to the near-real time RIC.
FIG. 4 is an architectural diagram of an artificial intelligence model process and an embodiment of continuing to apply an artificial intelligence model that has been deployed, as disclosed by embodiments of the present invention. The near real-time RIC collects data of wireless communication units such as CUs/DUs/RUs (centralized processing unit/distributed processing unit/radio frequency unit), and then judges the performance of the artificial intelligence model by analyzing the wireless network performance. In this embodiment, the near-real-time RIC evaluates that the artificial intelligence model performs better (e.g., network throughput = 1000M bps > X1), and then notifies the non-real-time RIC to continue applying the artificial intelligence model already deployed. And after receiving the notification, the non-real-time RIC sends an acknowledgement to the near-real-time RIC. Wherein the values of X1, X2 are configured by the non-real time RIC in the form of signaling to the near-real time RIC.
While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present invention is not limited by the illustrated ordering of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
The method is described in detail in the embodiments disclosed above, and the method of the present invention can be implemented by various types of apparatuses, so that the present invention also discloses an apparatus, and the following detailed description will be given of specific embodiments.
FIG. 5 is a schematic structural diagram of an artificial intelligence model processing apparatus in an O-RAN system according to an embodiment of the present invention, which can be applied to a near real-time RIC in the O-RAN system;
referring to fig. 5, an artificial intelligence model processing apparatus 50 in an O-RAN system may include:
a data receiving module 501, configured to receive data or signaling from different wireless communication units and non-real-time intelligent control units;
an information obtaining module 502, configured to obtain wireless network performance and signaling information based on the data collected by the data receiving module 501;
a data determining module 503, configured to determine, based on the wireless network performance, the performance of the artificial intelligence model, and determine status signaling content and related operations to be sent to the non-real-time intelligent control unit;
an operation executing module 504, configured to execute a corresponding operation according to the determination of the module 503.
FIG. 6 is a schematic structural diagram of an artificial intelligence model processing apparatus in an O-RAN system according to an embodiment of the present invention, which can be applied to a non-real-time RIC in the O-RAN system;
referring to fig. 6, an artificial intelligence model processing apparatus 60 in an O-RAN system may include:
the data receiving module 601 is configured to receive data or signaling from different wireless communication units and near real-time intelligent control units;
an information obtaining module 602, configured to obtain wireless network performance and signaling information based on the data collected by the data receiving module 601;
a data determining module 603, configured to determine, based on the wireless network performance and information sent by the near-real-time intelligent control unit, status signaling content and related operations sent to the near-real-time intelligent processing unit;
an operation executing module 604, configured to execute a corresponding operation according to the determination of the module 603.
The method and the device for processing the artificial intelligence model in the O-RAN system in the embodiment comprise the following steps: the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit; the real-time intelligent processing unit evaluates the performance of the artificial intelligent model and feeds back a request for processing the artificial intelligent model to the non-real-time intelligent processing unit based on a threshold configured by the non-real-time intelligent processing unit. Wherein the request comprises: the deployed artificial intelligence model works normally and requests to continue using the model; requesting to update and deploy a new artificial intelligence model when the deployed artificial intelligence model is in a poor state; an artificial intelligence model that has been deployed needs to be stopped, requesting a fallback to a traditional state where no artificial intelligence model is applied.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An artificial intelligence model processing method in an O-RAN system, which is applied to an intelligent processing unit in the O-RAN system, and is characterized by comprising the following steps:
the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit;
the real-time intelligent processing unit acquires data of the wireless communication unit and evaluates the performance of the artificial intelligent model by analyzing the performance of the wireless network;
based on the threshold configured by the non-real-time intelligent processing unit, the real-time intelligent processing unit determines the application state of the artificial intelligent model;
the real-time intelligent processing unit feeds back the application state of the artificial intelligent model to the non-real-time intelligent processing unit;
the O-RAN system comprises a non-real-time radio access network intelligent controller and a near-real-time radio access network intelligent controller, wherein the O-RAN is an open radio access network.
2. The method of claim 1, wherein the state of the artificial intelligence model application comprises:
continuing to apply the deployed artificial intelligence model, updating the deployed artificial intelligence model, terminating the deployed artificial intelligence model, and employing conventional data processing methods.
3. The method of claim 2, wherein the conventional data processing method comprises:
and processing data by adopting a non-artificial intelligence model method.
4. The method of claim 1, wherein the intelligence processing unit is a unit for processing data using artificial intelligence techniques, and comprises an O-RAN defined non-real time RIC and a near real time RIC.
5. The method of claim 1, wherein the non-real-time intelligent processing unit determines whether to terminate the deployment and update of the artificial intelligence model to the real-time intelligent processing unit, and signals the instruction.
6. The method of claim 5, wherein the signaling the instruction comprises at least terminating the artificial intelligence model deployment to the real-time intelligence processing unit and terminating the artificial intelligence model update.
7. An artificial intelligence model processing device in an O-RAN system, which is applied to an intelligent processing unit in the O-RAN system, and is characterized by comprising the following steps:
the non-real-time intelligent processing unit configures a threshold of an artificial intelligent model processing strategy to the real-time intelligent processing unit;
the real-time intelligent processing unit acquires data of the wireless communication unit and evaluates the performance of the artificial intelligent model by analyzing the performance of the wireless network;
based on the threshold configured by the non-real-time intelligent processing unit, the real-time intelligent processing unit determines the application state of the artificial intelligent model;
the real-time intelligent processing unit feeds back the application state of the artificial intelligent model to the non-real-time intelligent processing unit;
the O-RAN system comprises a non-real-time radio access network intelligent controller and a near-real-time radio access network intelligent controller, wherein the O-RAN is an open radio access network.
8. The apparatus of claim 7, wherein the state of the artificial intelligence model application comprises:
continuing to apply the deployed artificial intelligence model, updating the deployed artificial intelligence model, terminating the deployed artificial intelligence model, and employing conventional data processing methods.
9. The artificial intelligence model processing apparatus in an O-RAN system of claim 8, wherein the conventional data processing method comprises:
and processing data by adopting a non-artificial intelligence model method.
10. The artificial intelligence model processing apparatus in an O-RAN system of claim 7, wherein the intelligence processing unit is a unit for processing data using artificial intelligence techniques, and comprises an O-RAN defined non-real time RIC and a near-real time RIC.
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| CN108667850A (en) * | 2018-05-21 | 2018-10-16 | 济南浪潮高新科技投资发展有限公司 | A kind of artificial intelligence service system and its method for realizing artificial intelligence service |
| CN109710383A (en) * | 2018-12-29 | 2019-05-03 | 上海晏鼠计算机技术股份有限公司 | A kind of method of intelligent algorithm containerization application |
| WO2019217391A1 (en) * | 2018-05-07 | 2019-11-14 | Mavenir Networks, Inc. | Method and apparatus for fronthaul compression in cloud ran |
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| WO2019217391A1 (en) * | 2018-05-07 | 2019-11-14 | Mavenir Networks, Inc. | Method and apparatus for fronthaul compression in cloud ran |
| CN108667850A (en) * | 2018-05-21 | 2018-10-16 | 济南浪潮高新科技投资发展有限公司 | A kind of artificial intelligence service system and its method for realizing artificial intelligence service |
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| CF01 | Termination of patent right due to non-payment of annual fee |