WO2022000379A1 - 设备切换方法、装置、设备及可读存储介质 - Google Patents

设备切换方法、装置、设备及可读存储介质 Download PDF

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Publication number
WO2022000379A1
WO2022000379A1 PCT/CN2020/099773 CN2020099773W WO2022000379A1 WO 2022000379 A1 WO2022000379 A1 WO 2022000379A1 CN 2020099773 W CN2020099773 W CN 2020099773W WO 2022000379 A1 WO2022000379 A1 WO 2022000379A1
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WIPO (PCT)
Prior art keywords
access network
network device
computing power
power factor
handover
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PCT/CN2020/099773
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English (en)
French (fr)
Inventor
周珏嘉
Original Assignee
北京小米移动软件有限公司
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Publication date
Application filed by 北京小米移动软件有限公司 filed Critical 北京小米移动软件有限公司
Priority to CN202080001451.0A priority Critical patent/CN114128349A/zh
Priority to PCT/CN2020/099773 priority patent/WO2022000379A1/zh
Priority to EP20943156.8A priority patent/EP4178256A4/en
Priority to US18/013,193 priority patent/US20230239756A1/en
Publication of WO2022000379A1 publication Critical patent/WO2022000379A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00835—Determination of neighbour cell lists
    • H04W36/008357—Determination of target cell based on access point [AP] properties, e.g. AP service capabilities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • H04W36/302—Reselection being triggered by specific parameters by measured or perceived connection quality data due to low signal strength

Definitions

  • the present disclosure relates to the field of communications, and in particular, to a device switching method, apparatus, device, and readable storage medium.
  • AI artificial intelligence
  • 6G cellular networks When artificial intelligence (AI) technology is deployed on the basis of 6G cellular networks, the learning ability or computing resources of edge nodes become the key elements in the implementation of AI applications, such as speech recognition, video understanding and other AI applications.
  • UE User Equipment
  • the embodiments of the present disclosure provide a device handover method, apparatus, device, and readable storage medium, which can improve handover accuracy and comprehensiveness of consideration in a base station handover process.
  • the technical solution is as follows:
  • a device switching method which is applied to a terminal device, and the method includes:
  • the device switching situation between the access network devices is determined by using the AI computing power factor value of the access network device as an offset parameter.
  • an apparatus for switching equipment which is applied in terminal equipment, and the apparatus includes:
  • the processing module is configured to use the AI computing power factor value of the access network device as an offset parameter to determine the device switching situation between the access network devices.
  • a terminal comprising:
  • transceiver connected to the processor
  • the processor is configured to load and execute executable instructions to implement the device switching method described in the foregoing embodiments of the present disclosure.
  • a computer-readable storage medium stores at least one instruction, at least one piece of program, code set or instruction set, the above-mentioned at least one instruction, at least one piece of program, code set or instruction set
  • the set is loaded and executed by the processor to implement the device switching method described in the above embodiments of the present disclosure.
  • the AI computing power factor value of the access network device is used as the offset parameter.
  • the original determination process is offset according to the AI computing power factor value.
  • the AI computing power factor value indicates that when the AI computing power is suitable for switching, the offset is performed in the positive offset direction to complete the determination of the device switching.
  • the AI computing power factor indicates that the AI computing power is not suitable for switching, the reverse direction is used. Offset to the offset direction to complete the determination of device switching, which improves the accuracy in the process of device switching.
  • FIG. 1 is a block diagram of a communication system provided by an exemplary embodiment of the present disclosure
  • FIG. 2 is a schematic diagram of an allocation method of an AI computing power value provided by an exemplary embodiment of the present disclosure
  • FIG. 3 is a schematic diagram of an allocation method of AI computing power value provided by another exemplary embodiment of the present disclosure.
  • FIG. 4 is a schematic diagram of an allocation method of an AI computing power value provided by another exemplary embodiment of the present disclosure.
  • FIG. 5 is a flowchart of a device switching method provided by an exemplary embodiment of the present disclosure.
  • FIG. 6 is a flowchart of a device switching method provided by another exemplary embodiment of the present disclosure.
  • FIG. 7 is a structural block diagram of a device switching apparatus provided by an exemplary embodiment of the present disclosure.
  • FIG. 8 is a block diagram of a terminal provided by an exemplary embodiment of the present disclosure.
  • FIG. 9 is a block diagram of an access network device provided by an exemplary embodiment of the present disclosure.
  • FIG. 1 shows a block diagram of a communication system provided by an exemplary embodiment of the present disclosure.
  • the communication system includes a core network 11 , an access network 12 and a terminal 13 .
  • the core network 11 includes several core network devices 110 .
  • the core network device 110 includes an access and mobility management function (Access and Mobility Management Function, AMF), a session management function (Session Management Function, SMF), and a user plane management function (User Plane Function, UPF) and other equipment, wherein the AMF uses
  • AMF Access and Mobility Management Function
  • SMF Session Management Function
  • UPF User Plane Function
  • the access network 12 includes several access network devices 120 .
  • the access network device 120 is a base station, which is a device deployed in an access network to provide a wireless communication function for a terminal.
  • the base station includes various forms of macro base station, micro base station, relay station, access point and so on.
  • the names of devices with base station functions may be different.
  • LTE Long Term Evolution
  • eNodeB eNodeB
  • gNode B 5G new air interface
  • the name "base station” may be descriptive and will change.
  • the foregoing apparatuses for providing a wireless communication function for a terminal are collectively referred to as access network equipment.
  • the access network device 120 involved in the embodiment of the present disclosure has edge computing capability.
  • the terminal 13 includes various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to the wireless modem, as well as various forms of terminals (User Equipment, UE), mobile stations (Mobile Stations) , MS), terminal device, etc.
  • terminals User Equipment, UE
  • mobile stations Mobile Stations
  • terminal device etc.
  • the access network device 120 and the terminal 13 communicate with each other through a certain air interface technology, such as a Uu interface.
  • Edge computing refers to a computing technology that sets up edge nodes on the edge of the network near the terminal or data source, integrates network, computing, storage, and application core capabilities, and provides edge intelligent services nearby.
  • the network edge refers to a server located at the edge of the network and the corresponding hardware device, which are different from the central server and corresponding hardware device of a network.
  • the network edge may be implemented as a base station gNB.
  • the AI computing power of the edge node is used as the offset factor of mobility management, for example:
  • the AI computing power of the base station or upper management network element, such as the key control node (Mobility Management Entity, MME) is used as the base station handover
  • MME Mobility Management Entity
  • the offset factor makes the UE switch to the base station with more available AI computing power without affecting the connectivity.
  • the AI computing power of the edge node is used to indicate the computing capability of the edge node, wherein the computing capability includes the memory occupancy, central processing unit (Central Processing Unit, CPU) occupancy or storage occupancy of the edge node, etc. , or an index generated for the purpose of AI computing and used to indicate the AI computing capability, which is not limited in this embodiment of the present disclosure.
  • the computing capability includes the memory occupancy, central processing unit (Central Processing Unit, CPU) occupancy or storage occupancy of the edge node, etc.
  • an index generated for the purpose of AI computing and used to indicate the AI computing capability which is not limited in this embodiment of the present disclosure.
  • the level of AI computing power deployment on the edge side can be the base station side, that is, the AI computing power of the base station is used as the offset factor for mobility management, or it can be the upper-layer management network elements above the base station, such as MME. Wait.
  • the AI computing power factor is directly marked in the base station;
  • the base station managed by the upper-layer management network element can inherit the The upper layer manages the AI computing power of the network element, wherein the inheritance method includes at least one of the following methods according to the resource allocation method between the base stations:
  • the AI computing power of the subordinate base station is consistent with the AI computing power of the upper management network element
  • the AI computing power of the upper management network element 210 is u
  • the AI computing power of the edge node (base station) 221 is u
  • the AI computing power of the edge node (base station) 222 is also u.
  • the subordinate base station divides and uses the time-frequency resources.
  • the AI computing power of the upper management network element is evenly distributed to the subordinate base stations
  • the AI computing power of the upper management network element 310 is u
  • the AI computing power of the edge node (base station) 321 is u/2
  • the AI computing power of the edge node (base station) 322 is also u/ 2.
  • the AI computing power of the upper-layer management network element is allocated to the subordinate base stations according to the preset allocation mechanism.
  • the upper-layer management network element allocates AI computing power according to the service deployment density of the subordinate base stations.
  • the above-mentioned AI computing power refers to the maximum AI computing power of the base station or upper-layer management network element; or, the above-mentioned AI computing power refers to the maximum available AI computing power of the base station or upper-layer management network element within a certain time interval. That is, the remaining AI computing power.
  • the upper management network element updates the AI computing power factor value to the edge node (that is, the base station) through the transmission interface between the edge nodes, such as: updating the AI computing power to the base station through S1 signaling.
  • FIG. 5 is a flow chart of a device switching method provided by an exemplary embodiment of the present disclosure. The method is applied to a terminal device as an example for description. As shown in FIG. 5 , the method includes:
  • Step 501 using the AI computing power factor value of the access network device as an offset parameter, to determine the device switching situation between the access network devices.
  • the AI computing power factor value as the offset parameter is the first AI computing power factor value of the source access network device; or, the AI computing power factor value as the offset parameter is the second AI computing power factor value of the target access network device.
  • AI computing power factor value; or, the AI computing power factor value as the offset parameter is the computing power factor difference between the second AI computing power factor value and the first AI computing power factor value.
  • the computing power factor difference value is a difference obtained by subtracting the absolute value of the second AI computing power factor value from the absolute value of the first AI computing power factor value.
  • the terminal receives downlink signaling sent by the access network device, and the downlink signaling includes the AI computing power of the access network device.
  • the AI computing power factor value is the AI computing power of the access network device; or, the AI computing power of the access network device is converted according to a preset conversion method to obtain the AI computing power factor value, wherein the conversion method includes: At least one of linear transformation, exponential transformation, and logarithmic transformation.
  • the preset conversion method includes linear conversion
  • the AI computing power factor value is determined according to the product of a and AI computing power, where a is a natural number
  • the preset conversion method includes During exponential conversion, the AI computing power factor value is determined according to the a power of the AI computing power
  • the preset conversion method includes logarithmic conversion
  • the AI computing power factor value is determined with a as the base and the AI computing power as the true number.
  • the preset conversion mode is configured by the access network device to the terminal, such as: the access network device uses physical layer signaling, MAC layer signaling, radio resource control layer (Radio Resource Control, RRC) signaling or broadcast. At least one of the signaling modes configures the terminal with a preset conversion mode.
  • the access network device uses physical layer signaling, MAC layer signaling, radio resource control layer (Radio Resource Control, RRC) signaling or broadcast.
  • RRC Radio Resource Control
  • At least one of the signaling modes configures the terminal with a preset conversion mode.
  • the above conversion method is only a schematic example, and the determination method of the AI computing power factor value may also include other conversion forms, which are not limited in the embodiments of the present disclosure.
  • the device switching situation when the device switching situation is determined by using the first AI computing power factor value as an offset parameter, it includes any one of the following situations:
  • the first AI computing power factor value is used as a positive offset parameter to determine the source access network device.
  • the first AI computing power factor value is used to reflect the AI computing power of the source access network device, when the first AI computing power factor value is small, it means that the source access network device is more available in AI computing power. If the value is low, then according to the value of the first AI computing power factor, a handover offset is performed in a direction biased towards handover, so as to determine whether to handover from the source access network device to the target access network device.
  • the first AI computing power factor value is used as the reverse offset parameter to determine the source access network device. Device handover between the device and the target access network device.
  • the value of the first AI computing power factor is greater than or equal to the first threshold (that is, the value of the first AI computing power factor is relatively large)
  • the value of the first AI computing power factor is relatively large
  • a handover offset is performed in the direction of not switching, so as to determine whether to handover from the source access network device to the target access network device.
  • the switching offset is performed with the value of the first AI computing power factor as a positive offset parameter, or, the value of the first AI computing power factor is reversed.
  • the embodiment of the present disclosure does not limit the offset mode when the value of the first AI computing power factor is equal to the first threshold.
  • the device switching situation when the device switching situation is determined by using the second AI computing power factor value as an offset parameter, it includes any one of the following situations:
  • the second AI computing power factor value is used as the reverse offset parameter to determine the source access network device.
  • the second AI computing power factor value is used to reflect the AI computing power of the target access network device, when the second AI computing power factor value is small, it means that the target access network device is more available in AI computing power. If it is low, then according to the value of the second AI computing power factor, the handover is shifted in the direction of not switching, so as to determine whether to switch from the source access network device to the target access network device.
  • the second AI computing power factor value is used as a positive offset parameter to determine the source access network device.
  • a handover offset is performed in a direction biased toward handover, so as to determine whether to handover from the source access network device to the target access network device.
  • the switching offset is performed with the value of the second AI computing power factor as a positive offset parameter, or, the value of the second AI computing power factor is reversed.
  • the embodiment of the present disclosure does not limit the offset mode when the second AI computing power factor value is equal to the second threshold.
  • the device switching situation when the device switching situation is determined by using the computing power factor difference as an offset parameter, it includes any one of the following situations:
  • the computing power factor difference when the computing power factor difference is greater than or equal to the third threshold (that is, the computing power factor difference is larger), it indicates that the target access network device has higher availability in AI computing power relative to the source access network device. , then according to the difference of the computing power factor, the handover offset is performed in the direction of the handover, so as to determine whether to switch from the source access network device to the target access network device.
  • the difference of the computing power factor when the difference of the computing power factor is less than or equal to the third threshold, it indicates that the difference in AI computing power between the target access network device and the source access network device is not large, and the difference in AI computing power will be biased according to the difference of the computing power factor.
  • the direction of the handover is handover offset, so as to determine whether to handover from the source access network device to the target access network device.
  • the embodiment of the present disclosure does not limit the offset mode when the difference of the computing power factor is equal to the third threshold.
  • the way of determining the offset parameter is configured by the access network device to the terminal device, such as: the access network device uses at least one of physical layer signaling, MAC layer signaling, RRC signaling or broadcast signaling.
  • the method of determining the offset parameter is configured to the terminal.
  • the above-mentioned first threshold, second threshold or third threshold is configured by the access network device to the terminal device, for example: the access network device uses physical layer signaling, MAC layer signaling, RRC signaling or broadcast signaling. Configure the terminal with at least one of the first threshold, the second threshold or the third threshold.
  • the terminal determines the device switching situation as an example for description.
  • the terminal reports the AI computing power factor to the source access network device. value, the source access network device determines whether to perform handover; or, after the source access network device determines the AI computing power factor value from the upper management network element, it determines whether to perform handover according to the RSRP or RSRQ reported by the terminal.
  • the AI computing power factor value of the access network device is used as the offset parameter, and when determining whether to switch from the source access network device to the target access network device, according to the AI
  • the computing power factor value performs offset processing on the original determination process.
  • the AI computing power factor value indicates that the AI computing power is suitable for switching, it is offset in the positive offset direction to complete the device switching determination.
  • the force factor indicates that when the AI computing power is not suitable for switching, the offset is performed in the reverse offset direction to complete the determination of device switching, which improves the accuracy of the device switching process.
  • FIG. 6 is a flowchart of a device switching method provided by another exemplary embodiment of the present disclosure. Taking application in a terminal as an example, as shown in FIG. 6 , the method includes:
  • Step 601 Determine the first AI computing power factor value of the source access network device.
  • the source access network device sends downlink signaling to the terminal device, and the downlink signaling includes an indication field, where the indication field is used to indicate the first AI computing power factor value of the source access network device.
  • Step 602 in response to the value of the first AI computing power factor being less than or equal to the first threshold, using the first AI computing power factor value as a forward offset parameter to determine the device between the source access network device and the target access network device switch situation.
  • performing offset processing using the first AI computing power factor value as a forward offset parameter includes at least one of the following situations:
  • the source access network device is handed over to the target access network device;
  • the first reference switching parameter includes at least one of reference signal receiving quality (Reference Signal Receiving Quality, RSRQ) and reference signal receiving power (Reference Signal Receiving Power, RSRP) of the target access network device.
  • reference signal receiving quality Reference Signal Receiving Quality, RSRQ
  • RSRP Reference Signal Receiving Power
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the comparison result between the sum value or the product value and the switching threshold is used as an example to illustrate, and the first reference switching parameter and the first AI can also be calculated in a forward calculation manner.
  • the computing power factor value is calculated, it is compared with the switching threshold, that is, the first benchmark switching parameter and the first AI computing power factor value are calculated in a positive additive manner, and the calculation result is compared with the switching threshold. right.
  • the source access network device in response to the sum of the inverse number of the first AI computing power factor and the second reference handover parameter of the source access network device being less than the handover threshold, the source access network device is handed over to the target access network device, and the second reference handover
  • the parameter includes at least one of RSRQ and RSRP of the source access network device;
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the comparison result of the difference or ratio and the switching threshold is used as an example for illustration, and the second reference switching parameter and the first AI computing power factor value can also be calculated in a reverse calculation method. , compared with the switching threshold, that is, the second reference switching parameter and the first AI computing power factor value are calculated in a reverse reduction manner, and the calculation result is compared with the switching threshold.
  • the source access network device is handed over to the target access network device, wherein the handover parameter difference is the target access network device.
  • the difference between the first reference handover parameter of the network device and the second reference handover parameter of the source access network device is the target access network device.
  • the source access network device is handed over to the target access network device.
  • Step 603 in response to the value of the first AI computing power factor being greater than or equal to the first threshold, using the first AI computing power factor value as a reverse offset parameter to determine the device between the source access network device and the target access network device switch situation.
  • performing offset processing using the first AI computing power factor value as a reverse offset parameter includes at least one of the following situations:
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device.
  • the source access network device is handed over to the target access network device.
  • the source access The network device switches to the target access network device.
  • the above-mentioned reaching the switching threshold is implemented as greater than the switching threshold, or implemented as greater than or equal to the switching threshold.
  • Step 604 Determine the second AI computing power factor value of the target access network device.
  • the source access network device sends downlink signaling to the terminal device, and the downlink signaling includes an indication field, where the indication field is used to indicate the first AI computing power factor value of the source access network device.
  • Step 605 in response to the second AI computing power factor value being less than or equal to the second threshold, using the second AI computing power factor value as a reverse offset parameter to determine the device between the source access network device and the target access network device switch situation.
  • performing offset processing using the second AI computing power factor value as a reverse offset parameter includes at least one of the following situations:
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the source access The network device switches to the target access network device.
  • the above-mentioned reaching the switching threshold is implemented as greater than the switching threshold, or, implemented as greater than or equal to the switching threshold; the above-mentioned less than the switching threshold can also be implemented as less than or equal to the switching threshold.
  • Step 606 in response to the second AI computing power factor value reaching the second threshold, using the second AI computing power factor value as a forward offset parameter to determine the device switching situation between the source access network device and the target access network device .
  • performing offset processing using the second AI computing power factor value as a forward offset parameter includes at least one of the following situations:
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device, wherein the handover parameter difference is the target access network device.
  • the difference between the first reference handover parameter of the network device and the second reference handover parameter of the source access network device is the target access network device.
  • the above-mentioned reaching the switching threshold is implemented as greater than the switching threshold, or, implemented as greater than or equal to the switching threshold; the above-mentioned less than the switching threshold can also be implemented as less than or equal to the switching threshold.
  • Step 607 Determine the computing power factor difference between the second AI computing power factor value and the first AI computing power factor value.
  • Step 608 in response to the difference of the computing power factor reaching the third threshold, using the difference of the computing power factor as a positive offset parameter to determine the device handover situation between the source access network device and the target access network device.
  • performing offset processing by using the computing power factor difference as a forward offset parameter includes at least one of the following situations:
  • the source access network device in response to the sum of the difference between the switching parameter and the difference between the computing power factor reaching the switching threshold, the source access network device is switched to the target access network device;
  • the source access network device is handed over to the target access network device
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device.
  • the above-mentioned reaching the switching threshold is implemented as greater than the switching threshold, or, implemented as greater than or equal to the switching threshold; the above-mentioned less than the switching threshold can also be implemented as less than or equal to the switching threshold.
  • Step 609 in response to the difference of the computing power factor being less than the third threshold, using the difference of the computing power factor as a reverse offset parameter to determine the device handover situation between the source access network device and the target access network device.
  • performing offset processing using the difference in computing power factor as a reverse offset parameter includes at least one of the following situations:
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device
  • the source access network device is handed over to the target access network device;
  • the source access network device is handed over to the target access network device
  • the source access network device is handed over to the target access network device.
  • the above-mentioned reaching the switching threshold is implemented as greater than the switching threshold, or, implemented as greater than or equal to the switching threshold; the above-mentioned less than the switching threshold can also be implemented as less than or equal to the switching threshold.
  • the above embodiments are mainly for handover between base stations.
  • the AI computing power factor value and reference access parameters (such as RSRP, RSRQ, etc.) can also be combined.
  • the access base station is determined.
  • the AI computing power factor value of the access network device is used as the offset parameter, and when determining whether to switch from the source access network device to the target access network device, according to the AI
  • the computing power factor value performs offset processing on the original determination process.
  • the AI computing power factor value indicates that the AI computing power is suitable for switching, it is offset in the positive offset direction to complete the device switching determination.
  • the force factor indicates that when the AI computing power is not suitable for switching, the offset is performed in the reverse offset direction to complete the determination of device switching, which improves the accuracy of the device switching process.
  • FIG. 7 is a structural block diagram of a device switching apparatus provided by an exemplary embodiment of the present disclosure. As shown in FIG. 7 , the apparatus includes:
  • the processing module 710 is configured to use the AI computing power factor value of the access network device as an offset parameter to determine the device switching situation between the access network devices.
  • processing module 710 is further configured to determine the first AI computing power factor value of the source access network device
  • the processing module 710 is further configured to, in response to the value of the first AI computing power factor being less than a first threshold or the value of the first AI computing power factor being equal to the first threshold, use the first AI computing power
  • the factor value is a forward offset parameter that determines the device handover situation between the source access network device and the target access network device.
  • the processing module 710 is further configured to respond that the sum of the first reference handover parameter of the target access network device and the first AI computing power factor value reaches a handover threshold,
  • the source access network device is handed over to the target access network device, and the first reference handover parameter includes at least one of the reference signal received quality RSRQ and the reference signal received power RSRP of the target access network device ;
  • the processing module 710 is further configured to switch by the source access network device in response to the product of the first reference switching parameter of the target access network device and the first AI computing power factor value reaching a switching threshold. to the target access network device;
  • the processing module 710 is further configured to, in response to the sum of the inverse number of the first AI computing power factor and the second reference handover parameter of the source access network device being less than the handover threshold, send the data to the source access network by the source access network.
  • the device switches to the target access network device, and the second reference switching parameter includes at least one of RSRQ and RSRP of the source access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the ratio of the second reference handover parameter of the source access network device to the first AI computing power factor value being less than the handover threshold. to the target access network device.
  • the processing module 710 is further configured to determine a first AI computing power factor value of the source access network device; in response to the first AI computing power factor value being equal to the first threshold or The value of the first AI computing power factor is greater than the first threshold, and the first AI computing power factor value is a reverse offset parameter determined between the source access network device and the target access network device. of the device switching situation.
  • the processing module 710 is further configured to achieve a handover in response to the sum of the inverse number of the first AI computing power factor and the first reference handover parameter of the target access network device a threshold, where the source access network device is handed over to the target access network device, and the first reference handover parameter includes at least one of RSRQ and RSRP of the target access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the ratio of the first reference switching parameter of the target access network device and the first AI computing power factor value reaching a switching threshold. to the target access network device;
  • the processing module 710 is further configured to be switched by the source access network device in response to the sum of the second reference switching parameter of the source access network device and the first AI computing power factor value being less than the switching threshold.
  • the second reference handover parameter includes at least one of RSRQ and RSRP of the source access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the product of the second reference switching parameter of the source access network device and the first AI computing power factor value being less than the switching threshold. to the target access network device.
  • processing module 710 is further configured to determine the second AI computing power factor value of the target access network device
  • the processing module 710 is further configured to, in response to the second AI computing power factor value being less than a second threshold or the second AI computing power factor value being equal to the second threshold, use the second AI computing power
  • the factor value is a reverse offset parameter that determines the device handover situation between the source access network device and the target access network device.
  • the processing module 710 is further configured to achieve a handover in response to the sum of the opposite number of the second AI computing power factor and the first reference handover parameter of the target access network device a threshold, where the source access network device is handed over to the target access network device, and the first reference handover parameter includes at least one of RSRQ and RSRP of the target access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the ratio of the first reference switching parameter of the target access network device and the second AI computing power factor value reaching a switching threshold. to the target access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the sum of the second reference switching parameter of the source access network device and the second AI computing power factor being less than the switching threshold.
  • the second reference handover parameter includes at least one of RSRQ and RSRP of the source access network device;
  • the processing module 710 is further configured to be switched by the source access network device in response to the product of the second reference switching parameter of the source access network device and the second AI computing power factor value being less than a switching threshold. to the target access network device.
  • the processing module 710 is further configured to determine a second AI computing power factor value of the target access network device; in response to the second AI computing power factor value being equal to the second threshold or The value of the second AI computing power factor is greater than the second threshold, and the value of the second AI computing power factor is a positive offset parameter determined to be between the source access network device and the target access network device. of the device switching situation.
  • the processing module 710 is further configured to respond that the sum of the first reference handover parameter of the target access network device and the second AI computing power factor value reaches a handover threshold, handover from the source access network device to the target access network device, and the first reference handover parameter includes at least one of RSRQ and RSRP of the target access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the product of the first reference switching parameter of the target access network device and the second AI computing power factor value reaching a switching threshold. to the target access network device;
  • the processing module 710 is further configured to, in response to the sum of the opposite number of the second AI computing power factor and the second reference handover parameter of the source access network device being less than the handover threshold, send the data to the source access network by the source access network.
  • the device switches to the target access network device, and the second reference switching parameter includes at least one of RSRQ and RSRP of the source access network device;
  • the processing module 710 is further configured to switch by the source access network device in response to the ratio of the second reference handover parameter of the source access network device to the second AI computing power factor value being less than the handover threshold. to the target access network device.
  • the processing module 710 is further configured to determine the first AI computing power factor value of the source access network device and the second AI computing power factor value of the target access network device; the difference in computing power between the second AI computing power factor value and the first AI computing power factor value;
  • the processing module 710 is further configured to, in response to the difference of the computing power factor being equal to a third threshold or the difference of the computing power factor being greater than the third threshold, take the difference of the computing power factor as a positive bias. Shift parameters to determine the device handover situation between the source access network device and the target access network device.
  • the processing module 710 is further configured to, in response to the sum of the handover parameter difference and the computing power factor difference reaching a handover threshold, the source access network device to handover to the the target access network device, wherein the handover parameter difference is the difference between the first reference handover parameter of the target access network device and the second reference handover parameter of the source access network device;
  • the processing module 710 is further configured to switch from the source access network device to the target access network device in response to the product of the handover parameter difference and the computing power factor difference reaching a handover threshold.
  • the processing module 710 is further configured to determine the first AI computing power factor value of the source access network device and the second AI computing power factor value of the target access network device; computing power factor difference between the second AI computing power factor value and the first AI computing power factor value; in response to the computing power factor difference being less than the third threshold or the computing power factor difference being equal to the
  • the third threshold is used to determine the device handover situation between the source access network device and the target access network device by using the computing power factor difference as a reverse offset parameter.
  • the processing module 710 is further configured to, in response to the sum of the inverse of the difference of the computing power factor and the difference of the handover parameter reaching the handover threshold, send the source access network device to the handover to the target access network device, wherein the handover parameter difference is the difference between the first reference handover parameter of the target access network device and the second reference handover parameter of the source access network device;
  • the processing module 710 is further configured to switch from the source access network device to the target access network device in response to the ratio of the handover parameter difference to the computing power factor difference reaching a handover threshold.
  • the processing module 710 is further configured to convert the AI computing power of the access network device according to a preset conversion method to obtain the AI computing power factor value;
  • the conversion method includes at least one of linear conversion, exponential conversion, and logarithmic conversion.
  • the device switching device uses the AI computing power factor value of the access network device as an offset parameter, and when determining whether to switch from the source access network device to the target access network device, according to the AI
  • the computing power factor value performs offset processing on the original determination process.
  • the AI computing power factor value indicates that the AI computing power is suitable for switching, it is offset in the positive offset direction to complete the device switching determination.
  • the force factor indicates that when the AI computing power is not suitable for switching, the offset is performed in the reverse offset direction to complete the determination of device switching, which improves the accuracy of the device switching process.
  • FIG. 8 shows a schematic structural diagram of a terminal provided by an exemplary embodiment of the present disclosure.
  • the terminal includes: a processor 801 , a receiver 802 , a transmitter 803 , a memory 804 , and a bus 805 .
  • the processor 801 includes one or more processing cores, and the processor 801 executes various functional applications and information processing by running software programs and modules.
  • the receiver 802 and the transmitter 803 may be implemented as a communication component, which may be a communication chip.
  • the memory 804 is connected to the processor 801 through the bus 805 .
  • the memory 804 may be configured to store at least one instruction, and the processor 801 may be configured to execute the at least one instruction to implement the various steps in the above method embodiments.
  • memory 804 may be implemented by any type or combination of volatile or non-volatile storage devices including, but not limited to: magnetic or optical disks, electrically erasable programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Static Anytime Access Memory (SRAM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Programmable Read Only Memory (PROM) .
  • EEPROM electrically erasable programmable Read Only Memory
  • EPROM Erasable Programmable Read Only Memory
  • SRAM Static Anytime Access Memory
  • ROM Read Only Memory
  • Magnetic Memory Magnetic Memory
  • Flash Memory Programmable Read Only Memory
  • a non-transitory computer-readable storage medium including instructions such as a memory including instructions, is also provided, and the instructions can be executed by the processor of the terminal to complete the above-mentioned device switching method executed by the terminal side.
  • the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
  • a non-transitory computer-readable storage medium when an instruction in the non-transitory computer storage medium is executed by a processor of a terminal, enables the terminal to execute the above device switching method.
  • FIG. 9 is a block diagram of an access network device 900 according to an exemplary embodiment.
  • the access network device 900 is a base station.
  • the access network device 900 includes: a processor 901 , a receiver 902 , a transmitter 903 and a memory 904 .
  • the receiver 902, the transmitter 903 and the memory 904 are respectively connected to the processor 901 through a bus.
  • the processor 901 includes one or more processing cores, and the processor 901 executes the method performed by the access network device in the device switching method provided by the embodiment of the present disclosure by running software programs and modules.
  • Memory 904 may be used to store software programs and modules. Specifically, the memory 904 can store the operating system 941 and the application program module 942 required for at least one function.
  • the receiver 902 is used for receiving communication data sent by other devices, and the transmitter 903 is used for sending communication data to other devices.
  • An exemplary embodiment of the present disclosure further provides a communication system, the system includes: a terminal and an access network device;
  • the terminal includes the device switching apparatus provided in the embodiment shown in FIG. 7 .
  • An exemplary embodiment of the present disclosure also provides a communication system, where the communication system includes: a terminal and an access network device;
  • the terminal includes the terminal provided by the embodiment shown in FIG. 8;
  • the access network device includes the access network device provided in the embodiment shown in FIG. 9 .
  • An exemplary embodiment of the present disclosure further provides a computer-readable storage medium, where at least one instruction, at least one piece of program, code set or instruction set is stored in the computer-readable storage medium, the at least one instruction, the At least one piece of program, the code set or the instruction set is loaded and executed by the processor to implement the steps executed by the terminal or the access network device in the device switching method provided by each of the above method embodiments.
  • references herein to "a plurality” means two or more.
  • "And/or" which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone.
  • the character “/” generally indicates that the associated objects are an "or" relationship.

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Abstract

本公开提供了一种设备切换方法、装置、设备及可读存储介质，涉及通信领域。该方法包括：以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。将接入网设备的AI算力因素值作为偏移参数，在确定是否从源接入网设备切换至目标接入网设备时，根据AI算力因素值对原确定过程进行偏移处理，当AI算力因素值表示AI算力上适合切换时，以正向偏移方向进行偏移从而完成设备切换的确定，同理，当AI算力因素表示AI算力上不适合切换时，以反向偏移方向进行偏移从而完成设备切换的确定，提高了设备切换过程中的准确率。

Description

设备切换方法、装置、设备及可读存储介质 技术领域
本公开涉及通信领域，特别涉及一种设备切换方法、装置、设备及可读存储介质。
背景技术
人工智能(Artificial Intelligence，AI)技术在6G蜂窝网络基础上部署的时候，边缘节点的学习能力或计算资源成为AI应用实现过程中的关键要素，如：语音识别、视频理解等AI应用。
然而，相关技术中，蜂窝网络在用户终端(User Equipment，UE)移动的时候，仅会考虑蜂窝网络的信号强弱或质量以进行基站之间的切换，设备切换过程中的考虑因素较为单一。
发明内容
本公开实施例提供了一种设备切换方法、装置、设备及可读存储介质，能够提高基站切换过程中的切换准确率和考虑的全面性。所述技术方案如下：
一方面，提供了一种设备切换方法，应用于终端设备中，所述方法包括：
以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。
另一方面，提供了一种设备切换装置，应用于终端设备中，所述装置包括：
处理模块，被配置为以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。
另一方面，提供了一种终端，该终端包括：
处理器；
与处理器相连的收发器；
用于存储所述处理器的可执行信令的存储器；
其中，处理器被配置为加载并执行可执行指令以实现如上述本公开实施例所述的设备切换方法。
另一方面，提供了一种计算机可读存储介质，该计算机可读存储介质中存储有至少一条指令、至少一段程序、代码集或指令集，上述至少一条指令、至少一段程序、代码集或指令集由处理器加载并执行以实现如上述本公开实施例所述的设备切换方法。
本公开实施例提供的技术方案带来的有益效果至少包括：
将接入网设备的AI算力因素值作为偏移参数，在确定是否从源接入网设备切换至目标接入网设备时，根据AI算力因素值对原确定过程进行偏移处理，当AI算力因素值表示AI算力上适合切换时，以正向偏移方向进行偏移从而完成设备切换的确定，同理，当AI算力因素表示AI算力上不适合切换时，以反向偏移方向进行偏移从而完成设备切换的确定，提高了设备切换过程中的准确率。
附图说明
为了更清楚地说明本公开实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本公开一个示例性实施例提供的通信***的框图；
图2是本公开一个示例性实施例提供的AI算力值的分配方式示意图；
图3是本公开另一个示例性实施例提供的AI算力值的分配方式示意图；
图4是本公开另一个示例性实施例提供的AI算力值的分配方式示意图；
图5是本公开一个示例性实施例提供的设备切换方法的流程图；
图6是本公开另一个示例性实施例提供的设备切换方法的流程图；
图7是本公开一个示例性实施例提供的设备切换装置的结构框图；
图8是本公开一个示例性实施例提供的终端的框图；
图9是本公开一个示例性实施例提供的接入网设备的框图。
具体实施方式
为使本公开的目的、技术方案和优点更加清楚，下面将结合附图对本公开实施方式作进一步地详细描述。
图1示出了本公开一个示意性实施例提供的通信***的框图，该通信***包括：核心网11、接入网12和终端13。
核心网11中包括若干个核心网设备110。核心网设备110包括接入和移动管理功能(Access and Mobility Management Function，AMF)，会话管理功能(Session Management Function，SMF)以及用户面管理功能(User Plane Function，UPF)等设备，其中，AMF用于控制终端的接入权限以及切换等功能，SMF用于提供服务器连续性、服务器的不间断用户体验，如：IP地址和锚点变化等。
接入网12中包括若干个接入网设备120。在一些实施例中，接入网设备120是基站，基站是一种部署在接入网中用以为终端提供无线通信功能的装置。基站包括各种形式的宏基站，微基站，中继站，接入点等等。在采用不同的无线接入技术的***中，具备基站功能的设备的名称可能会有所不同，例如在长期演进(Long Term Evolution，LTE)***中，称为eNodeB或者eNB；在5G新空口(New Radio，NR)***中，称为gNode B或者gNB。随着通信技术的演进，“基站”这一名称可能描述，会变化。为方便本公开实施例中，上述为终端提供无线通信功能的装置统称为接入网设备。
可选地，本公开实施例中涉及的接入网设备120具有边缘计算能力。
终端13包括各种具有无线通信功能的手持设备、车载设备、可穿戴设备、计算设备或连接到无线调制解调器的其他处理设备，以及各种形式的终端(User Equipment，UE)，移动台(Mobile Station，MS)，终端(terminal device)等等。为方便描述，上面提到的设备统称为终端。接入网设备120与终端13之间通过某种空口技术互相通信，例如Uu接口。
边缘计算，是指在靠近终端或数据源头的网络边缘侧设置边缘节点，融合网络、计算、存储、应用核心能力的分布开放式平台，就近提供边缘智能化服务的一种计算技术。可选地，网络边缘即指示与一个网络的中心服务器以及所对应地硬件设备相区别的，处在网络边缘的服务器以及对应地硬件设备。在一个示例中，对一个UE发送的数据在网络边缘节点进行处理时，因为网络的边缘节点任务量较小，距离UE较近，通信效果较好，故相应时间将快于在网络中心服务器进行处理的相应时间。可选地，相关技术中，网络边缘可实现为基站gNB。
本公开实施例中，将边缘节点的AI算力作为移动性管理的偏移因素，如： 将基站或上层管理网元，如关键控制节点(Mobility Management Entity，MME)的AI算力作为基站切换的偏移因素，使UE在不影响连接性的基础上，向AI算力更可用的基站切换。
可选地，边缘节点的AI算力用于指示边缘节点的计算能力，其中，该计算能力包括边缘节点的内存占用情况、中央处理器(Central Processing Unit，CPU)占用情况或存储量占用情况等，也可以是以AI运算为目的生成的用于指示AI运算能力的指标，本公开实施例对此不加以限定。
可选地，边缘侧AI算力部署下沉的层次可以是基站侧，也即，以基站的AI算力作为移动性管理的偏移因素，也可以是基站以上的上层管理网元，如MME等。
其中，当AI算力部署下沉至基站侧时，AI算力因素直接标记在基站中；当AI算力部署下沉至上层管理网元时，归属该上层管理网元进行管理的基站可以继承上层管理网元的AI算力，其中，继承方式根据基站之间的资源分配方式包括如下方式中的至少一种：
第一，下属基站的AI算力与上层管理网元的AI算力一致；
示意性的，请参考图2，上层管理网元210的AI算力为u，边缘节点(基站)221的AI算力为u，且边缘节点(基站)222的AI算力也为u。
可选地，在方式下，下属基站对时频资源进行划分使用。
第二，上层管理网元的AI算力平均分配至下属基站；
示意性的，请参考图3，上层管理网元310的AI算力为u，边缘节点(基站)321的AI算力为u/2，且边缘节点(基站)322的AI算力也为u/2。
第三，上层管理网元的AI算力根据预设分配机制分配至下属基站。
示意性的，上层管理网元根据下属基站的业务部署密度进行AI算力的分配，请参考图4，上层管理网元410的AI算力为u，其中，u＝u a+u b，根据业务部署密度，向边缘节点(基站)421分配的AI算力为u a，且边缘节点(基站)422的AI算力为u b。
值得注意的是，上述AI算力是指基站或者上层管理网元的最大AI计算能力；或，上述AI算力是指基站或者上层管理网元在一定时间区间内的最大可用AI计算能力，也即剩余AI计算能力。
可选地，上层管理网元通过边缘节点之间的传输接口向边缘节点(也即基 站)进行AI算力因素值的更新，如：通过S1信令向基站更新AI算力。
图5是本公开一个示例性实施例提供的设备切换方法的流程图，以该方法应用于终端设备中为例进行说明，如图5所示，该方法包括：
步骤501，以接入网设备的AI算力因素值作为偏移参数，确定在接入网设备之间的设备切换情况。
可选地，作为偏移参数的AI算力因素值为源接入网设备的第一AI算力因素值；或，作为偏移参数的AI算力因素值为目标接入网设备的第二AI算力因素值；或，作为偏移参数的AI算力因素值为第二AI算力因素值和第一AI算力因素值的算力因素差值。可选地，算力因素差值为第二AI算力因素值的绝对值减去第一AI算力因素值的绝对值得到的差值。
可选地，终端接收接入网设备发送的下行信令，下行信令中包括接入网设备的AI算力。
可选地，AI算力因素值为接入网设备的AI算力；或，根据预设转换方式对接入网设备的AI算力进行转换，得到AI算力因素值，其中，转换方式包括线性转换、指数转换、对数转换中的至少一种。示意性的，针对不同的转换方式进行说明，当预设转换方式中包括线性转换时，则根据a与AI算力的乘积确定AI算力因素值，a为自然数；当预设转换方式中包括指数转换时，根据AI算力的a次方确定AI算力因素值；当预设转换方式中包括对数转换时，则以a为底数，AI算力为真数，确定AI算力因素值。
可选地，预设转换方式为接入网设备向终端配置的，如：接入网设备通过物理层信令、MAC层信令、无线资源控制层(Radio Resource Control，RRC)信令或广播信令中的至少一种方式向终端配置预设转换方式。
上述转换方式仅为示意性的举例，AI算力因素值的确定方式还可以包括其他转换形式，本公开实施例对此不加以限定。
可选地，当将第一AI算力因素值作为偏移参数确定设备切换情况时，包括如下情况中的任意一种：
1.1、响应于第一AI算力因素值小于第一阈值或第一AI算力因素值等于第一阈值，以第一AI算力因素值为正向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况；
可选地，由于第一AI算力因素值用于体现源接入网设备的AI算力，则当 第一AI算力因素值较小时，表示源接入网设备在AI算力上可用性较低，则根据第一AI算力因素值向偏向切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
1.2、响应于第一AI算力因素值大于第一阈值或第一AI算力因素值等于第一阈值，以第一AI算力因素值为反向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况。
可选地，当第一AI算力因素值大于或者等于第一阈值(也即第一AI算力因素值较大)时，表示源接入网设备在AI算力上的可用性较高，则根据第一AI算力因素值向偏向不切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
值得注意的是，当第一AI算力因素值等于第一阈值时，以第一AI算力因素值为正向偏移参数进行切换偏移，或，以第一AI算力因素值为反向偏移参数进行切换偏移，本公开实施例对第一AI算力因素值等于第一阈值时的偏移方式不加以限定。
可选地，当将第二AI算力因素值作为偏移参数确定设备切换情况时，包括如下情况中的任意一种：
2.1、响应于第二AI算力因素值小于第二阈值或第二AI算力因素值等于第二阈值，以第二AI算力因素值为反向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况；
可选地，由于第二AI算力因素值用于体现目标接入网设备的AI算力，则当第二AI算力因素值较小时，表示目标接入网设备在AI算力上可用性较低，则根据第二AI算力因素值向偏向不切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
2.2、响应于第二AI算力因素值大于第二阈值或第二AI算力因素值等于第二阈值，以第二AI算力因素值为正向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况。
可选地，当第二AI算力因素值大于或者等于第二阈值(也即第二AI算力因素值较大)时，表示目标接入网设备在AI算力上的可用性较高，则根据第二AI算力因素值向偏向切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
值得注意的是，当第二AI算力因素值等于第二阈值时，以第二AI算力因 素值为正向偏移参数进行切换偏移，或，以第二AI算力因素值为反向偏移参数进行切换偏移，本公开实施例对第二AI算力因素值等于第二阈值时的偏移方式不加以限定。
可选地，当将算力因素差值作为偏移参数确定设备切换情况时，包括如下情况中的任意一种：
3.1、响应于算力因素差值大于第三阈值或算力因素差值等于第三阈值，以算力因素差值为正向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况；
可选地，当算力因素差值大于或等于第三阈值(也即算力因素差值较大)时，表示目标接入网设备相对源接入网设备在AI算力上的可用性更高，则根据算力因素差值向偏向切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
3.2、响应于算力因素差值小于第三阈值或算力因素差值等于第三阈值，以算力因素差值为反向偏移参数，确定在源接入网设备和目标接入网设备之间的设备切换情况。
可选地，当算力因素差值小于或等于第三阈值时，表示目标接入网设备相对源接入网设备在AI算力上的差别不大，则根据算力因素差值向偏向不切换的方向进行切换偏移，从而确定是否从源接入网设备切换至目标接入网设备。
值得注意的是，当算力因素差值等于第三阈值时，以算力因素差值为正向偏移参数进行切换偏移，或，以算力因素差值为反向偏移参数进行切换偏移，本公开实施例对算力因素差值等于第三阈值时的偏移方式不加以限定。
可选地，偏移参数的确定方式为接入网设备向终端设备配置的，如：接入网设备通过物理层信令、MAC层信令、RRC信令或广播信令中的至少一种方式向终端配置偏移参数的确定方式。
可选地，上述第一阈值、第二阈值或第三阈值为接入网设备向终端设备配置的，如：接入网设备通过物理层信令、MAC层信令、RRC信令或广播信令中的至少一种方式向终端配置第一阈值、第二阈值或第三阈值。
上述实施例中，以终端确定设备切换情况为例进行说明，在一个可选地实施例中，终端确定接入网设备的AI算力因素值后，向源接入网设备上报AI算力因素值，由源接入网设备确定是否进行切换；或，源接入网设备从上层管理网元确定AI算力因素值后，根据终端上报的RSRP或RSRQ确定是否进行切换。
综上所述，本实施例提供的设备切换方法，将接入网设备的AI算力因素值作为偏移参数，在确定是否从源接入网设备切换至目标接入网设备时，根据AI算力因素值对原确定过程进行偏移处理，当AI算力因素值表示AI算力上适合切换时，以正向偏移方向进行偏移从而完成设备切换的确定，同理，当AI算力因素表示AI算力上不适合切换时，以反向偏移方向进行偏移从而完成设备切换的确定，提高了设备切换过程中的准确率。
在一个可选的实施例中，正向偏移和反向偏移通过对基准切换参数的调整实现，图6是本公开另一个示例性实施例提供的设备切换方法的流程图，以该方法应用于终端中为例进行说明，如图6所示，该方法包括：
步骤601，确定源接入网设备的第一AI算力因素值。
可选地，源接入网设备向终端设备发送下行信令，下行信令中包括指示字段，指示字段用于指示源接入网设备的第一AI算力因素值。
步骤602，响应于第一AI算力因素值小于或等于第一阈值，以第一AI算力因素值为正向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将第一AI算力因素值作为正向偏移参数进行偏移处理包括如下情况中的至少一种：
第一，响应于目标接入网设备的第一基准切换参数与第一AI算力因素值之和达到切换阈值，由源接入网设备切换至目标接入网设备；
其中，第一基准切换参数包括目标接入网设备的参考信号接收质量(Reference Signal Receiving Quality，RSRQ)和参考信号接收功率(Reference Signal Receiving Power，RSRP)中的至少一种。
可选地，响应于第一基准切换参数与第一AI算力因素值之和大于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第二，响应于目标接入网设备的第一基准切换参数与第一AI算力因素值之积达到切换阈值，由源接入网设备切换至目标接入网设备；
可选地，响应于第一基准切换参数与第一AI算力因素值之积大于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
值得注意的是，上述第一、第二种对比过程中，以和值或积值与切换阈值的对比结果为例进行说明，还能以正向计算方式对第一基准切换参数与第一AI 算力因素值进行计算后，与切换阈值进行对比，也即，将第一基准切换参数与第一AI算力因素值，以正向加成方式进行计算，并将计算结果与切换阈值进行比对。
第三，响应于第一AI算力因素值相反数与源接入网设备的第二基准切换参数之和小于切换阈值，由源接入网设备切换至目标接入网设备，第二基准切换参数包括源接入网设备的RSRQ和RSRP中的至少一种；
可选地，响应于第一AI算力因素值相反数与第二基准切换参数之和小于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第四，响应于源接入网设备的第二基准切换参数与第一AI算力因素值的比值小于切换阈值，由源接入网设备切换至目标接入网设备；
可选地，响应于第二基准切换参数与第一AI算力因素值的比值小于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
值得注意的是，上述对比过程中，以差值或比值与切换阈值的对比结果为例进行说明，还能以反向计算方式对第二基准切换参数与第一AI算力因素值进行计算后，与切换阈值进行对比，也即，将第二基准切换参数与第一AI算力因素值，以反向削减方式进行计算，并将计算结果与切换阈值进行比对。
第五，响应于切换参数差值与第一AI算力因素值之和/之积达到切换阈值，由源接入网设备切换至目标接入网设备，其中，切换参数差值为目标接入网设备的第一基准切换参数与源接入网设备的第二基准切换参数之差。
可选地，响应于切换参数差值与第一AI算力因素值之和/之积大于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
步骤603，响应于第一AI算力因素值大于或等于第一阈值，以第一AI算力因素值为反向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将第一AI算力因素值作为反向偏移参数进行偏移处理包括如下情况中的至少一种：
第一，响应于第一AI算力因素值相反数与目标接入网设备的第一基准切换参数之和达到切换阈值，由源接入网设备切换至目标接入网设备；
可选地，响应于第一AI算力因素值相反数与第一基准切换参数之和大于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第二，响应于目标接入网设备的第一基准切换参数与第一AI算力因素值的 比值达到切换阈值，由源接入网设备切换至目标接入网设备；
可选地，响应于第一基准切换参数与第一AI算力因素值的比值大于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第三，响应于源接入网设备的第二基准切换参数与第一AI算力因素值之和小于切换阈值，由源接入网设备切换至目标接入网设备；
可选地，响应于第二基准切换参数与第一AI算力因素值之和小于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第四，响应于源接入网设备的第二基准切换参数与第一AI算力因素值之积小于切换阈值，由源接入网设备切换至目标接入网设备。
可选地，响应于第二基准切换参数与第一AI算力因素值之积小于或者等于切换阈值，由源接入网设备切换至目标接入网设备。
第五，响应于第一AI算力因素值的相反数与切换参数差值之和达到切换阈值；或，切换参数差值与第一AI算力因素值的比值达到切换阈值，由源接入网设备切换至目标接入网设备。
可选地，上述达到切换阈值实现为大于切换阈值，或，实现为大于或等于切换阈值。
步骤604，确定目标接入网设备的第二AI算力因素值。
可选地，源接入网设备向终端设备发送下行信令，下行信令中包括指示字段，指示字段用于指示源接入网设备的第一AI算力因素值。
步骤605，响应于第二AI算力因素值小于或等于第二阈值，以第二AI算力因素值为反向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将第二AI算力因素值作为反向偏移参数进行偏移处理包括如下情况中的至少一种：
第一，响应于第二AI算力因素值相反数与目标接入网设备的第一基准切换参数之和达到切换阈值，由源接入网设备切换至目标接入网设备；
第二，响应于目标接入网设备的第一基准切换参数与第二AI算力因素值的比值达到切换阈值，由源接入网设备切换至目标接入网设备；
第三，响应于源接入网设备的第二基准切换参数与第二AI算力因素值之和小于切换阈值，由源接入网设备切换至目标接入网设备；
第四，响应于源接入网设备的第二基准切换参数与第二AI算力因素值之积 小于切换阈值，由源接入网设备切换至目标接入网设备。
第五，响应于第二AI算力因素值的相反数与切换参数差值之和达到切换阈值；或，切换参数差值与第二AI算力因素值的比值达到切换阈值，由源接入网设备切换至目标接入网设备。
值得注意的是，上述达到切换阈值实现为大于切换阈值，或，实现为大于或等于切换阈值；上述小于切换阈值还可以实现为小于或等于切换阈值。
步骤606，响应于第二AI算力因素值达到第二阈值，以第二AI算力因素值为正向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将第二AI算力因素值作为正向偏移参数进行偏移处理包括如下情况中的至少一种：
第一，响应于目标接入网设备的第一基准切换参数与第二AI算力因素值之和达到切换阈值，由源接入网设备切换至目标接入网设备；
第二，响应于目标接入网设备的第一基准切换参数与第二AI算力因素值之积达到切换阈值，由源接入网设备切换至目标接入网设备；
第三，响应于第二AI算力因素值的相反数与源接入网设备的第二基准切换参数之和小于切换阈值，由源接入网设备切换至目标接入网设备；
第四，响应于源接入网设备的第二基准切换参数与第二AI算力因素值的比值小于切换阈值，由源接入网设备切换至目标接入网设备；
第五，响应于切换参数差值与第一AI算力因素值之和/之积达到切换阈值，由源接入网设备切换至目标接入网设备，其中，切换参数差值为目标接入网设备的第一基准切换参数与源接入网设备的第二基准切换参数之差。
值得注意的是，上述达到切换阈值实现为大于切换阈值，或，实现为大于或等于切换阈值；上述小于切换阈值还可以实现为小于或等于切换阈值。
步骤607，确定第二AI算力因素值和第一AI算力因素值之间的算力因素差值。
可选地，将第一AI算力因素值的相反数与第二AI算力因素值相加，得到算力因素差值，也即，将第二AI算力因素值减去第一AI算力因素值，得到算力因素差值。
步骤608，响应于算力因素差值达到第三阈值，以算力因素差值为正向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将算力因素差值作为正向偏移参数进行偏移处理包括如下情况中 的至少一种：
第一，响应于切换参数差值与算力因素差值之和达到切换阈值，由源接入网设备切换至目标接入网设备；
第二，响应于切换参数差值用于算力因素差值之积达到切换，由源接入网设备切换至目标接入网设备；
第三，响应于目标接入网设备的第一基准切换参数与算力因素差值之和/之积达到切换阈值，由源接入网设备切换至目标接入网设备；
第四，响应于算力因素差值的相反数与第二基准切换参数之和小于切换阈值，由源接入网设备切换至目标接入网设备；
第五，响应于第二基准切换参数与算力因素差值的比值小于切换阈值，由源接入网设备切换至目标接入网设备。
值得注意的是，上述达到切换阈值实现为大于切换阈值，或，实现为大于或等于切换阈值；上述小于切换阈值还可以实现为小于或等于切换阈值。
步骤609，响应于算力因素差值小于第三阈值，以算力因素差值为反向偏移参数，确定源接入网设备和目标接入网设备之间的设备切换情况。
可选地，将算力因素差值作为反向偏移参数进行偏移处理包括如下情况中的至少一种：
第一，响应于算力因素差值的相反数与切换参数差值之和达到切换阈值，由源接入网设备切换至目标接入网设备；
第二，响应于切换参数差值与算力因素差值的比值达到切换阈值，由源接入网设备切换至目标接入网设备；
第三，响应于算力因素差值的相反数与第一基准切换参数之和达到切换阈值，由源接入网设备切换至目标接入网设备；
第四，响应于第一基准切换参数与算力因素差值的比值达到切换阈值，由源接入网设备切换至目标接入网设备；
第五，响应于第二基准切换参数与算力因素差值之和/之积小于切换阈值，由源接入网设备切换至目标接入网设备。
值得注意的是，上述达到切换阈值实现为大于切换阈值，或，实现为大于或等于切换阈值；上述小于切换阈值还可以实现为小于或等于切换阈值。
值得注意的是，上述实施例中主要是针对在基站之间的切换进行说明，当终端初始化接入基站时，也可以结合AI算力因素值和基准接入参数(如：RSRP、 RSRQ等)进行接入基站的确定。
综上所述，本实施例提供的设备切换方法，将接入网设备的AI算力因素值作为偏移参数，在确定是否从源接入网设备切换至目标接入网设备时，根据AI算力因素值对原确定过程进行偏移处理，当AI算力因素值表示AI算力上适合切换时，以正向偏移方向进行偏移从而完成设备切换的确定，同理，当AI算力因素表示AI算力上不适合切换时，以反向偏移方向进行偏移从而完成设备切换的确定，提高了设备切换过程中的准确率。
图7是本公开一个示例性实施例提供的设备切换装置的结构框图，如图7所示，该装置包括：
处理模块710，被配置为以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为确定源接入网设备的第一AI算力因素值；
所述处理模块710，还被配置为响应于所述第一AI算力因素值小于第一阈值或所述第一AI算力因素值等于所述第一阈值，以所述第一AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的参考信号接收质量RSRQ和参考信号接收功率RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
或，
所述处理模块710，还被配置为响应于所述第一AI算力因素值相反数与所述源接入网设备的第二基准切换参数之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的 RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值的比值小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为确定源接入网设备的第一AI算力因素值；响应于所述第一AI算力因素值等于第一阈值或所述第一AI算力因素值大于所述第一阈值，以所述第一AI算力因素值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于所述第一AI算力因素值相反数与所述目标接入网设备的第一基准切换参数之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值之积小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为确定目标接入网设备的第二AI算力因素值；
所述处理模块710，还被配置为响应于所述第二AI算力因素值小于第二阈值或所述第二AI算力因素值等于所述第二阈值，以所述第二AI算力因素值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切 换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于所述第二AI算力因素值相反数与所述目标接入网设备的第一基准切换参数之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值之积小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为确定目标接入网设备的第二AI算力因素值；响应于所述第二AI算力因素值等于第二阈值或所述第二AI算力因素值大于所述第二阈值，以所述第二AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
或，
所述处理模块710，还被配置为响应于所述第二AI算力因素值相反数与所述源接入网设备的第二基准切换参数之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
或，
所述处理模块710，还被配置为响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值的比值小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为确定源接入网设备的第一AI算力因素值和目标接入网设备的第二AI算力因素值；确定所述第二AI算力因素值和所述第一AI算力因素值之间的算力因素差值；
所述处理模块710，还被配置为响应于所述算力因素差值等于第三阈值或所述算力因素差值大于所述第三阈值，以所述算力因素差值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于切换参数差值与所述算力因素差值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，其中，所述切换参数差值为所述目标接入网设备的第一基准切换参数与所述源接入网设备的第二基准切换参数之差；
或，
所述处理模块710，还被配置为响应于切换参数差值与所述算力因素差值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为确定源接入网设备的第一AI算力因素值和目标接入网设备的第二AI算力因素值；确定所述第二AI算力因素值和所述第一AI算力因素值之间的算力因素差值；响应于所述算力因素差值小于第三阈值或所述算力因素差值等于所述第三阈值，以所述算力因素差值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
在一个可选的实施例中，所述处理模块710，还被配置为响应于所述算力因素差值的相反数与切换参数差值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，其中，所述切换参数差值为所述目标接入网设备的第一基准切换参数与所述源接入网设备的第二基准切换参数之差；
或，
所述处理模块710，还被配置为响应于切换参数差值与所述算力因素差值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备。
在一个可选的实施例中，所述处理模块710，还被配置为根据预设转换方式对所述接入网设备的AI算力进行转换，得到所述AI算力因素值；
其中，所述转换方式包括线性转换、指数转换、对数转换中的至少一种。
综上所述，本实施例提供的设备切换装置，将接入网设备的AI算力因素值作为偏移参数，在确定是否从源接入网设备切换至目标接入网设备时，根据AI算力因素值对原确定过程进行偏移处理，当AI算力因素值表示AI算力上适合切换时，以正向偏移方向进行偏移从而完成设备切换的确定，同理，当AI算力因素表示AI算力上不适合切换时，以反向偏移方向进行偏移从而完成设备切换的确定，提高了设备切换过程中的准确率。
图8示出了本公开一个示例性实施例提供的终端的结构示意图，该终端包括：处理器801、接收器802、发射器803、存储器804和总线805。
处理器801包括一个或者一个以上处理核心，处理器801通过运行软件程序以及模块，从而执行各种功能应用以及信息处理。
接收器802和发射器803可以实现为一个通信组件，该通信组件可以是一块通信芯片。
存储器804通过总线805与处理器801相连。
存储器804可用于存储至少一个指令，处理器801用于执行该至少一个指令，以实现上述方法实施例中的各个步骤。
此外，存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，易失性或非易失性存储设备包括但不限于：磁盘或光盘，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，静态随时存取存储器(SRAM)，只读存储器(ROM)，磁存储器，快闪存储器，可编程只读存储器(PROM)。
在示例性实施例中，还提供了一种包括指令的非临时性计算机可读存储介质，例如包括指令的存储器，上述指令可由终端的处理器执行以完成上述设备切换方法中由终端侧执行的方法。例如，所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设 备等。
一种非临时性计算机可读存储介质，当所述非临时性计算机存储介质中的指令由终端的处理器执行时，使得终端能够执行上述设备切换方法。
图9是根据一示例性实施例示出的一种接入网设备900的框图。在一些实施例中，该接入网设备900是基站。
接入网设备900包括：处理器901、接收机902、发射机903和存储器904。接收机902、发射机903和存储器904分别通过总线与处理器901连接。
其中，处理器901包括一个或者一个以上处理核心，处理器901通过运行软件程序以及模块以执行本公开实施例提供的设备切换方法中接入网设备所执行的方法。存储器904可用于存储软件程序以及模块。具体的，存储器904可存储操作***941、至少一个功能所需的应用程序模块942。接收机902用于接收其他设备发送的通信数据，发射机903用于向其他设备发送通信数据。
本公开一示例性实施例还提供了一种通信***，所述***包括：终端和接入网设备；
所述终端包括如图7所示实施例提供的设备切换装置。
本公开一示例性实施例还提供了一种通信***，所述通信***包括：终端和接入网设备；
所述终端包括如图8所示实施例提供的终端；
所述接入网设备包括如图9所示实施例提供的接入网设备。
本公开一示例性实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有至少一条指令、至少一段程序、代码集或指令集，所述至少一条指令、所述至少一段程序、所述代码集或指令集由所述处理器加载并执行以实现上述各个方法实施例提供的设备切换方法中由终端或者接入网设备执行的步骤。
应当理解的是，在本文中提及的“多个”是指两个或两个以上。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。字符“/”一般表示前后关联对象是一种“或”的关系。
本领域技术人员在考虑说明书及实践这里公开的发明后，将容易想到本公 开的其它实施方案。本公开旨在涵盖本公开的任何变型、用途或者适应性变化，这些变型、用途或者适应性变化遵循本公开的一般性原理并包括本公开未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的，本公开的真正范围和精神由下面的权利要求指出。
应当理解的是，本公开并不局限于上面已经描述并在附图中示出的精确结构，并且可以在不脱离其范围进行各种修改和改变。本公开的范围仅由所附的权利要求来限制。

Claims (17)

  1. 一种设备切换方法，其特征在于，应用于终端设备中，所述方法包括：
    以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。
  2. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定源接入网设备的第一AI算力因素值；
    响应于所述第一AI算力因素值小于第一阈值或所述第一AI算力因素值等于所述第一阈值，以所述第一AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
  3. 根据权利要求2所述的方法，其特征在于，所述以所述第一AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况，包括：
    响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的参考信号接收质量RSRQ和参考信号接收功率RSRP中的至少一种；
    或，
    响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
    或，
    响应于所述第一AI算力因素值相反数与所述源接入网设备的第二基准切换参数之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值的比值小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  4. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定源接入网设备的第一AI算力因素值；
    响应于所述第一AI算力因素值等于第一阈值或所述第一AI算力因素值大于所述第一阈值，以所述第一AI算力因素值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
  5. 根据权利要求4所述的方法，其特征在于，所述以所述第一AI算力因素值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的设备切换情况，包括：
    响应于所述第一AI算力因素值相反数与所述目标接入网设备的第一基准切换参数之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述目标接入网设备的第一基准切换参数与所述第一AI算力因素值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第一AI算力因素值之积小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  6. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定目标接入网设备的第二AI算力因素值；
    响应于所述第二AI算力因素值小于第二阈值或所述第二AI算力因素值等于所述第二阈值，以所述第二AI算力因素值为反向偏移参数，确定在所述源接 入网设备和目标接入网设备之间的所述设备切换情况。
  7. 根据权利要求6所述的方法，其特征在于，所述以所述第二AI算力因素值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的设备切换情况，包括：
    响应于所述第二AI算力因素值相反数与所述目标接入网设备的第一基准切换参数之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值之积小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  8. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定目标接入网设备的第二AI算力因素值；
    响应于所述第二AI算力因素值等于第二阈值或所述第二AI算力因素值大于所述第二阈值，以所述第二AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
  9. 根据权利要求8所述的方法，其特征在于，所述以所述第二AI算力因素值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况，包括：
    响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第一基准切换参数包括所述目标接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述目标接入网设备的第一基准切换参数与所述第二AI算力因素值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备；
    或，
    响应于所述第二AI算力因素值相反数与所述源接入网设备的第二基准切换参数之和小于切换阈值，由所述源接入网设备切换至所述目标接入网设备，所述第二基准切换参数包括所述源接入网设备的RSRQ和RSRP中的至少一种；
    或，
    响应于所述源接入网设备的第二基准切换参数与所述第二AI算力因素值的比值小于切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  10. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定源接入网设备的第一AI算力因素值和目标接入网设备的第二AI算力因素值；
    确定所述第二AI算力因素值和所述第一AI算力因素值之间的算力因素差值；
    响应于所述算力因素差值等于第三阈值或所述算力因素差值大于所述第三阈值，以所述算力因素差值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
  11. 根据权利要求10所述的方法，其特征在于，所述以所述算力因素差值为正向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况，包括：
    响应于切换参数差值与所述算力因素差值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，其中，所述切换参数差值为所述目标接入网设备的第一基准切换参数与所述源接入网设备的第二基准切换参数之差；
    或，
    响应于切换参数差值与所述算力因素差值之积达到切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  12. 根据权利要求1所述的方法，其特征在于，所述以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况，包括：
    确定源接入网设备的第一AI算力因素值和目标接入网设备的第二AI算力因素值；
    确定所述第二AI算力因素值和所述第一AI算力因素值之间的算力因素差值；
    响应于所述算力因素差值小于第三阈值或所述算力因素差值等于所述第三阈值，以所述算力因素差值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况。
  13. 根据权利要求12所述的方法，其特征在于，所述以所述算力因素差值为反向偏移参数，确定在所述源接入网设备和目标接入网设备之间的所述设备切换情况，包括：
    响应于所述算力因素差值的相反数与切换参数差值之和达到切换阈值，由所述源接入网设备切换至所述目标接入网设备，其中，所述切换参数差值为所述目标接入网设备的第一基准切换参数与所述源接入网设备的第二基准切换参数之差；
    或，
    响应于切换参数差值与所述算力因素差值的比值达到切换阈值，由所述源接入网设备切换至所述目标接入网设备。
  14. 根据权利要求1至13任一所述的方法，其特征在于，所述方法还包括：
    根据预设转换方式对所述接入网设备的AI算力进行转换，得到所述AI算力因素值；
    其中，所述转换方式包括线性转换、指数转换、对数转换中的至少一种。
  15. 一种设备切换装置，其特征在于，应用于终端设备中，所述装置包括：
    处理模块，被配置为以接入网设备的AI算力因素值作为偏移参数，确定在所述接入网设备之间的设备切换情况。
  16. 一种终端，其特征在于，所述终端包括：
    处理器；
    与所述处理器相连的收发器；
    用于存储所述处理器的可执行信令的存储器；
    其中，所述处理器被配置为加载并执行可执行指令以实现如权利要求1至14任一所述的设备切换方法。
  17. 一种计算机可读存储介质，所述计算机可读存储介质中存储有至少一条指令、至少一段程序、代码集或指令集，所述至少一条指令、所述至少一段程序、所述代码集或所述指令集由处理器加载并执行以实现如权利要求1至14任一所述的设备切换方法。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023160560A1 (zh) * 2022-02-25 2023-08-31 华为技术有限公司 一种切换方法及相关装置

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110059741A1 (en) * 2009-09-10 2011-03-10 At&T Mobility Ii Llc Predictive hard and soft handover
CN105874848A (zh) * 2013-12-31 2016-08-17 微软技术许可有限责任公司 混合网络中的呼叫切换发起
US20200092750A1 (en) * 2018-09-14 2020-03-19 Cable Television Laboratories, Inc. Wireless access point enhancements for session transfer, mirroring, and mobility
US20200112899A1 (en) * 2018-10-08 2020-04-09 Reliance Jio Infocomm Limited System and method of handover

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105191414B (zh) * 2013-03-15 2019-02-19 日本电气株式会社 基站装置和切换控制方法
US10623070B1 (en) * 2019-04-29 2020-04-14 Sprint Spectrum L.P. Base station selection based on quantity of MIMO layers supported

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110059741A1 (en) * 2009-09-10 2011-03-10 At&T Mobility Ii Llc Predictive hard and soft handover
CN105874848A (zh) * 2013-12-31 2016-08-17 微软技术许可有限责任公司 混合网络中的呼叫切换发起
US20200092750A1 (en) * 2018-09-14 2020-03-19 Cable Television Laboratories, Inc. Wireless access point enhancements for session transfer, mirroring, and mobility
US20200112899A1 (en) * 2018-10-08 2020-04-09 Reliance Jio Infocomm Limited System and method of handover

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
See also references of EP4178256A4 *
ZTE: "Smart Grid Method for SON", 3GPP DRAFT; R3-190269_SMART GRID METHOD FOR SON, 3RD GENERATION PARTNERSHIP PROJECT (3GPP), MOBILE COMPETENCE CENTRE ; 650, ROUTE DES LUCIOLES ; F-06921 SOPHIA-ANTIPOLIS CEDEX ; FRANCE, vol. RAN WG3, no. Athens, Greece; 20190225 - 20190301, 15 February 2019 (2019-02-15), Mobile Competence Centre ; 650, route des Lucioles ; F-06921 Sophia-Antipolis Cedex ; France , XP051604212 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023160560A1 (zh) * 2022-02-25 2023-08-31 华为技术有限公司 一种切换方法及相关装置

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