Communication methods, devices, apparatuses, chips, storage media and software products

Abstract

This application relates to communication methods, devices, apparatuses, chips, storage media, and program products. In this method, a terminal device receives a first configuration indicating one or more data collection tasks for training an RRM measurement prediction model, and these tasks are associated with one or more events. Based on the first configuration, the terminal device generates one or more data logs, each corresponding to at least one of the data collection tasks. The terminal device sends a first message indicating the generation of the one or more data logs. In this manner, the terminal device can perform event-triggered data collection according to network configuration and report the collected data information to the network side, which helps the network side update the RRM measurement prediction model training and optimize model performance.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically to communication methods, communication devices, communication apparatuses, computer-readable storage media, and computer program products for radio resource management (RRM) measurement and prediction. Background Technology

[0002] RRM measurement refers to the process by which network and terminal devices continuously collect, analyze, and evaluate information about the wireless environment and resource status. This information supports key functions such as resource scheduling, interference management, handover decisions, and load balancing, ensuring efficient utilization of the wireless network and a positive user experience. Currently, RRM measurement prediction based on artificial intelligence / machine learning (AI / ML) predictive models has been identified as an AI / ML mobility management use case. However, the implementation of data collection for training RRM measurement prediction models is still imperfect and requires further research. Summary of the Invention

[0003] In view of the above problems, the embodiments of this application aim to provide a communication scheme to improve the data collection process for training the RRM measurement prediction model and improve the performance of the RRM measurement prediction model.

[0004] According to a first aspect of the embodiments of this application, a communication method is provided. The method can be executed by a terminal device. The method includes: receiving a first configuration indicating one or more data collection tasks, the one or more data collection tasks being used for training an RRM measurement prediction model, and the one or more data collection tasks being associated with one or more events; generating one or more data logs based on the first configuration, the one or more data logs corresponding to at least one of the one or more data collection tasks; and sending a first message indicating the generation of the one or more data logs.

[0005] According to a second aspect of the embodiments of this application, a communication method is provided. The method can be performed by a network device. The method includes: sending a first configuration indicating one or more data collection tasks for training an RRM measurement prediction model, and the one or more data collection tasks being associated with one or more events; and receiving a first message indicating the generation of one or more data logs, the one or more data logs corresponding to at least one of the one or more data collection tasks.

[0006] According to a third aspect of the embodiments of this application, a communication apparatus is provided. The apparatus includes: a receiving component configured to receive a first configuration, the first configuration indicating one or more data collection tasks, the one or more data collection tasks being used for training an RRM measurement prediction model, and the one or more data collection tasks being associated with one or more events; a processing component configured to generate one or more data logs based on the first configuration, the one or more data logs corresponding to at least one of the one or more data collection tasks; and a sending component configured to send a first message indicating the generation of the one or more data logs.

[0007] According to a fourth aspect of the embodiments of this application, a communication apparatus is provided. The apparatus includes: a transmitting component configured to transmit a first configuration indicating one or more data collection tasks for training an RRM measurement prediction model, and the one or more data collection tasks being associated with one or more events; and a receiving component configured to receive a first message indicating the generation of one or more data logs, the one or more data logs corresponding to at least one of the one or more data collection tasks.

[0008] According to a fifth aspect of the embodiments of this application, a communication device is provided. The device includes a processor and a memory, the memory including computer program code that, when executed by the processor, causes the method described according to the first or second aspect to be performed.

[0009] According to a sixth aspect of the embodiments of this application, a chip is provided. The chip includes a processor connected to a memory located inside or outside the chip, the memory being used to store a computer program, and the processor being used to call and run the computer program from the memory to cause the method according to the first aspect or the second aspect to be executed.

[0010] According to a seventh aspect of the embodiments of this application, a computer-readable storage medium is provided. The computer-readable storage medium includes machine-executable instructions that, when executed by a device, cause the method described according to the first or second aspect to be performed.

[0011] According to an eighth aspect of the embodiments of this application, a computer program product is provided. The computer program product includes computer program code that, when executed by a device, causes the method described according to the first or second aspect to be performed.

[0012] As will be understood from the following description of exemplary embodiments, according to the technical solution presented herein, by configuring one or more data collection tasks for training an RRM measurement prediction model, and these data collection tasks being associated with one or more events, it is possible to collect data for optimizing the RRM measurement prediction model under specific conditions. By generating one or more data logs corresponding to the one or more data collection tasks and reporting the generation of these data logs, it is possible to help the network side update the training data of the RRM measurement prediction model, thereby updating the RRM measurement prediction model training, achieving iteration of the RRM measurement prediction model, and improving the performance of the RRM measurement prediction model.

[0013] It should be understood that the description in the Summary Section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will be readily understood from the following description. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0015] Figure 1 A schematic diagram of an example communication system that may be implemented in accordance with embodiments of this application is shown;

[0016] Figure 2A A schematic diagram illustrating RRM measurement prediction according to some embodiments of this application is shown;

[0017] Figure 2B A schematic diagram illustrating another RRM measurement prediction according to some embodiments of this application is shown;

[0018] Figure 2C A schematic diagram of another RRM measurement prediction according to some embodiments of this application is shown;

[0019] Figure 3 A schematic diagram of an example communication process according to some embodiments of this application is shown;

[0020] Figure 4 A flowchart illustrating a communication method implemented at a terminal device according to some embodiments of this application is shown;

[0021] Figure 5 A flowchart illustrating a communication method implemented at a network device according to some embodiments of this application is shown;

[0022] Figure 6 A schematic block diagram of an example communication device according to some embodiments of this application is shown;

[0023] Figure 7 A schematic block diagram of another example communication device according to some embodiments of this application is shown;

[0024] Figure 8 A simplified block diagram of a device suitable for implementing embodiments of this application is shown. Detailed Implementation

[0025] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0026] The term "terminal device" as used herein refers to any terminal device capable of wireless communication. By way of example and not limitation, a terminal device may also be referred to as a communication device, UE, user station (SS), portable user station, mobile station (MS), or access terminal (AT). Terminal devices may include, but are not limited to, mobile phones, smartphones, Voice over Internet Protocol (VoIP) phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image acquisition terminal devices such as digital cameras, gaming terminal devices, music storage and playback devices, in-vehicle wireless terminal devices, wireless endpoints, mobile stations, laptop embedded equipment (LEE), laptop mounted equipment (LME), universal serial bus (USB) dongles, smart devices, wireless customer premises equipment (CPE), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (such as remote surgery), industrial devices and applications (such as robots and / or other wireless devices in industrial and / or automated processing chain contexts), consumer electronics devices, and devices operating on commercial and / or industrial wireless networks. In the following description, the terms “terminal equipment”, “communication equipment”, “terminal”, “user equipment” and “UE” are used interchangeably.

[0027] In this document, the term "network device" refers to a node in a communication network, including radio access network (RAN) nodes (also known as RAN devices) and / or core network (CN) nodes (also known as CN devices). The term "network side" can refer to the RAN device and / or CN device side.

[0028] The term "RAN equipment" refers to a RAN node through which terminal devices access the network and receive services. Depending on the terminology and technology used, network equipment can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), a New Radio (NR) NB (also known as a gNB), a remote radio unit (RRU), a remote radiohead (RRH), a relay, a low-power node such as a pico or femtocell, and so on. In some embodiments, the BS or AP can be mobile, such as a satellite associated with a non-terrestrial network.

[0029] RAN equipment can be implemented as a central unit (CU) - distributed unit (DU) separation architecture. This CU-DU separation architecture can include one CU and one or more DUs. It should be understood that a CU can also be called a gNB-CU, and a DU can also be called a gNB-DU. The CU carries the radio resource control (RRC) layer, the service data adaptation protocol (SDAP) layer, and the packet data convergence protocol (PDCP). The DU carries the radio link control (RLC) layer, the medium access control (MAC) layer, and the physical (PHY) layer. The CU controls the one or more DUs. Of course, RAN equipment can also be implemented as a non-separated architecture.

[0030] The term "CN device" refers to a CN node, which terminal devices can access and receive services from via RAN devices. A CN device can provide one or more CN functions, such as access and mobility management (AMF), location management (LMF), application function (AF), ambient Internet of Things (A-IoT) function (AIOTF), network exposure function (NEF), authentication server function (AUSF), unified data management (UDM), session management function (SMF), and user plane function (UPF). It should be understood that a CN device can also provide any other known or future-developed CN functions.

[0031] In this document, the term "communication device" refers to a device that enables the functionality of a terminal device or network device. A communication device can be the terminal device or network device itself, or it can be a component of the terminal device or network device, such as a chip. A chip can be, for example, a system-on-a-chip (SoC), a modem, etc.

[0032] The term "comprising" or similar expressions in this document mean open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. The term "and / or" means at least one of the two items associated with it. For example, "a and / or b" means a, b, or "a and b". The character " / " generally indicates that the related objects are in an "or" relationship. The term "at least one" means one or more items. The term "at least one of the following" or similar expressions mean any combination of these items, including any combination of single items or multiple items. For example, "at least one of a, b, c" may mean a, b, c, "a and b", "a and c", "b and c", or "a, b and c". Other terms will be defined in the description below.

[0033] The current 3GPP (Third Generation Partnership Project) protocol does not support the collection and triggering configuration of training data for AI / ML prediction models in its RRM (Real-Time Measurement) and configuration methods. However, how data collection is implemented directly impacts model performance. For example, in the original training data, the amount of data for a specific case may be insufficient, resulting in poor model output in that case. Performing extensive data collection for this reason would increase radio resource overhead. Therefore, how data collection is implemented under specific conditions directly affects the overall performance of the RRM measurement prediction model.

[0034] In view of this, embodiments of this application propose a communication scheme. In this scheme, a network device can send a first configuration to a terminal device, the first configuration indicating one or more data collection tasks, which are used for training an RRM measurement prediction model, and which are associated with one or more events. Based on the first configuration, the terminal device can generate one or more data logs, which correspond to at least one of the one or more data collection tasks. The terminal device can send a first message to the network device, the first message indicating the generation of one or more data logs.

[0035] Therefore, the terminal device can collect data triggered by specific events according to the network configuration and report the information of the collected data to the network side. Based on the received information of the collected data, the network side can obtain and update the training data of the RRM measurement prediction model, realize the update of the RRM measurement prediction model training, thereby realizing the iteration of the RRM measurement prediction model and improving the performance of the RRM measurement prediction model.

[0036] The principles and implementation of this solution will be described in detail below with reference to the accompanying drawings.

[0037] Figure 1 A schematic diagram of an example communication system 100 that may be implemented according to embodiments of this application is shown. Figure 1 As shown, the communication system 100 may include at least one terminal device ( Figure 1 The diagram shows terminal devices 110-1 and 110-2 (hereinafter referred to as terminal device 110 for convenience) and at least one RAN device ( Figure 1 The diagram shows RAN devices 120-1 and 120-2, which, for convenience, will be collectively referred to as RAN device 120 below. RAN device 120 can provide one or more cells ( Figure 1 The diagram shows cell 121) used to serve one or more terminal devices.

[0038] Terminal device 110 can connect to RAN device 120 wirelessly. Terminal devices 110-1 and 110-2 can connect via wired or wireless means. RAN devices 120-1 and 120-2 can connect via wired or wireless means.

[0039] like Figure 1 As shown, the communication system 100 may further include a CN 130. The terminal device 110 can communicate with one or more CN devices (not shown) in the CN 130 via the RAN device 120. The RAN device 120 can be connected to the CN 130 wirelessly or via a wired connection. The RAN device 120 can be implemented as a physical device independent of the CN devices, or it can be implemented as a physical device integrating some of the functions of the CN devices.

[0040] It should be understood that Figure 1 The number and type of terminal devices or network devices described are merely examples and do not imply any limitation on this application. Communication system 100 may involve any suitable number of terminal devices and / or network devices and / or cells suitable for implementing embodiments of this application.

[0041] The communication in communication system 100 can conform to any suitable communication standard, including but not limited to Global System for Mobile Communications (GSM), Long Term Evolution (LTE), LTE Evolution, LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), GSM EDGE Radio Access Network (GERAN), Machine Type Communication (MTC), etc. Furthermore, communication between terminal equipment and network equipment can be performed according to any suitable generation communication protocol, including but not limited to fourth-generation (4G), 5G, sixth-generation (6G) communication protocols, or other existing or future suitable communication protocols.

[0042] It should be noted that the embodiments of this application can be applied to various suitable communication systems. Considering the rapid development of communication technology, there will naturally be future types of communication technologies and systems, which this application may incorporate. Communication system 100 is merely an example and does not imply that the scope of this application is limited to a specific system.

[0043] As mentioned earlier, RRM measurement refers to the process by which network devices and terminal devices continuously collect, analyze, and evaluate wireless environment and resource status information to support key functions such as resource scheduling, interference management, handover decisions, and load balancing, thereby ensuring efficient utilization of the wireless network and a good user experience. In RRM measurement, terminal devices can measure received signals and periodically / event-triggeredly report the measurement results via the uplink channel. Reference signals used for RRM measurement mainly include the Channel State Information Reference Signal (CSI-RS) and the Synchronization Signal Block (SSB). Network devices configure a measurement object identifier for each measurement object and configure relevant measurement object information, such as signal frequency, subcarrier spacing, and physical cell identifier (PCI), to the terminal devices through the information element (IE) meansObjectiveToAddModList in the measurement configuration MeasConfig. The data reporting type can be configured as periodic reporting or event-triggered reporting. In the reporting of new radio (NR) events, the event type (such as A1, A2, A3, etc.), trigger threshold, and trigger duration must be configured in the IE EventTriggerConfig under IE ReportConfigToAddModList in the measurement configuration MeasConfig, as well as other information. Once the configured time is triggered, the terminal device then reports the measured data to the network.

[0044] In a recent detailed discussion on the application of AI / ML in mobility management, use cases for AI / ML in mobility management have been identified. In these use cases, AI / ML predictive models can be deployed on the end-device side and can employ time-domain measurement prediction methods. The following section combines... Figure 2A , Figure 2B and Figure 2C Some examples describing time-domain measurement prediction methods.

[0045] Figure 2AA schematic diagram of an RRM measurement prediction 200A according to some embodiments of this application is shown. This RRM measurement prediction 200A is an example of a time-domain use case A for co-frequency measurement. Figure 2A As shown, continuous historical measurements in the observation window (OW) are used to predict continuous measurements in the prediction window (PW). Then, OW and PW slide forward in units of sampling period or measurement period, where actual measurements are taken before sliding.

[0046] Figure 2B A schematic diagram of another RRM measurement prediction 200B according to some embodiments of this application is shown. This RRM measurement prediction 200B is an example 1 of an omitted mode of the same-frequency measurement time-domain use case B. Figure 2B As shown, continuous historical measurements in the Open Window (OW) are used to predict continuous measurements in the Closed Window (PW). Then, the OW and PW slide forward in units of sampling or measurement periods, omitting actual measurements from previous PW measurements during the window sliding process.

[0047] Figure 2C A schematic diagram of yet another RRM measurement prediction 200C according to some embodiments of this application is shown. This RRM measurement prediction 200C is an example 2 of an omitted mode of the same-frequency measurement time-domain use case B. Figure 2C As shown, a measurement result in the PW is predicted using discontinuous historical measurements in the OW. The OW and PW are then slid forward in units of sampling periods or measurement periods, where the actual measurement is performed before the sliding.

[0048] In general, the terminal device can receive the reference signal of the measured object in the operating system (OW), use multiple real measurement results as model inputs, and predict the reference signal measurement results (e.g., reference signal receiving power (RSRP), reference signal receiving quality (RSRQ), signal-to-interference plus noise ratio (SINR), etc.) within a future period (PW) through an AI / ML model. This allows for advance knowledge of the signal strength of the measured object within a future timeframe.

[0049] Typically, the RRM measurement prediction model on a terminal device needs to be updated. The network side is responsible for updating and iterating the model, which requires the terminal device to collect real training data and send it to the network side to update the model.

[0050] Currently, the consensus reached on data collection is as follows: (1) The network can configure terminal devices to store measurement data; (2) When the storage memory reaches a certain amount, the terminal device will report relevant instruction messages; (3) Support data measurement and storage at the layer 3 (L3) cell level, L3 beam level, and layer 1 (L1) filter beam level.

[0051] However, it is still unclear how to achieve data collection in specific situations.

[0052] Therefore, this application provides a communication scheme to collect data under specific conditions based on network configuration, thereby completing the collection of specific data samples and helping the network side improve the performance of the RRM measurement prediction model. The following will combine... Figure 3 This plan will be described in more detail.

[0053] Figure 3 A schematic diagram of an example communication process 300 according to some embodiments of this application is shown. The communication process 300 may involve a terminal device 301 and a network device 302. The terminal device 301 may be implemented as follows: Figure 1 The terminal device 110 and network device 302 shown can be implemented as follows: Figure 1 The RAN device 120 and / or CN device are shown. It should be understood that the communication process 300 can be executed between the terminal device 301 and the network device 302, or between a communication device supporting the functionality of the terminal device 301 and the network device 302, or between the terminal device 301 and the communication device supporting the functionality of the network device 302, or between the communication device supporting the functionality of the terminal device 301 and the communication device supporting the functionality of the network device 302. For example, the communication device can be a chip such as a SoC, modem, etc.

[0054] like Figure 3 As shown, in step 310, network device 302 may send signaling for capability query to terminal device 301. In some embodiments, when terminal device 301 has just accessed the network, network device 302 may send the signaling for capability query to terminal device 301. Accordingly, terminal device 301 may receive the signaling for capability query.

[0055] In some embodiments, the capability query may include whether the terminal device 301 has the capability to deploy an RRM measurement prediction model and / or collect data. It should be understood that the capability query may also include other suitable capability query information, and this application embodiment does not limit this.

[0056] In step 320, terminal device 301 may send signaling for capability reporting to network device 302. Correspondingly, network device 302 may receive the signaling for capability reporting. In some embodiments, when terminal device 301 possesses the capability to deploy an RRM measurement prediction model and / or the capability to collect data, terminal device 301 may inform network device 302 of its RRM measurement prediction model deployment capability and / or data collection capability in the signaling for capability reporting. It should be noted that steps 310 and 320 are optional.

[0057] In step 330, network device 302 may send a first configuration to terminal device 301, which may indicate one or more data collection tasks. Correspondingly, terminal device 301 may receive the first configuration. These one or more data collection tasks may be used for training an RRM measurement prediction model, and may be associated with one or more events. Through this first configuration, the data collection function of the RRM measurement prediction model deployed on terminal device 301 can be realized.

[0058] In some embodiments, a data collection task may be associated with one event. In some embodiments, a data collection task may be associated with multiple events. In some embodiments, any two or more data collection tasks may be associated with the same event. In some embodiments, each data collection task may be associated with a different event.

[0059] In some embodiments, the first configuration may include a configuration list of the one or more data collection tasks and a configuration list of the one or more events. The configuration list of the one or more data collection tasks may indicate the configuration of each of the one or more data collection tasks. The configuration list of the one or more events may indicate the configuration of each of the one or more events.

[0060] In some embodiments, the first configuration may be housed within the measurement configuration IE MeasConfig. In other embodiments, the first configuration may be housed within an IE at the same level as the measurement configuration IE MeasConfig. For example, assuming the first configuration is represented by IELoggedMeassurementConfig, IE LoggedMeassurementConfig can be added to the measurement configuration IE MeasConfig, or it can be added as an IE at the same level as the measurement configuration IE MeasConfig.

[0061] For example, the first configuration can be described as follows: LoggedMeassurementConfig ::= SEQUENCE { LoggedTaskConfigToAddModList SEQUENCE (SIZE (1...)) OFLoggedTaskConfigToAddMod, LoggedEventConfigToAddModList SEQUENCE (SIZE (1...)) OFLoggedEventConfigToAddMod, ... } Wherein, `LoggedMeassurementConfig` represents the first configuration, `LoggedTaskConfigToAddModList` represents the configuration list for data collection tasks, `LoggedTaskConfigToAddMod` represents the configuration for data collection tasks, `LoggedEventConfigToAddModList` represents the configuration list for events, and `LoggedEventConfigToAddMod` represents the configuration for events. It should be understood that this first configuration can also be implemented in other forms.

[0062] In some embodiments, the configuration of a data collection task may include a configuration identifier for that data collection task. This configuration identifier is used to distinguish different data collection tasks. It should be understood that the configuration identifier can take any format that can uniquely identify the data collection task.

[0063] In some embodiments, the configuration of a data collection task may include the cache size of the data log corresponding to the data collection task. In some embodiments, the cache size of the data log may be used to indicate the maximum storage space (e.g., 128M, etc.) that the terminal device 301 can collect when it performs data collection after triggering the data collection task. It should be understood that the cache size can be set to any suitable value as needed.

[0064] In some embodiments, the configuration of a data collection task may include configuration identifiers for one or more events corresponding to the data collection task. These configuration identifiers are used to distinguish different event configurations. It should be understood that the configuration identifiers may adopt any format capable of uniquely identifying the event configuration. In some embodiments, the data collection task is triggered when all one or more events corresponding to a data collection task are triggered.

[0065] In some embodiments, the configuration of a data collection task may include the type of data log corresponding to the data collection task. In some embodiments, the type of data log may indicate the specific data type recorded and collected after the data collection task is triggered. For example, the data type may be a signal quality parameter such as RSRP, RSRQ, or SINR. It should be understood that the data type may also include other types of measurement parameters, and this application embodiment does not limit this.

[0066] It should be understood that the configuration of a data collection task can include any combination of the above information.

[0067] For example, the configuration of a data collection task can be described as follows: LoggedTaskConfigToAddMod ::= SEQUENCE { loggedTaskConfigId INTEGER (...), loggedBufferSize ENUMERATED (...), loggedTaskExecutionConfig SEQUENCE (SIZE (1...)) OFLoggedEventConfigId, loggedQuantity CHOICE { rsrp, rsrq, sinr } ... } In this configuration, `LoggedTaskConfigToAddMod` represents the configuration of the data collection task, `loggedTaskConfigId` represents the configuration identifier of the data collection task, `loggedBufferSize` represents the buffer size of the data log corresponding to the data collection task, `loggedTaskExecutionConfig` represents the list of event configuration identifiers required to trigger the data collection task, `LoggedEventConfigId` represents the configuration identifier of one or more events corresponding to the data collection task, and `loggedQuantity` represents the type of data log corresponding to the data collection task. It should be understood that the configuration of the data collection task is not limited to the above example and other suitable forms can also be used.

[0068] In some embodiments, the configuration of an event may include a configuration identifier for the event. In some embodiments, the configuration of an event may include a set of parameters associated with the triggering of the event. It should be understood that the configuration of an event may include a combination of the above information.

[0069] For example, the configuration of an event can be described as follows: loggedEventConfigToAddMod ::= SEQUENCE { loggedEventConfigId LoggedEventConfigId, loggedEventConfig LoggedEventConfig, ... } LoggedEventConfigId ::= INTEGER (...) LoggedEventConfig ::= SEQUENCE { LoggedEvent CHOICE { LoggedEventA1, LoggedEventA2, LoggedEventA3, ... } } In this configuration, `loggedEventConfigToAddMod` represents the event configuration, `loggedEventConfigId` represents the event configuration identifier, `loggedEventConfig` represents the event configuration list, `LoggedEvent` represents the event configuration parameters (i.e., the set of parameters associated with the event trigger), and `LoggedEventA1`, `LoggedEventA2`, and `LoggedEventA3` represent specific triggering events (i.e., different event types). It should be understood that various event options can be added to restrict the data type of a specific sample. Each event can be bound to a `loggedEventConfigId`, which identifies the configuration of each event. It should also be understood that event configuration is not limited to the above example and can use other suitable forms.

[0070] In some embodiments, the set of parameters associated with the triggering of an event may include at least one of the following: a threshold, a time-to-trigger (TTT), a hysteresis value, or one or more measurement object identifiers. This set of parameters is used to determine the triggering of the event (e.g., using a threshold, TTT, and / or a hysteresis value), and is associated with the event type (e.g., whether it involves one or more measurement object identifiers).

[0071] In some embodiments, an event can be associated with one or more measurement objects. In some embodiments, an event can be associated with the measurement result of one or more parameters. That is, the event can be associated with the actual measurement data of the one or more parameters. In some embodiments, an event can be associated with the predicted result of one or more parameters. That is, the event can be associated with the predicted data of the one or more parameters.

[0072] In some embodiments, the above-mentioned one or more parameters may indicate the signal quality of the terminal device 301. For example, the above-mentioned one or more parameters may include signal quality parameters such as RSRP, RSRQ, SNIR, etc. In some embodiments, the above-mentioned one or more parameters may indicate the moving speed of the terminal device 301. For example, the above-mentioned one or more parameters may include speed parameters such as absolute speed, relative speed, etc. In some embodiments, the above-mentioned one or more parameters may indicate the area where the terminal device 301 is located. For example, the above-mentioned one or more parameters may include area identifiers such as the identifier of the cell where the terminal device 301 is camped, the identifier of the RAN notification area (RNA) where the terminal device 301 is located, etc. It should be understood that the above-mentioned one or more parameters may include any combination of the above information.

[0073] For example, an event can be defined based on the measurement results and / or prediction results of one or more parameters of one or more measured objects. In some embodiments, the event is triggered when a condition (also referred to herein as a first condition) is met or when the duration of the condition being met exceeds the TTT. The condition relates to the measurement results and / or prediction results of one or more parameters of one or more measured objects, and to the set of parameters mentioned above. For illustration, some examples of conditions, events, or event types are given below.

[0074] For example, event type 1 can be defined: triggered based on actual measurement data of a single measurement object. The characteristics of event type 1 include: the number of measurement objects is 1; and the event trigger is determined using actual measurement data.

[0075] As an example of event type 1, event A1 can be defined as follows: the difference between the measurement result of one or more parameters of a measurement object and the hysteresis value is greater than a first threshold, and the duration exceeds TTT.

[0076] For example, the configuration for event A1 can be described as follows: LoggedEventA1 SEQUENCE { logged-A1-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, } Where LoggedEventA1 represents the configuration of event A1, logged-A1-Threshold represents the first threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier. It should be noted that the measurement object identifier in this article can reuse existing RRM measurement object IDs, i.e.: MeasConfig→MeasObjctToAddMod→MeasObjectId. Of course, the measurement object identifier in this article can also be newly defined.

[0077] For example, the triggering condition for event A1 can be expressed as the following formula (1): MeasValue–loggedHysteresis>logged-A1-Threshold (1) Where MeasValue represents the parameter measurement result of the measured object, loggedHysteresis represents the hysteresis value, and logged-A1-Threshold represents the first threshold. Event A1 is triggered if and only if the duration of the parameter measurement result of the measured object satisfying the above equation (1) exceeds TTT.

[0078] As another example of event type 1, event A2 can be defined: the sum of the measurement results of one or more parameters of a measurement object and the hysteresis value is less than a second threshold, and the duration exceeds TTT.

[0079] For example, the configuration for event A2 can be described as follows: LoggedEventA2 SEQUENCE { logged-A2-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA2 represents the configuration of event A2, logged-A2-Threshold represents the second threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier.

[0080] For example, the triggering condition for event A2 can be expressed as the following formula (2): MeasValue+loggedHysteresis <logged-A2-Threshold (2) Where MeasValue represents the parameter measurement result of the measured object, loggedHysteresis represents the hysteresis value, and logged-A2-Threshold represents the second threshold. Event A2 is triggered only if the duration of the parameter measurement result of the measured object satisfying the above equation (2) exceeds TTT.

[0081] For example, event type 2 can be defined: triggered based on real measurement data from multiple measurement objects. The characteristics of event type 2 include: the number of measurement objects is greater than or equal to two; and the event trigger is determined using real measurement data.

[0082] As an example of event type 2, event A3 can be defined: the sum of the difference between the measurement results of one or more parameters of two measured objects and the hysteresis value is less than a third threshold, and the duration exceeds TTT.

[0083] For example, the configuration for event A3 can be described as follows: LoggedEventA3 SEQUENCE { logged-A3-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId1 MeasObjectId1, measObjectId2 MeasObjectId2, ... } Where LoggedEventA3 represents the configuration of event A3, logged-A3-Threshold represents the third threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId1 and measObjectId2 represent the measurement object identifiers of two specific measurement objects.

[0084] For example, the triggering condition for event A3 can be expressed as the following formula (3): MeasValue1–MeasValue2+loggedHysteresis <logged-A3-Threshold (3) Where MeasValue1 represents the parameter measurement result of the measurement object measObjectId1, MeasValue2 represents the parameter measurement result of the measurement object measObjectId2, loggedHysteresis represents the hysteresis value, and logged-A3-Threshold represents the third threshold. Event A3 is triggered only if the duration of the parameter measurement results of the two measurement objects satisfying the above equation (3) exceeds TTT.

[0085] As another example of event type 2, event A4 can be defined: the difference between the measurement results of one or more parameters of two measured objects and the difference between the hysteresis value is greater than the fourth threshold, and the duration exceeds TTT.

[0086] For example, the configuration for event A4 can be described as follows: LoggedEventA4 SEQUENCE { logged-A4-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId1 MeasObjectId1, measObjectId2 MeasObjectId2, ... } Where LoggedEventA4 represents the configuration of event A4, logged-A4-Threshold represents the fourth threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId1 and measObjectId2 represent the measurement object identifiers of two specific measurement objects.

[0087] For example, the triggering condition for event A4 can be expressed as the following formula (4): MeasValue1–MeasValue2–loggedHysteresis>logged-A4-Threshold (4) Where MeasValue1 represents the parameter measurement result of the measurement object measObjectId1, MeasValue2 represents the parameter measurement result of the measurement object measObjectId2, loggedHysteresis represents the hysteresis value, and logged-A4-Threshold represents the fourth threshold. Event A4 is triggered if and only if the duration of the parameter measurement results of the two measurement objects satisfying the above equation (4) exceeds TTT.

[0088] For example, event type 3 can be defined: triggered based on actual measurement data and predicted data for a single measurement object. The characteristics of event type 3 include: the number of measurement objects is one; and the event trigger is determined using actual measurement data and predicted data.

[0089] As an example of event type 3, event A5 can be defined: the sum of the difference between the measured result and the predicted result of one or more parameters of a measured object and the hysteresis value is less than the fifth threshold, and the duration exceeds TTT.

[0090] For example, the configuration for event A5 can be described as follows: LoggedEventA5 SEQUENCE { logged-A5-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA5 represents the configuration of event A5, logged-A5-Threshold represents the fifth threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier for this specific measurement object.

[0091] For example, the triggering condition for event A5 can be expressed as the following formula (5): MeasValue–PredValue+loggedHysteresis <logged-A5-Threshold (5) Where MeasValue represents the parameter measurement result of the measured object, PredValue represents the parameter prediction result of the measured object, loggedHysteresis represents the hysteresis value, and logged-A5-Threshold represents the fifth threshold. Event A5 is triggered if and only if the duration of the parameter measurement result and the parameter prediction result of the measured object satisfying the above equation (5) exceeds TTT.

[0092] As another example of event type 3, event A6 can be defined: the difference between the measured result and the predicted result of one or more parameters of a measured object and the difference between the hysteresis value is greater than the sixth threshold, and the duration exceeds TTT.

[0093] For example, the configuration for event A6 can be described as follows: LoggedEventA6 SEQUENCE { logged-A6-Threshold MeasTriggerQuantity, loggedHysteresis LoggedHysteresis, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA6 represents the configuration of event A6, logged-A6-Threshold represents the sixth threshold, loggedHysteresis represents the hysteresis value, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier for this specific measurement object.

[0094] For example, the triggering condition for event A6 can be expressed as the following formula (6): MeasValue–PredValue–loggedHysteresis>logged-A6-Threshold (6) Where MeasValue represents the parameter measurement result of the measured object, PredValue represents the parameter prediction result of the measured object, loggedHysteresis represents the hysteresis value, and logged-A6-Threshold represents the sixth threshold. Event A6 is triggered if and only if the duration of the parameter measurement result and parameter prediction result of the measured object satisfying the above formula (6) exceeds TTT.

[0095] It should be understood that equations (1) to (6) above are merely examples, and other forms are also possible. For example, equations (5) and (6) can also be written as: PredValue – MeasValue + loggedHysteresis<logged-A5-Threshold;PredValue–MeasValue–loggedHysteresis> logged-A6-Threshold. It should also be understood that event types 1 to 3 above are merely examples, and other event types can be defined similarly.

[0096] Preferably, the parameters in events A1 to A6 above can be the signal quality of the terminal device. Of course, other parameters are also possible, and the embodiments of this application are not limited to this.

[0097] In some embodiments, the hysteresis values ​​in events A1 to A6 above are optional. For example, as a variation of events A1 to A6, events A7 to A12 can be defined as follows.

[0098] Event A7: The measurement results of one or more parameters of a measurement object are all greater than the seventh threshold, and the duration exceeds TTT.

[0099] For example, the configuration for event A7 can be described as follows: LoggedEventA1 SEQUENCE { logged-A1-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA7 represents the configuration of event A7, logged-A7-Threshold represents the seventh threshold, loggedtimeToTrigger represents TTT, and measObjectId represents the identifier of the measurement object.

[0100] For example, the triggering condition for event A7 can be expressed as the following formula (7): MeasValue>logged-A7-Threshold (7) Where MeasValue represents the parameter measurement result of the measured object, and logged-A7-Threshold represents the seventh threshold. Event A7 is triggered only if the duration of the parameter measurement result of the measured object satisfying the above formula (7) exceeds TTT.

[0101] Event A8: The measurement results of one or more parameters of a measurement object are all less than the eighth threshold, and the duration exceeds TTT.

[0102] For example, the configuration for event A8 can be described as follows: LoggedEventA8 SEQUENCE { logged-A8-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA8 represents the configuration of event A8, logged-A8-Threshold represents the eighth threshold, loggedtimeToTrigger represents TTT, and measObjectId represents the identifier of the measurement object.

[0103] For example, the triggering condition for event A8 can be expressed as the following formula (8): MeasValue <logged-A8-Threshold (8) Where MeasValue represents the parameter measurement result of the measured object, and logged-A2-Threshold represents the second threshold. Event A8 is triggered only if the duration of the parameter measurement result of the measured object satisfying the above equation (8) exceeds TTT.

[0104] Event A9: The difference between the measurement results of one or more parameters of two measurement objects is less than the ninth threshold, and the duration exceeds TTT.

[0105] For example, the configuration for event A9 can be described as follows: LoggedEventA9 SEQUENCE { logged-A9-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId1 MeasObjectId1, measObjectId2 MeasObjectId2, ... } Where LoggedEventA9 represents the configuration of event A9, logged-A9-Threshold represents the ninth threshold, loggedtimeToTrigger represents TTT, and measObjectId1 and measObjectId2 represent the measurement object identifiers of two specific measurement objects.

[0106] For example, the triggering condition for event A9 can be expressed as the following formula (9): MeasValue1–MeasValue2 <logged-A9-Threshold (9) Where MeasValue1 represents the parameter measurement result of the measurement object measObjectId1, MeasValue2 represents the parameter measurement result of the measurement object measObjectId2, and logged-A9-Threshold represents the ninth threshold. Event A9 is triggered if and only if the duration of the parameter measurement results of the two measurement objects satisfying the above formula (9) exceeds TTT.

[0107] Event A10: The difference between the measurement results of one or more parameters of two measurement objects is greater than the tenth threshold, and the duration exceeds TTT.

[0108] For example, the configuration for event A10 can be described as follows: LoggedEventA10 SEQUENCE { logged-A10-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId1 MeasObjectId1, measObjectId2 MeasObjectId2, ... } Where LoggedEventA10 represents the configuration of event A10, logged-A10-Threshold represents the tenth threshold, loggedtimeToTrigger represents TTT, and measObjectId1 and measObjectId2 represent the measurement object identifiers of two specific measurement objects.

[0109] For example, the triggering condition for event A10 can be expressed as the following formula (10): MeasValue1–MeasValue2>logged-A4-Threshold (10) Where MeasValue1 represents the parameter measurement result of the measurement object measObjectId1, MeasValue2 represents the parameter measurement result of the measurement object measObjectId2, and logged-A10-Threshold represents the tenth threshold. Event A10 is triggered if and only if the duration of the parameter measurement results of the two measurement objects satisfying the above formula (10) exceeds TTT.

[0110] Event A11: The difference between the measured result and the predicted result of one or more parameters of a measured object is less than the eleventh threshold, and the duration exceeds TTT.

[0111] For example, the configuration for event A11 can be described as follows: LoggedEventA11 SEQUENCE { logged-A11-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA11 represents the configuration of event A11, logged-A11-Threshold represents the eleventh threshold, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier for this specific measurement object.

[0112] For example, the triggering condition for event A11 can be expressed as the following formula (11): MeasValue–PredValue <logged-A11-Threshold (11) Where MeasValue represents the parameter measurement result of the measured object, PredValue represents the parameter prediction result of the measured object, and logged-A11-Threshold represents the eleventh threshold. Event A11 is triggered if and only if the duration of the parameter measurement result and parameter prediction result of the measured object satisfying the above formula (11) exceeds TTT.

[0113] Event A12: The difference between the measured result and the predicted result of one or more parameters of a measured object is greater than the twelfth threshold, and the duration exceeds TTT.

[0114] For example, the configuration of event A12 can be described as follows: LoggedEventA12 SEQUENCE { logged-A12-Threshold MeasTriggerQuantity, loggedtimeToTrigger LoggedTimeToTrigger, measObjectId MeasObjectId, ... } Where LoggedEventA12 represents the configuration of event A12, logged-A12-Threshold represents the twelfth threshold, loggedtimeToTrigger represents TTT, and measObjectId represents the measurement object identifier for this specific measurement object.

[0115] For example, the triggering condition for event A12 can be expressed as the following equation (12): MeasValue–PredValue>logged-A12-Threshold (12) Where MeasValue represents the parameter measurement result of the measured object, PredValue represents the parameter prediction result of the measured object, and logged-A12-Threshold represents the twelfth threshold. Event A12 is triggered if and only if the duration of the parameter measurement result and parameter prediction result of the measured object satisfying the above formula (12) exceeds TTT.

[0116] It should be understood that equations (7) to (12) above are merely examples, and other forms are also possible. For example, equations (11) and (12) can also be written as: PredValue – MeasValue<logged-A11-Threshold;PredValue–MeasValue> logged-A12-Threshold. It should also be understood that events A7 and A8 are examples of event type 1 above, events A9 and A10 are examples of event type 2 above, and events A11 and A12 are examples of event type 3 above.

[0117] Preferably, the parameters in events A7 to A12 above can be the moving speed of the terminal device or the area where the terminal device is located. Of course, other parameters are also possible, and the embodiments of this application are not limited to these.

[0118] In some embodiments, the triggering condition "duration exceeds TTT" in events A1 to A12 above is optional. Accordingly, the parameter "loggedtimeToTrigger" may not be included in the configuration of events A1 to A12.

[0119] It should be understood that events A1 to A12 above are merely examples, and other variations are also possible.

[0120] Below is a more specific example of a data collection task trigger configuration. Suppose that network device 302, through data analysis of AI / ML model training, discovers a lack of data samples for the following specific scenario: the signal quality of the measured target object (ID=11) is greater than threshold threshold1, and the signal quality of the measured target object (ID=12) is less than threshold threshold2. To collect this data sample, network device 302 can send a first configuration to terminal device 301. In this first configuration, the configuration list of events and the configuration list of data collection tasks are as follows: loggedEventConfigToAddModList { loggedEventConfigId 1, loggedEventConfig { LoggedEventA1 { logged-A1-Threshold threshold1, loggedHysteresis 10 loggedtimeToTrigger 40ms measObjectId 11 ... }} loggedEventConfigId 2, loggedEventConfig { LoggedEventA2 { logged-A2-Threshold threshold2, loggedHysteresis 10 loggedtimeToTrigger 40ms measObjectId 12 ... } } ... } loggedTaskConfigToAddModList { loggedTaskConfigId 3 loggedBufferSize 128M loggedTaskExecutionConfig { 1, 2 } loggedQuantity rsrq ... } As can be seen from the above configuration, the data size configured for this task is 128M, and two events with event IDs 1 and 2 have been added to the loggedTaskExecutionConfig. This indicates that when events 1 and 2 are triggered simultaneously, the data collection task will be triggered, and corresponding data will be recorded according to the task configuration. It should be understood that this example is for illustrative purposes only and is not intended to limit the embodiments of this application.

[0121] Continue to refer to Figure 3 In step 340, after receiving the first configuration, the terminal device 301 may send signaling for configuration feedback to the network device 302 to confirm the receipt of the first configuration. It should be noted that step 340 is optional.

[0122] In step 350, terminal device 301 may generate one or more data logs based on the first configuration. These one or more data logs correspond to at least one of the one or more data collection tasks.

[0123] In some embodiments, terminal device 301 may perform measurements based on the configuration of one or more events associated with a data collection task in the first configuration (e.g., loggedTaskExecutionConfig→LoggedEventConfigId). Terminal device 301 triggers a data collection task and records the collected data into allocated storage space (e.g., a buffer defined by loggedBufferSize) only when all events associated with a data collection task (e.g., any combination of one or more of events A1 to A6) are satisfied, thereby generating a data log. In some embodiments, the generation of the data log may also be based on periodic triggering or other specific rules. In some embodiments, a data log may contain data recorded by one or more triggers of all events associated with the data collection task.

[0124] In some embodiments, a data log may include measurement results of one or more of the parameters described above. In some embodiments, the measurement results may include actual measured signal quality data. For example, terminal device 301 may record physical layer metrics, such as RSRP, RSRQ, or SINR, measured for the object at the trigger time. It should be understood that the measurement results may also include any other suitable measurement data.

[0125] In some embodiments, a data log may include prediction results of one or more of the parameters described above. In some embodiments, the prediction results may include predicted signal quality data. For example, terminal device 301 uses its deployed AI / ML prediction model to predict future signal states based on historical measurement data and records the predicted values ​​output by the model in a log. It should be understood that the prediction results can be in any suitable form.

[0126] In some embodiments, a data log may include the measurement time of a measurement result. In some embodiments, the measurement time may record the specific moment when data was sampled or an event was triggered. It should be understood that the time recording format may be absolute time, relative time, or any other suitable format.

[0127] In some embodiments, a data log may include a cell identifier, such as a PCI or cell ID. It should be understood that a data log may include any combination of the above information.

[0128] Continue to refer to Figure 3 In step 360, terminal device 301 may send a first message to network device 302, the first message indicating the generation of one or more data logs. In some embodiments, terminal device 301 may send the first message when a data collection task is triggered and the configured buffer is full or about to be full of collected data. It should be noted that the sending of the first message may also be triggered based on other conditions, and this application embodiment does not limit this.

[0129] In some embodiments, the first message may include a configuration identifier (e.g., loggedTaskConfigId) of at least one data collection task associated with the one or more data logs. By carrying this configuration identifier in the first message, the terminal device 301 can inform the network device 302 which data collection task(s) has completed data collection, or request the data logs of that data collection task to be reported. It should be noted that the first message may also include other suitable information to indicate the completion of data collection for a data collection task, or to request the reporting of data logs.

[0130] In step 370, after receiving the first message, network device 302 may send a second message to terminal device 301, the second message indicating whether to send the one or more data logs. Accordingly, terminal device 301 may receive the second message.

[0131] In some embodiments, the second message may indicate that the one or more data logs need to be sent, that is, that the one or more data logs need to be reported. In this case, as shown in step 380, the terminal device 301 may send the one or more data logs.

[0132] In some embodiments, the second message may indicate that the one or more data logs do not need to be sent, i.e., the one or more data logs do not need to be reported. In this case, the terminal device 301 may not send the one or more data logs. In some embodiments, if the second message indicates that the one or more data logs do not need to be sent, for example, if the second message includes an instruction to delete data logs, then the terminal device 301 may delete the one or more data logs. In this way, storage space for data collection used in the RRM measurement prediction model can be effectively managed.

[0133] Accordingly, network device 302 can receive the one or more data logs. In some embodiments, based on the one or more data logs, network device 302 can perform training and updating functions for the RRM measurement prediction model. This enables model iteration and improves the performance of the RRM prediction model.

[0134] This concludes the description of a data collection scheme for the RRM measurement and prediction model. According to the scheme in this application, by adding specific data collection tasks and configuration parameters for triggering these tasks to the RRM configuration, the network can collect specific sample data from the terminal-side RRM measurement and prediction model. Furthermore, the terminal can report data logs based on instructions sent by the network, and the network-side model training function can update the model based on the received data, thereby achieving model iteration and improving the performance of the RRM prediction model.

[0135] Corresponding to the communication process 300 described above, embodiments of this application also provide a communication method that can be implemented at terminal devices and network devices. Figure 4 A flowchart of a communication method 400 implemented at a terminal device according to an embodiment of this application is shown. It should be understood that method 400 may include other additional steps not shown, or some steps shown may be omitted. The scope of this application is not limited thereto.

[0136] In step 410, the terminal device (e.g., terminal device 301) receives a first configuration that indicates one or more data collection tasks for training an RRM measurement prediction model and that the one or more data collection tasks are associated with one or more events.

[0137] In step 420, the terminal device generates one or more data logs based on the first configuration, the one or more data logs corresponding to at least one of the one or more data collection tasks.

[0138] In step 430, the terminal device sends a first message, which indicates the generation of the one or more data logs.

[0139] In some embodiments, the first configuration may include: a configuration list of the one or more data collection tasks; and a configuration list of the one or more events.

[0140] In some embodiments, the configuration of one of the one or more data collection tasks may include at least one of the following: a configuration identifier for the data collection task; a cache size for the data log corresponding to the data collection task; a configuration identifier for one or more events corresponding to the data collection task; or a type of data log corresponding to the data collection task.

[0141] In some embodiments, the data collection task may be triggered when one or more events corresponding to the data collection task are triggered.

[0142] In some embodiments, the configuration of one of the one or more events may include at least one of the following: a configuration identifier for the event; or a set of parameters associated with the triggering of the event.

[0143] In some embodiments, the parameter set may include at least one of the following: a threshold; a trigger time; a hysteresis value; or one or more measurement object identifiers.

[0144] In some embodiments, one of the one or more events may be associated with at least one of the following: one or more measurement objects; measurement results of one or more parameters; or prediction results of one or more parameters.

[0145] In some embodiments, the event is triggered when a first condition is met or when the duration of the first condition being met exceeds the trigger time. The first condition includes one of the following: the difference between the measurement result and the hysteresis value of one or more parameters of a measurement object is greater than a first threshold; the sum of the measurement result and the hysteresis value of one or more parameters of a measurement object is less than a second threshold; the sum of the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is less than a third threshold; the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is greater than a fourth threshold; the sum of the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is less than a fifth threshold; the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is greater than a sixth threshold; the measurement result of one or more parameters of a measurement object is greater than a seventh threshold; the measurement result of one or more parameters of a measurement object is less than an eighth threshold; the difference between the measurement result of one or more parameters of two measurement objects is less than a ninth threshold; the difference between the measurement result of one or more parameters of two measurement objects is greater than a tenth threshold; the difference between the measurement result and the prediction result of one or more parameters of a measurement object is less than an eleventh threshold; or the difference between the measurement result and the prediction result of one or more parameters of a measurement object is greater than a twelfth threshold.

[0146] In some embodiments, the one or more parameters may indicate at least one of the following: the signal quality of the terminal device; the moving speed of the terminal device; or the area where the terminal device is located.

[0147] In some embodiments, one of the data logs may include at least one of the following: measurement results of the one or more parameters; prediction results of the one or more parameters; measurement time of the measurement results; or cell identifier.

[0148] In some embodiments, the first message includes a configuration identifier for the at least one data collection task.

[0149] In some embodiments, the terminal device may receive a second message indicating whether to send the one or more data logs; and send the one or more data logs based on the second message indicating that the one or more data logs should be sent.

[0150] Figure 5 A flowchart of a communication method 500 implemented at a network device according to an embodiment of this application is shown. It should be understood that method 500 may include other additional steps not shown, or some steps shown may be omitted. The scope of this application is not limited thereto.

[0151] In step 510, a network device (e.g., network device 302) sends a first configuration that indicates one or more data collection tasks for training an RRM measurement prediction model and that the one or more data collection tasks are associated with one or more events.

[0152] In step 520, the network device receives a first message indicating the generation of one or more data logs, the one or more data logs corresponding to at least one of the one or more data collection tasks.

[0153] In some embodiments, the first configuration may include: a configuration list of the one or more data collection tasks; and a configuration list of the one or more events.

[0154] In some embodiments, the configuration of one of the one or more data collection tasks may include at least one of the following: a configuration identifier for the data collection task; a cache size for the data log corresponding to the data collection task; a configuration identifier for one or more events corresponding to the data collection task; or a type of data log corresponding to the data collection task.

[0155] In some embodiments, the data collection task may be triggered when one or more events corresponding to the data collection task are triggered.

[0156] In some embodiments, the configuration of one of the one or more events may include at least one of the following: a configuration identifier for the event; or a set of parameters associated with the triggering of the event.

[0157] In some embodiments, the parameter set may include at least one of the following: a threshold; a trigger time; a hysteresis value; or one or more measurement object identifiers.

[0158] In some embodiments, one of the one or more events may be associated with at least one of the following: one or more measurement objects; measurement results of one or more parameters; or prediction results of one or more parameters.

[0159] In some embodiments, the event is triggered when a first condition is met or when the duration of the first condition being met exceeds the trigger time. The first condition includes one of the following: the difference between the measurement result and the hysteresis value of one or more parameters of a measurement object is greater than a first threshold; the sum of the measurement result and the hysteresis value of one or more parameters of a measurement object is less than a second threshold; the sum of the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is less than a third threshold; the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is greater than a fourth threshold; the sum of the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is less than a fifth threshold; the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is greater than a sixth threshold; the measurement result of one or more parameters of a measurement object is greater than a seventh threshold; the measurement result of one or more parameters of a measurement object is less than an eighth threshold; the difference between the measurement result of one or more parameters of two measurement objects is less than a ninth threshold; the difference between the measurement result of one or more parameters of two measurement objects is greater than a tenth threshold; the difference between the measurement result and the prediction result of one or more parameters of a measurement object is less than an eleventh threshold; or the difference between the measurement result and the prediction result of one or more parameters of a measurement object is greater than a twelfth threshold.

[0160] In some embodiments, the one or more parameters may indicate at least one of the following: the signal quality of the terminal device; the moving speed of the terminal device; or the area where the terminal device is located.

[0161] In some embodiments, one of the data logs may include at least one of the following: measurement results of the one or more parameters; prediction results of the one or more parameters; measurement time of the measurement results; or cell identifier.

[0162] In some embodiments, the first message may include a configuration identifier for the at least one data collection task.

[0163] In some embodiments, the terminal device may send a second message indicating whether to send the one or more data logs; and receive the one or more data logs based on the second message indicating that the one or more data logs should be sent.

[0164] It should be understood that the description of communication process 300 also applies to the above-mentioned communication methods 400 and 500, therefore other details will not be repeated.

[0165] Corresponding to the above communication method, embodiments of this application also provide a communication device, which is described below in conjunction with... Figure 6 and Figure 7 This will be described.

[0166] Figure 6 A schematic block diagram of an example communication device 600 according to an embodiment of this application is shown. The communication device 600 may be implemented at a terminal device (e.g., terminal device 301). The communication device 600 may be part of the terminal device or the terminal device itself. It should be understood that the communication device 600 may include more additional components than those shown or may omit some of the components shown, and this embodiment of the application does not limit this.

[0167] like Figure 6 As shown, the communication device 600 may include a receiving unit 610, a processing unit 620, and a transmitting unit 630. The receiving unit 610 may be configured to receive a first configuration indicating one or more data collection tasks for training an RRM measurement prediction model, and said one or more data collection tasks being associated with one or more events. The processing unit 620 may be configured to generate one or more data logs based on the first configuration, said one or more data logs corresponding to at least one of the one or more data collection tasks. The transmitting unit 630 may be configured to send a first message indicating the generation of the one or more data logs.

[0168] In some embodiments, the first configuration may include: a configuration list of the one or more data collection tasks; and a configuration list of the one or more events.

[0169] In some embodiments, the configuration of one of the one or more data collection tasks may include at least one of the following: a configuration identifier for the data collection task; a cache size for the data log corresponding to the data collection task; a configuration identifier for one or more events corresponding to the data collection task; or a type of data log corresponding to the data collection task.

[0170] In some embodiments, the data collection task may be triggered when one or more events corresponding to the data collection task are triggered.

[0171] In some embodiments, the configuration of one of the one or more events may include at least one of the following: a configuration identifier for the event; or a set of parameters associated with the triggering of the event.

[0172] In some embodiments, the parameter set may include at least one of the following: a threshold; a trigger time; a hysteresis value; or one or more measurement object identifiers.

[0173] In some embodiments, one of the one or more events may be associated with at least one of the following: one or more measurement objects; measurement results of one or more parameters; or prediction results of one or more parameters.

[0174] In some embodiments, the event is triggered when a first condition is met or when the duration of the first condition being met exceeds the trigger time. The first condition includes one of the following: the difference between the measurement result and the hysteresis value of one or more parameters of a measurement object is greater than a first threshold; the sum of the measurement result and the hysteresis value of one or more parameters of a measurement object is less than a second threshold; the sum of the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is less than a third threshold; the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is greater than a fourth threshold; the sum of the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is less than a fifth threshold; the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is greater than a sixth threshold; the measurement result of one or more parameters of a measurement object is greater than a seventh threshold; the measurement result of one or more parameters of a measurement object is less than an eighth threshold; the difference between the measurement result of one or more parameters of two measurement objects is less than a ninth threshold; the difference between the measurement result of one or more parameters of two measurement objects is greater than a tenth threshold; the difference between the measurement result and the prediction result of one or more parameters of a measurement object is less than an eleventh threshold; or the difference between the measurement result and the prediction result of one or more parameters of a measurement object is greater than a twelfth threshold.

[0175] In some embodiments, the one or more parameters may indicate at least one of the following: the signal quality of the terminal device; the moving speed of the terminal device; or the area where the terminal device is located.

[0176] In some embodiments, one of the data logs may include at least one of the following: measurement results of the one or more parameters; prediction results of the one or more parameters; measurement time of the measurement results; or cell identifier.

[0177] In some embodiments, the first message includes a configuration identifier for the at least one data collection task.

[0178] In some embodiments, the receiving component 610 may also be configured to receive a second message indicating whether the one or more data logs should be sent. The sending component 630 may also be configured to send the one or more data logs based on the second message indicating that the one or more data logs should be sent.

[0179] Figure 7 A schematic block diagram of another example communication device 700 according to an embodiment of this application is shown. Communication device 700 may be implemented at a network device (e.g., network device 302). Communication device 700 may be part of the network device or the network device itself. It should be understood that communication device 700 may include more additional components than those shown or omit some of the components shown, and this embodiment of the application does not impose limitations in this regard.

[0180] like Figure 7 As shown, the communication device 700 may include a transmitting component 710 and a receiving component 720. The transmitting component 710 may be configured to transmit a first configuration indicating one or more data collection tasks for training an RRM measurement prediction model, and the one or more data collection tasks being associated with one or more events. The receiving component 720 may be configured to receive a first message indicating the generation of one or more data logs, the one or more data logs corresponding to at least one of the one or more data collection tasks.

[0181] In some embodiments, the first configuration may include: a configuration list of the one or more data collection tasks; and a configuration list of the one or more events.

[0182] In some embodiments, the configuration of one of the one or more data collection tasks may include at least one of the following: a configuration identifier for the data collection task; a cache size for the data log corresponding to the data collection task; a configuration identifier for one or more events corresponding to the data collection task; or a type of data log corresponding to the data collection task.

[0183] In some embodiments, the data collection task may be triggered when one or more events corresponding to the data collection task are triggered.

[0184] In some embodiments, the configuration of one of the one or more events may include at least one of the following: a configuration identifier for the event; or a set of parameters associated with the triggering of the event.

[0185] In some embodiments, the parameter set may include at least one of the following: a threshold; a trigger time; a hysteresis value; or one or more measurement object identifiers.

[0186] In some embodiments, one of the one or more events may be associated with at least one of the following: one or more measurement objects; measurement results of one or more parameters; or prediction results of one or more parameters.

[0187] In some embodiments, the event is triggered when a first condition is met or when the duration of the first condition being met exceeds the trigger time. The first condition includes one of the following: the difference between the measurement result and the hysteresis value of one or more parameters of a measurement object is greater than a first threshold; the sum of the measurement result and the hysteresis value of one or more parameters of a measurement object is less than a second threshold; the sum of the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is less than a third threshold; the difference between the measurement result and the hysteresis value of one or more parameters of two measurement objects is greater than a fourth threshold; the sum of the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is less than a fifth threshold; the difference between the measurement result and the prediction result and the hysteresis value of one or more parameters of a measurement object is greater than a sixth threshold; the measurement result of one or more parameters of a measurement object is greater than a seventh threshold; the measurement result of one or more parameters of a measurement object is less than an eighth threshold; the difference between the measurement result of one or more parameters of two measurement objects is less than a ninth threshold; the difference between the measurement result of one or more parameters of two measurement objects is greater than a tenth threshold; the difference between the measurement result and the prediction result of one or more parameters of a measurement object is less than an eleventh threshold; or the difference between the measurement result and the prediction result of one or more parameters of a measurement object is greater than a twelfth threshold.

[0188] In some embodiments, the one or more parameters may indicate at least one of the following: the signal quality of the terminal device; the moving speed of the terminal device; or the area where the terminal device is located.

[0189] In some embodiments, one of the data logs may include at least one of the following: measurement results of the one or more parameters; prediction results of the one or more parameters; measurement time of the measurement results; or cell identifier.

[0190] In some embodiments, the first message may include a configuration identifier for the at least one data collection task.

[0191] In some embodiments, the sending component 710 may also be configured to send a second message indicating whether the one or more data logs should be sent.

[0192] In some embodiments, the receiving component 720 may also be configured to receive the one or more data logs based on the second message indicating that the one or more data logs are to be sent.

[0193] It should be understood that the communication devices 600 and 700 mentioned above correspond to the communication methods 400 and 500 mentioned above, respectively, and correspond to the description in the communication process 300 mentioned above. Therefore, other details will not be repeated.

[0194] Figure 8 This is a simplified block diagram of a device 800 suitable for implementing embodiments of this application. The device 800 can be provided to implement a terminal device 301 or a network device 302, or a communication device (e.g., a chip) that can support the functionality of the terminal device 301 or the network device 302. As shown, the device 800 includes one or more processors 810 and one or more memories 820 coupled to the processors 810. Optionally, the one or more memories 820 may also be integrated with the one or more processors 810.

[0195] Processor 810 can be of any type suitable for a local technology network, and by way of limiting examples, can include one or more of the following: general-purpose computer, special-purpose computer, microprocessor, digital signal processor, and processor based on a multi-core processor architecture. Device 800 can have multiple processors, such as application-specific integrated circuit chips, which are time-subordinate to a clock synchronized with the main processor.

[0196] Memory 820 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 824, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), and other magnetic and / or optical storage devices. Examples of volatile memories include, but are not limited to, random access memory (RAM) 822 and other volatile memories that do not persist during power-off periods.

[0197] Computer program 830 includes computer-executable instructions that are executed by the associated processor 810. Program 830 may be stored in ROM 820. Processor 810 can perform any suitable actions and processes by loading program 830 into RAM 820.

[0198] The embodiments of this application can be implemented by means of program 830, such that device 800 performs as described in the reference. Figures 1 to 5 The embodiments of this application describe a solution. Device 800 can correspond to the aforementioned communication device 600 or 700, and the functional modules in communication device 600 or 700 can be implemented using software within device 800. In other words, the functional modules included in communication device 600 or 700 can be generated by the processor 810 of device 800 reading program code stored in memory 820. Embodiments of this application can also be implemented using hardware or a combination of software and hardware.

[0199] In some embodiments, program 830 may be tangibly contained in a computer-readable medium, which may include in device 800 (such as in memory 820) or other storage device accessible by device 800. Program 830 may be loaded from the computer-readable medium into RAM 822 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc.

[0200] In some embodiments, device 800 may further include one or more communication modules (not shown). These communication modules may be coupled to processor 810. The communication modules may be used for bidirectional communication. The communication modules may have a communication interface to facilitate communication. The communication interface may represent any interface required for communication with other network elements.

[0201] As used in this document, the term "circuit" refers to one or more of the following: • Hardware circuit implementation only, such as implementation of analog and / or digital circuits only; and • Combinations of hardware circuits and software, such as: 1) combinations of analog and / or digital hardware circuits with software / firmware, 2) any part of a hardware processor with software, including digital signal processors, software and memory (these components work together to enable devices such as terminal devices or network devices to perform various functions), and 3) hardware circuits and / or processors that require software / firmware to operate.

[0202] The term “circuit” as used herein also covers an implementation of hardware circuitry or a processor, or a portion of a hardware circuitry or processor and its accompanying software / firmware.

[0203] Generally, the various exemplary embodiments of this application can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of the embodiments of this application are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof. Examples of hardware devices that can be used to implement the embodiments of this application include, but are not limited to: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard parts (ASSPs), SoCs, complex programmable logic devices (CPLDs), etc.

[0204] As an example, embodiments of this application can be described in the context of machine-executable instructions, such as program modules that execute on a device running on a real or virtual processor of the target. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., which perform specific tasks or implement specific abstract data structures. In various embodiments, the functionality of program modules may be combined or divided among the described program modules. The machine-executable instructions for a program module can execute within a local or distributed device. In a distributed device, the program module can reside on both local and remote storage media.

[0205] The computer program code used to implement the methods of this application may be written in one or more programming languages. This computer program code may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0206] In the context of this application, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagated signals, such as carrier waves, infrared signals, etc. A machine-readable medium may be any tangible medium that contains or stores a program for or relating to an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More detailed examples of machine-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM, or flash memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0207] Furthermore, although the operations are depicted in a specific order, this should not be construed as requiring such operations to be performed in the specific order shown or in a sequential order, or to execute all illustrated operations to obtain the desired result. In some cases, multitasking or parallel processing may be beneficial. Similarly, although the foregoing discussion includes certain specific implementation details, this should not be construed as limiting the scope of any invention or claim, but rather as a description of specific embodiments that may be implemented with respect to a particular invention. Certain features described in this specification in the context of separate embodiments may also be implemented in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination.

[0208] Although the subject matter has been described in language specific to structural features and / or methodological actions, it should be understood that the subject matter defined in the appended claims is not limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms of implementing the claims.

Claims

1. A communication method, comprising: Receive a first configuration, the first configuration indicating one or more data collection tasks for training a radio resource management (RRM) measurement prediction model, and the one or more data collection tasks being associated with one or more events; Based on the first configuration, one or more data logs are generated, and the one or more data logs correspond to at least one of the one or more data collection tasks. as well as Send a first message, which instructs the generation of the one or more data logs.

2. The method of claim 1, wherein the first configuration comprises: A configuration list for the one or more data collection tasks; as well as A configuration list of one or more events.

3. The method of claim 2, wherein the configuration of one of the one or more data collection tasks includes at least one of the following: The configuration identifier for the data collection task; The cache size of the data logs corresponding to the data collection task; Configuration identifiers for one or more events corresponding to the data collection task; or The type of data log corresponding to the data collection task.

4. The method of claim 3, wherein the data collection task is triggered when all one or more events corresponding to the data collection task are triggered.

5. The method of claim 2, wherein the configuration of one of the one or more events includes at least one of the following: The configuration identifier of the event; or The set of parameters associated with the triggering of the event.

6. The method of claim 5, wherein the parameter set includes at least one of the following: Threshold; Trigger time; Hysteresis value; or One or more measurement object identifiers.

7. The method of claim 1, wherein one of the one or more events is associated with at least one of the following: One or more measurement objects; The measurement results of one or more parameters; or The prediction result of one or more parameters.

8. The method of claim 7, wherein the event is triggered when a first condition is met or when the duration of the first condition being met exceeds a trigger time, the first condition comprising one of the following: The difference between the measurement result of one or more parameters of a measurement object and the hysteresis value is greater than a first threshold. The sum of the measurement results of one or more parameters of a measurement object and the hysteresis value is less than a second threshold; The sum of the difference between the measurement results of one or more parameters of the two measured objects and the hysteresis value is less than a third threshold; The difference between the measurement results of one or more parameters of the two measured objects and the difference between the hysteresis value is greater than the fourth threshold; The sum of the difference between the measured result and the predicted result of one or more parameters of a measured object and the hysteresis value is less than a fifth threshold; The difference between the measured result and the predicted result of one or more parameters of a measurement object and the difference between the hysteresis value is greater than the sixth threshold; The measurement results of one or more parameters of a measurement object are all greater than the seventh threshold; The measurement results of one or more parameters of a measurement object are all less than the eighth threshold; The difference between the measurement results of one or more parameters of the two measurement objects is less than the ninth threshold; The difference between the measurement results of one or more parameters of the two measured objects is greater than the tenth threshold; The difference between the measured result and the predicted result of one or more parameters of a measurement object is less than the eleventh threshold; or The difference between the measured result and the predicted result of one or more parameters of a measurement object is greater than the twelfth threshold.

9. The method of claim 7, wherein the one or more parameters indicate at least one of the following: Signal quality of terminal equipment; The moving speed of the terminal device; or The area where the terminal device is located.

10. The method of claim 7, wherein one of the one or more data logs comprises at least one of the following: The measurement results of one or more parameters; The prediction results of the one or more parameters; The measurement time of the measurement result; or Community signage.

11. The method of claim 1, wherein the first message includes a configuration identifier of the at least one data collection task.

12. The method according to claim 1, further comprising: Receive a second message, which indicates whether to send the one or more data logs; as well as Based on the second message indicating that the one or more data logs should be sent, the one or more data logs are sent.

13. A communication method, comprising: Send a first configuration, which instructs one or more data collection tasks for training a radio resource management (RRM) measurement prediction model, and the one or more data collection tasks are associated with one or more events; as well as A first message is received, which indicates the generation of one or more data logs, the one or more data logs corresponding to at least one of the one or more data collection tasks.

14. The method of claim 13, wherein the first configuration comprises: A configuration list for the one or more data collection tasks; as well as A configuration list of one or more events.

15. The method of claim 14, wherein the configuration of one of the one or more data collection tasks includes at least one of the following: The configuration identifier for the data collection task; The cache size of the data logs corresponding to the data collection task; Configuration identifiers for one or more events corresponding to the data collection task; or The type of data log corresponding to the data collection task.

16. The method of claim 15, wherein the data collection task is triggered when all one or more events corresponding to the data collection task are triggered.

17. The method of claim 14, wherein the configuration of one of the one or more events comprises at least one of the following: The configuration identifier of the event; or The set of parameters associated with the triggering of the event.

18. The method of claim 17, wherein the parameter set comprises at least one of the following: Threshold; Trigger time; Hysteresis value; or One or more measurement object identifiers.

19. The method of claim 13, wherein one of the one or more events is associated with at least one of the following: One or more measurement objects; The measurement results of one or more parameters; or The prediction result of one or more parameters.

20. The method of claim 19, wherein the event is triggered when a first condition is met or when the duration of the first condition being met exceeds a trigger time, the first condition comprising one of the following: The difference between the measurement result of one or more parameters of a measurement object and the hysteresis value is greater than a first threshold. The sum of the measurement results of one or more parameters of a measurement object and the hysteresis value is less than a second threshold; The sum of the difference between the measurement results of one or more parameters of the two measured objects and the hysteresis value is less than a third threshold; The difference between the measurement results of one or more parameters of the two measured objects and the difference between the hysteresis value is greater than the fourth threshold; The sum of the difference between the measured result and the predicted result of one or more parameters of a measured object and the hysteresis value is less than a fifth threshold; The difference between the measured result and the predicted result of one or more parameters of a measurement object and the difference between the hysteresis value is greater than the sixth threshold; The measurement results of one or more parameters of a measurement object are all greater than the seventh threshold; The measurement results of one or more parameters of a measurement object are all less than the eighth threshold; The difference between the measurement results of one or more parameters of the two measurement objects is less than the ninth threshold; The difference between the measurement results of one or more parameters of the two measured objects is greater than the tenth threshold; The difference between the measured result and the predicted result of one or more parameters of a measurement object is less than the eleventh threshold; or The difference between the measured result and the predicted result of one or more parameters of a measurement object is greater than the twelfth threshold.

21. The method of claim 19, wherein the one or more parameters indicate at least one of the following: Signal quality of terminal equipment; The moving speed of the terminal device; or The area where the terminal device is located.

22. The method of claim 19, wherein one of the one or more data logs comprises at least one of the following: The measurement results of one or more parameters; The prediction results of the one or more parameters; The measurement time of the measurement result; or Community signage.

23. The method of claim 13, wherein the first message includes a configuration identifier of the at least one data collection task.

24. The method of claim 13, further comprising: Send a second message, which indicates whether to send the one or more data logs; as well as Based on the second message indicating that the one or more data logs should be sent, the one or more data logs are received.

25. A communication device, comprising: processor; as well as Memory, including computer program code, When the computer program code is run by the processor, it causes the method according to any one of claims 1 to 12 or any one of claims 13 to 24 to be performed.

26. A communication device comprising components for performing the method according to any one of claims 1 to 12 or any one of claims 13 to 24.

27. A chip comprising a processor connected to a memory located inside or outside the chip, the memory for storing a computer program, the processor for calling and running the computer program from the memory to cause the method according to any one of claims 1 to 12 or any one of claims 13 to 24 to be performed.

28. A computer-readable storage medium comprising machine-executable instructions, which, when executed by a device, cause the method according to any one of claims 1 to 12 or any one of claims 13 to 24 to be performed.

29. A computer program product comprising a computer program that, when run on a device, causes the method according to any one of claims 1 to 12 or any one of claims 13 to 24 to be performed.

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