Method, device and equipment for measuring control based on user behavior portrait and medium

Abstract

The application provides a measurement control method and device based on user behavior portrait, equipment and medium, the method comprises: obtaining the space-time behavior portrait of a user or a user group and the corresponding PCC strategy, the space-time behavior portrait is constructed based on the historical cell residence sequence, location trajectory and service data of the user or the user group, and the PCC strategy is generated based on the space-time behavior features of the space-time behavior portrait; a prediction measurement template is generated based on the space-time behavior portrait and the PCC strategy, the prediction measurement template is configured with a reduced measurement strategy adapted to the space-time behavior portrait; the prediction measurement template is issued to the terminal, so that the terminal performs directional reduced wireless access measurement based on the reduced measurement strategy. The application reduces the measurement signaling overhead, reduces the terminal power consumption and network load, and realizes the wireless measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence and wireless communication technology, and in particular to a measurement and control method, device, electronic device and storage medium based on user behavior profiles. Background Technology

[0002] To ensure coverage and handover quality, current cellular networks often require UEs to perform periodic, wide-area neighbor cell and frequency measurements. This results in high measurement overhead, increased RRC signaling, and higher terminal power consumption.

[0003] In related technologies, static or semi-static measurement configuration methods are usually adopted to reduce measurement and signaling overhead. However, this method cannot be dynamically adjusted according to the user's actual mobility mode and business needs, which can easily lead to poor user experience, waste of measurement resources and invalid signaling transmission. It is also difficult to achieve reliable and efficient measurement burden reduction in complex and ever-changing real-world scenarios.

[0004] Therefore, how to reduce measurement and signaling overhead and improve resource utilization while ensuring user experience and reliable handover is a problem that needs to be solved in network optimization. Summary of the Invention

[0005] This application provides a measurement and control method, apparatus, electronic device, and storage medium based on user behavior profiles, to at least solve the problems in related technologies where the inability to dynamically adjust according to actual user movement patterns and business needs leads to poor user experience, wasted measurement resources, and invalid signaling transmission. The technical solution of this application is as follows: According to a first aspect of the embodiments of this application, a measurement and control method based on user behavior profiles is provided, the method comprising: The system acquires spatiotemporal behavioral profiles of users or user groups and corresponding policies and billing control (PCC) policies. The spatiotemporal behavioral profiles are constructed based on the historical cell dwell sequences, location trajectories, and service data of users or user groups. The PCC policies are generated based on the spatiotemporal behavioral features of the spatiotemporal behavioral profiles. Based on the spatiotemporal behavior profile and the corresponding PCC strategy, a prediction measurement template is generated. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile. The predictive measurement template configured with the reduction measurement strategy is sent to the terminal so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

[0006] Optionally, the step of generating a predictive measurement template based on the spatiotemporal behavioral profile and the corresponding PCC strategy, wherein the predictive measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavioral profile, including: The spatiotemporal behavior profile and the corresponding PCC strategy are analyzed to obtain the prediction path, spatiotemporal behavior features and profile confidence. Based on the switching probability of each resource in the predicted path, a set of priority measurement objects and a set of skippable measurement objects are divided. Based on the spatiotemporal behavioral characteristics, the measurement gap parameters are configured, and the upper limit of the measurement resource budget is set according to the confidence level of the profile. By integrating the priority measurement object set, the skippable measurement object set, the measurement gap parameter, and the measurement resource budget limit, a reduction measurement strategy adapted to the spatiotemporal behavior profile is generated. The reduction measurement strategy is encapsulated to obtain the prediction measurement template of the spatiotemporal behavior profile.

[0007] Optionally, the method further includes: The measured behavior data obtained by the terminal after reducing the measurement according to the predicted measurement template is received, and the behavior deviation is compared with the predicted behavior data of the spatiotemporal behavior profile. When the value of the behavior deviation exceeds the preset rollback threshold, a baseline full measurement configuration is sent to the terminal to activate the fast rollback mechanism.

[0008] The system receives the behavior deviation reported by the terminal, whereby the behavior deviation is the deviation value between the measured behavior data obtained by the terminal performing a reduction measurement based on the prediction measurement template and the predicted behavior data of the spatiotemporal behavior profile. Obtain the preset fallback threshold configured based on profile confidence in the PCC strategy; When the deviation of the behavior exceeds the preset backoff threshold, the baseline full measurement configuration is sent to the terminal to trigger the fast backoff mechanism so that the terminal terminates the reduced measurement and switches to the baseline full measurement.

[0009] Optionally, the method further includes: Receive measurement acceptance and budget consumption data reported by the terminal; dynamically adjust the upper limit of the measurement resource budget within the predicted measurement template based on the measurement acceptance and budget consumption data; and / or The system periodically acquires various key performance indicators of the terminal after reducing the measurement according to the predicted measurement template; and transmits these key performance indicators back to the core network processing node so that the core network processing node can iteratively update the spatiotemporal behavior profile and the corresponding PCC strategy.

[0010] Optionally, the spatiotemporal behavioral profile is pre-constructed using the Network Data Analysis Function (NWDAF) based on the user's or user group's historical cell dwell sequence, location trajectory, and service data in the following manner: De-identification preprocessing is performed on the historical cell dwell sequence, location trajectory and business data of the acquired user or user group; Based on the preprocessing results, group prototype profiles are constructed by clustering users according to the similarity of their behaviors. Based on the group prototype profile, individual increments are learned, and the basic features of the group prototype profile are superimposed and fused with the individual increment features to obtain a personalized spatiotemporal behavioral profile. A standardized spatiotemporal behavioral profile is obtained by uniformly encapsulating a personalized behavioral profile carrying a profile identifier, confidence level, spatiotemporal effective domain, prediction path, and profile version information; and the profile version information and spatiotemporal effective domain are sent to a unified data repository (UDR) to realize the storage, management, and backtracking of version profiles. Configure robust profile switching strategies for user cold start scenarios, including: prioritizing the reuse of group profiles in user cold start scenarios, or automatically downgrading to use group profiles or switching to baseline measurement mode when profile confidence is insufficient or users exceed the spatiotemporal validity domain of profiles.

[0011] Optionally, the PCC strategy is pre-generated through the policy control function PCF according to the spatiotemporal behavioral features of the spatiotemporal behavioral profile in the following manner: The received spatiotemporal behavior profile is parsed to obtain profile confidence, spatiotemporal effective domain, predicted path, and spatiotemporal behavior features; The constraint level of the measurement strategy is determined based on the confidence level of the profile; Based on the predicted path and constraint level, target measurement objects and skippable objects are divided, and the spatiotemporal effective domain is set as the effective condition for the measurement strategy. Based on the spatiotemporal behavioral characteristics, the measurement gap parameters are configured, and the upper limit of the measurement resource budget is set in combination with the profile confidence. Configure the boundary conditions and fallback thresholds for the policy to take effect; The target measurement object, skippable object, measurement gap parameter, measurement resource budget limit, backoff threshold, and spatiotemporal effective domain are encapsulated to generate the corresponding PCC strategy.

[0012] Optionally, the user or user group is a low-altitude flight device equipped with a cellular communication module; The spatiotemporal behavioral profile is used to depict the flight path, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of low-altitude flight equipment. The PCC strategy and predictive measurement template are adapted to authorized flight times, permitted altitude zones, restricted flight areas, and security levels of airport airspace, energy parks, ports, and urban low-altitude routes, so as to achieve differentiated and targeted reduction of wireless measurement control in low-altitude scenarios.

[0013] According to a second aspect of the embodiments of this application, a measurement and control method based on user behavior profiles is provided, the method comprising: Receive a prediction measurement template sent by the network side, the prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group; Perform directional wireless access reduction measurement according to the reduction measurement strategy; Determine the degree of behavioral deviation between the measured behavioral data and the predicted behavioral data from the spatiotemporal behavioral profile; When the behavior deviation meets the reporting conditions, it is reported to the network side so that the network side can determine whether to trigger the fast rollback mechanism of the full baseline measurement.

[0014] Optionally, performing directional wireless access reduction measurement according to the reduction measurement strategy includes: The reduction measurement strategy in the predicted measurement template is analyzed to obtain the target measurement object, the measurement object that can be skipped, the measurement gap parameter, and the upper limit of the measurement resource budget. When the current location and time period match the spatiotemporal effective domain of the spatiotemporal behavior profile, a reduction measurement is enabled. The system filters out the skippable measurement objects, performs periodic wireless measurements on the target measurement objects according to the measurement gap parameters, monitors the number of radio frequency scans, and controls energy consumption within the upper limit of the measurement resource budget.

[0015] According to a third aspect of the embodiments of this application, a measurement and control device based on user behavior profiles is provided, the device comprising: The acquisition module is used to acquire the spatiotemporal behavior profile of a user or user group and the corresponding policies and billing control (PCC) policies. The spatiotemporal behavior profile is constructed based on the user's or user group's historical cell dwell sequence, location trajectory, and service data. The PCC policy is generated based on the spatiotemporal behavior features of the spatiotemporal behavior profile. The generation module is used to generate a prediction measurement template based on the spatiotemporal behavior profile and the corresponding PCC strategy. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile. The first sending module is used to send a predictive measurement template configured with the reduction measurement strategy to the terminal, so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

[0016] Optionally, the generation module includes: The parsing module is used to parse the spatiotemporal behavior profile and the corresponding PCC strategy to obtain the prediction path, spatiotemporal behavior features and profile confidence. The partitioning module is used to partition the priority measurement object set and the skippable measurement object set based on the switching probability of each resource in the predicted path. The configuration module is used to configure measurement gap parameters based on the spatiotemporal behavioral characteristics, and to set the upper limit of the measurement resource budget according to the confidence level of the profile. The strategy generation module is used to integrate the priority measurement object set, the skippable measurement object set, the measurement gap parameter, and the measurement resource budget limit to generate a reduction measurement strategy adapted to the spatiotemporal behavior profile. The encapsulation module is used to encapsulate the reduction measurement strategy to obtain the prediction measurement template of the spatiotemporal behavior profile.

[0017] Optionally, the device further includes: The first receiving module is used to receive the behavior deviation reported by the terminal, wherein the behavior deviation is the deviation value between the measured behavior data obtained by the terminal performing reduction measurement based on the prediction measurement template and the predicted behavior data of the spatiotemporal behavior profile. The threshold acquisition module is used to acquire the preset fallback threshold configured based on the profile confidence in the PCC strategy. The judgment module is used to determine whether the value of the behavior deviation exceeds a preset backoff threshold; The second sending module is used to send a baseline full measurement configuration to the terminal when the judgment module determines that the value of the behavior deviation exceeds a preset backoff threshold, triggering a fast backoff mechanism so that the terminal terminates the reduced measurement and switches to baseline full measurement.

[0018] Optionally, the device further includes: a second receiving module and a dynamic adjustment module; and / or, an indicator acquisition module and a feedback module, wherein, The second receiving module is used to receive the measurement acceptance rate and budget consumption data reported by the terminal; The dynamic adjustment module is used to dynamically adjust the upper limit of the measurement resource budget within the predicted measurement template based on the measurement acceptance and budget consumption data. The indicator acquisition module is used to periodically acquire various key performance indicators of the terminal after the measurement is reduced according to the prediction measurement template. The backhaul module is used to backhaul the various key performance indicators to the core network processing node so that the core network processing node can iteratively update the spatiotemporal behavior profile and the corresponding PCC strategy.

[0019] Optionally, the device further includes: a pre-construction module, used to pre-construct spatiotemporal behavioral profiles of users or user groups based on the user's or user group's historical cell dwell sequence, location trajectory, and business data.

[0020] Optionally, the pre-built module includes: The preprocessing module is used to perform de-identification preprocessing on the historical cell dwell sequences, location trajectories, and business data of the acquired users or user groups; The profile building module is used to build group prototype profiles and individual incremental profiles based on the preprocessing results and user behavior similarity clustering. The learning module is used to learn the individual increment based on the group prototype profile, and to superimpose and fuse the basic features of the group prototype profile with the features of the individual increment to obtain a personalized spatiotemporal behavior profile. The encapsulation and transmission module is used to uniformly encapsulate personalized behavioral profiles carrying profile identifiers, profile types, confidence levels, spatiotemporal effective domains, prediction paths, and profile version information to obtain standardized spatiotemporal behavioral profiles; and to send the profile version information and spatiotemporal effective domain of the spatiotemporal behavioral profiles to the unified data repository UDR to realize the storage, management, and backtracking of version profiles. The configuration module is used to configure robust profile switching strategies for user cold start scenarios, including: prioritizing the reuse of group profiles in user cold start scenarios, or automatically downgrading to the use of group profiles or switching to baseline measurement mode when the profile confidence is insufficient or the user exceeds the spatiotemporal validity domain of the profile.

[0021] Optionally, the device further includes: a pre-generation module, used to pre-generate PCC strategies based on the spatiotemporal behavioral features of the spatiotemporal behavioral profile through the policy control function PCF.

[0022] Optionally, the pre-generation module includes: The parsing module is used to parse the received spatiotemporal behavior profile to obtain the profile confidence, spatiotemporal effective domain, prediction path, and spatiotemporal behavior features. The level determination module is used to determine the constraint level of the measurement strategy based on the confidence level of the profile; The object partitioning module is used to partition the target measurement object and the skippable object according to the predicted path and constraint level, and set the spatiotemporal effective domain as the measurement strategy effective condition; The parameter configuration module is used to configure measurement gap parameters based on the spatiotemporal behavioral characteristics and set the upper limit of the measurement resource budget in conjunction with the profile confidence. The threshold configuration module is used to configure the boundary conditions for the policy to take effect and the fallback threshold; The strategy encapsulation module is used to encapsulate the target measurement object, skippable object, measurement gap parameter, measurement resource budget limit, backoff threshold, and spatiotemporal effective domain to generate the corresponding PCC strategy.

[0023] Optionally, the acquisition module is further configured to acquire the spatiotemporal behavior profile of a user or user group and the corresponding strategy and billing control (PCC) strategy for low-altitude flight equipment equipped with a cellular communication module; the spatiotemporal behavior profile is used to depict the flight path trajectory, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of the low-altitude flight equipment; the PCC strategy and predictive measurement template are adapted to the authorized flight time periods, permitted altitude zones, restricted flight areas, and security levels of low-altitude routes in airport airspace, energy parks, ports, and cities, so as to achieve differentiated and targeted reduction of wireless measurement and control in low-altitude scenarios.

[0024] According to a fourth aspect of the embodiments of this application, a measurement and control device based on user behavior profiles is provided, the device comprising: The receiving module is used to receive the prediction measurement template sent by the network side. The prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group. The measurement module is used to perform directional wireless access reduction measurement according to the reduction measurement strategy; The determination module is used to determine the degree of deviation of the measured behavior data from the predicted behavior data of the spatiotemporal behavior profile; The sending module is used to report to the network side when the behavior deviation meets the reporting conditions, so that the network side can determine whether to trigger the fast backoff mechanism of the full baseline measurement.

[0025] Optionally, the measurement module includes: The parsing module is used to parse the reduction measurement strategy in the predicted measurement template to obtain the target measurement object, the measurement object that can be skipped, the measurement gap parameter and the upper limit of the measurement resource budget; The measurement module is activated to enable reduction measurement when the current location and time period match the spatiotemporal effective domain of the spatiotemporal behavior profile. The wireless measurement module is used to filter the skippable measurement objects, perform periodic wireless measurements on the target measurement objects according to the measurement gap parameters, monitor the number of radio frequency scans, and control the power consumption within the upper limit of the measurement resource budget.

[0026] According to a fifth aspect of the embodiments of this application, an electronic device is provided, comprising: It includes a processor, a memory; and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0027] According to a sixth aspect of the embodiments of this application, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor of an electronic device, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0028] According to a sixth aspect of the present application, a computer program product is provided, including a computer program or instructions, which, when executed by a processor of an electronic device, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0029] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: In this embodiment, a spatiotemporal behavioral profile of a user or user group and corresponding policies and billing control (PCC) policies are obtained. The spatiotemporal behavioral profile is constructed based on the user's or user group's historical cell dwell sequence, location trajectory, and service data. The PCC policies are generated based on the spatiotemporal behavioral features of the spatiotemporal behavioral profile. A predictive measurement template is generated based on the spatiotemporal behavioral profile and the corresponding PCC policies. The predictive measurement template is configured with a reduced measurement policy adapted to the spatiotemporal behavioral profile. The predictive measurement template configured with the reduced measurement policy is sent to the terminal so that the terminal can perform targeted reduced wireless access measurements based on the reduced measurement policy. In other words, this application constructs a user spatiotemporal behavioral profile to accurately depict the user's mobile behavior patterns, adaptively generates differentiated measurement policies based on the profile features, and solidifies them into a predictive measurement template adapted to user behavior, which is then sent to the terminal to enable the terminal to perform targeted reduced wireless measurements. While ensuring mobility and measurement reliability, this allows the terminal to effectively reduce measurement signaling overhead caused by invalid RF scanning, reduce terminal power consumption and network load, and achieve improved wireless measurement accuracy.

[0030] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0031] The accompanying drawings, incorporated in and forming part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. They do not constitute an undue limitation of this application. To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1This is a flowchart of a measurement and control method based on user behavior profiles provided in an embodiment of this application.

[0033] Figure 2 This is another flowchart of a measurement and control method based on user behavior profiles provided in the embodiments of this application.

[0034] Figure 3 This is an application diagram of a measurement and control method based on user behavior profiles provided in an embodiment of this application.

[0035] Figure 4 This is a block diagram of a measurement and control device based on user behavior profiles provided in an embodiment of this application.

[0036] Figure 5 This is another block diagram of a measurement and control device based on user behavior profiles provided in the embodiments of this application.

[0037] Figure 6 This is a block diagram of an electronic device provided in an embodiment of this application.

[0038] Figure 7 This is a block diagram of a device for measurement and control based on user behavior profiles, provided in an embodiment of this application. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0040] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0041] Figure 1 This is a flowchart of a measurement and control method based on user behavior profiles provided in an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps: Step 101: Obtain the spatiotemporal behavior profile of the user or user group and the corresponding policy and billing control (PCC) policy. The spatiotemporal behavior profile is constructed based on the user or user group's historical cell dwell sequence, location trajectory and service data. The PCC policy is generated based on the spatiotemporal behavior features of the spatiotemporal behavior profile.

[0042] Step 102: Generate a prediction measurement template based on the spatiotemporal behavior profile and the corresponding PCC strategy. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile.

[0043] Step 103: Send the predictive measurement template configured with the reduction measurement strategy to the terminal so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

[0044] The measurement and control method based on user behavior profiles described in this application can be applied to terminals, network devices, servers, etc., without limitation. The terminal implementation device can be an electronic device such as a smartphone, laptop, tablet, desktop computer, personal digital assistant (PDA), and wearable device. The network device can include network elements of the core network, network elements of the wireless access network, etc. The server can be an independent server, a server cluster, or a server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, or big data and artificial intelligence platforms, etc., without limitation.

[0045] The following is combined with Figure 1 The specific implementation steps of a measurement and control method based on user behavior profiles provided in this application embodiment will be described in detail.

[0046] In step 101, the spatiotemporal behavior profile of the user or user group and the corresponding policy and billing control (PCC) policy are obtained. The spatiotemporal behavior profile is constructed based on the user or user group's historical cell dwell sequence, location trajectory and service data. The PCC policy is generated based on the spatiotemporal behavior features of the spatiotemporal behavior profile.

[0047] In this step, the spatiotemporal behavior profiles of users or user groups and the corresponding policies and billing control (PCC) policies can be obtained from the core network side. In other words, the core network completes the construction of spatiotemporal behavior profiles of users or user groups, policy orchestration, and persistent storage of profiles offline in advance.

[0048] The spatiotemporal behavioral profile is pre-constructed by the NWDAF (Network Data Analytics Function) on the core network side based on the historical cell dwell sequence, location trajectory, and service data of the user or user group in the following manner: 11) Perform de-identification preprocessing on the historical cell dwelling sequences, location trajectories, and business data of the acquired users or user groups.

[0049] In this step, NWDAF can collect multi-source raw data from at least two network elements on the AMF, UPF, OAM, AF, and gNB sides. In this embodiment, user cell camping time sequence, terminal location trajectory, and service data are used as examples. The service data may include: service types for each time period, RSRP / SINR radio measurement statistics, mobile handover events, service throughput / latency KPIs, etc.

[0050] Of course, in this embodiment, the collected multi-source raw data may also include: mobility data (such as cell camping sequences reported by AMF, TA tracking area change records, terminal location trajectories, etc.); measurement statistics (historical neighbor cell measurements returned by gNB, cell reselection / handover statistics, etc.); service and slicing data (such as service types and network slicing requirements issued by the AF application function); network performance KPIs (such as user plane / control plane throughput, latency, and connection duration indicators forwarded by SMF, UPF, and OAM, etc.).

[0051] Next, the collected multi-source raw data is de-identified. For example, plaintext identity identifiers such as user mobile phone numbers, SUPI, and IMSI are removed, and an anonymous temporary user ID is generated using a hash algorithm. The original coordinates that can pinpoint precise users are stripped away, and only coarse-grained location information at the cell level and geographic block level is retained to meet the data minimization compliance requirements.

[0052] Next, data cleaning and formatting are performed, which involves removing missing, abnormal jumps, outliers, and invalid data; unifying the data formats of timestamps, cell numbers, and frequency indexes; aligning the time series granularity; and outputting a standardized time series sample dataset.

[0053] 12) Based on the preprocessing results, construct group prototype profiles by clustering according to user behavior similarity.

[0054] In this step, feature extraction is first performed based on the preprocessing results. This involves extracting user behavior patterns from the preprocessed time-series data, such as the set of frequently visited cells, commuting time windows, regular movement paths, active service periods, high-frequency service types, and wireless signal fluctuation patterns, to generate predicted paths or predicted movement paths (e.g., frequently visited cells, frequency points, and beam summary sets). Specifically, a time-series clustering algorithm based on similarity clustering can be used to quantify the similarity of users' spatiotemporal behavioral features and group users with highly similar behavioral patterns into the same user group. All user features within the same user group are statistically aggregated to extract common features, such as the group's general candidate Top-N cells, frequency points, beam sets, fixed spatiotemporal effective time periods, and standard movement paths, generating a prototype profile of the group and labeling the profile type as GROUP.

[0055] 13) Learn individual increments based on the group prototype profile and overlay them to obtain personalized spatiotemporal behavioral profiles.

[0056] In this step, the corresponding group prototype profile is used as the baseline template. Individual increments (i.e., independent time-series data of a single user) are input. The differential offset features of the user relative to the common behavior of the group are calculated through Markov migration probability model and lightweight sequence model. This is called individual increment Δ. The basic features of the group prototype and the individual increment features are superimposed and fused to obtain a personalized spatiotemporal behavior profile that adapts to the mobile and business habits of a single user. The profile type is marked as INDIV.

[0057] 14) A standardized spatiotemporal behavioral profile is obtained by uniformly encapsulating the personalized behavioral profile carrying the profile identifier, profile type, confidence level, spatiotemporal effective domain and prediction path summary; and the profile version and effective domain information of the spatiotemporal behavioral profile are sent to the unified data repository UDR to realize the management and backtracking of the version profile.

[0058] In this step, standardized fields are first bound to the overlaid and merged personalized spatiotemporal behavioral profiles. These fields may include: a unique profile ID (behaviorProfileId), profile type GROUP / INDIV, behavioral confidence level in the 0-1 range, spatiotemporal validity domain (validityTime time window + validityArea cell / geographic block range), and prediction path or prediction path summary (e.g., frequently visited cells, frequency point list, etc.). Next, an independent version number is assigned to each newly generated spatiotemporal behavioral profile, associated with its effective and expiration timestamps. Then, the complete spatiotemporal behavioral profile data, version identifier, and spatiotemporal validity domain information are synchronously pushed to UDR for unified data storage, enabling profile version management and backtracking. In other words, UDR provides unified control over all versions of spatiotemporal behavioral profiles. UDR stores all versions of profiles (i.e., spatiotemporal behavioral profiles) in partitions by user or user group; expired old versions are archived and retained, not directly deleted; and historical profiles can be retrieved by profile ID, version number, and time period for metric review, anomaly localization, and model iteration calibration. In other words, NWDAF uploads the complete spatiotemporal behavioral profile to UDR / UDM persistent storage, retaining the profile version, minimized identifier, and validity period verification information.

[0059] In this step, the prediction path is generated based on common features of the group. The confidence level (0~1) is configured so that the stronger the regularity of user behavior and the larger the historical sample size, the higher the confidence value. The profile validity domain parameters also need to be configured: validityTime, which represents the start and end time of the profile's effectiveness / periodic time period rule; and validityArea, which represents the tracking area (TA), cell list, and GeoHash geographical range covered by the profile. A unique de-identified behaviorProfileId profile ID is generated. Finally, the profile type, confidence level, validity domain, and prediction path are bound to obtain a complete spatiotemporal behavioral profile.

[0060] 15) Configure robust profile switching strategies for user cold start scenarios, including: prioritizing the reuse of group profiles in user cold start scenarios, or automatically downgrading to use group profiles or switching to baseline measurement mode when profile confidence is insufficient or users exceed the spatiotemporal validity domain of profiles.

[0061] In this step, a user cold start strategy is configured. For newly connected users with no historical data and no available individual incremental profiles, the corresponding group prototype profile is loaded first by default as the initial behavioral benchmark to support the generation of the reduction measurement strategy. Automatic degradation trigger conditions are also configured. This embodiment takes setting two types of degradation judgment thresholds as an example: confidence threshold, i.e., the real-time matching confidence of the profile is lower than a preset threshold; and spatiotemporal out-of-domain judgment, i.e., the UE's current location and running time exceed the spatiotemporal effective domain configured by the profile. Degradation branch rules are also configured, i.e., when any condition is triggered, the system automatically switches back to the group prototype profile generation measurement strategy; if the group profile still does not match, the reduction mode is completely abandoned and the system switches to full measurement without optimization baseline.

[0062] Furthermore, NWDAF can push the entire set of switching rules as policy auxiliary parameters along with the standardized spatiotemporal behavioral profile to PCF so that PCF can orchestrate PCC measurement constraint policies.

[0063] In this step, NWDAF pushes the profile data carrying the profile ID, confidence level, effective domain, and prediction path to the Policy Control Function (PCF) through the Nnwdaf-A1 interface, so that the PCF can generate a corresponding PCC policy based on the spatiotemporal behavioral profile.

[0064] In this embodiment, the PCF on the PCC policy network side is pre-generated based on the spatiotemporal behavioral features of the spatiotemporal behavioral profile in the following manner: 21) Analyze the received spatiotemporal behavior profile to obtain the profile confidence, spatiotemporal effective domain, prediction path, and spatiotemporal behavior features.

[0065] In this step, the PCF receives the spatiotemporal behavioral profile sent by the NWDAF and reads the profile version verification information from the UDR. It then parses and extracts key profile parameters, including: profile confidence, spatiotemporal effective domain (including effective time window, effective geographical area, etc.), predicted path, and user-side spatiotemporal behavioral characteristics. These may also include profile version references, data minimization rules, and global control policies configured on the operations side. The spatiotemporal behavioral characteristics include at least user mobility rate, frequent residence area, active service periods, and cell handover probability distribution.

[0066] 22) Determine the measurement strategy constraint level based on the confidence level of the profile.

[0067] In this step, the confidence level of the profile is used as the baseline weight for strategy orchestration: the higher the confidence level, the greater the reduction is allowed; the lower the confidence level, the tighter the reduction range is, converging towards the baseline measurement, thereby controlling the mobility risk caused by excessive reduction. For example, high confidence level corresponds to the first-level constraint: allowing the maximum reduction; medium confidence level corresponds to the second-level constraint: a moderate reduction range; and low confidence level corresponds to the third-level constraint: weak reduction, approaching the baseline measurement.

[0068] 23) Based on the predicted path and constraint level, target measurement objects and skippable objects are divided, and the spatiotemporal effective domain is set as the policy activation condition. Objects may include frequency points, neighboring cells, beam sets, etc. In this step, the handover probability distribution corresponding to each cell, frequency point, and beam within the predicted path, as well as the constraint level, are used for adjustment to distinguish the measurement objects. Specifically, this includes: 231) Resources with a switching probability higher than a preset threshold in the predicted path are identified as target measurement objects and used as the terminal priority measurement set.

[0069] 232) Mark resources that are not in the predicted path and whose switching probability is below the threshold as skippable objects, allowing the terminal to skip scanning.

[0070] 233) Bind the spatiotemporal effective domain of the spatiotemporal behavior profile to the effective constraint of the PCC strategy, so that the measurement object rules are only enabled in the corresponding time window and geographical area.

[0071] In this embodiment, the higher the constraint level, the more the object set can be expanded and the priority measurement object set can be shrunk; the lower the constraint level, the smaller the scope of exempted objects and the more priority measurement resources can be.

[0072] 24) Configure measurement gap parameters based on the spatiotemporal behavior characteristics.

[0073] In this step, the measurement gap parameters are adapted based on the user movement rate in the spatiotemporal behavior features obtained from the analysis. The adaptation principle is as follows: in high-speed movement scenarios, the measurement gap period is reduced and the measurement sampling frequency is increased; in low-speed or stationary scenarios, the measurement gap duty cycle is increased and the terminal measurement overhead is reduced.

[0074] Of course, the measurement gap parameters can also be configured in conjunction with the constraint level. The higher the constraint level, the lower the measurement frequency and the lower the energy consumption limit are allowed; the lower the constraint level, the measurement budget is automatically raised to converge to the baseline.

[0075] 25) Set the upper limit of the measurement resource budget based on the confidence level of the profile and the spatiotemporal behavioral characteristics.

[0076] In this step, based on the profile confidence level and the business type constraints in the spatiotemporal behavioral features, the upper limit of the measurement resource budget is configured, including the maximum number of measurement reports per unit time (maxReportsPerMin), and may also include the terminal measurement energy consumption limit (energyCap), etc.; when the confidence level is low, the measurement resource budget is automatically increased to approach the baseline measurement configuration.

[0077] 26) Configure the validity and fallback thresholds of the policy, i.e. the fallback thresholds for behavioral deviation.

[0078] In this step, the time window and geographical range of the spatiotemporal behavior profile are written into the validity as the boundary conditions for the entire set of PCC policies (referred to as policy validity boundary conditions); and fallback thresholds are configured, including handover missed trigger rate, RSRP deviation threshold, etc., for the RAN to determine whether it is necessary to fall back to the baseline measurement, etc.

[0079] Furthermore, the rollback threshold can be adjusted based on the constraint level of the measurement strategy. That is, the higher the constraint level, the smaller the behavior deviation threshold should be set, making it easier to trigger rollback; the lower the constraint level, the more relaxed the rollback threshold should be.

[0080] 27) Encapsulate the target measurement object, skippable object, measurement gap parameter, measurement resource budget limit, backoff threshold and spatiotemporal effective domain to generate the corresponding PCC strategy.

[0081] In this step, the target measurement object, skippable object, measurement gap parameters, measurement resource budget limit, fallback threshold, and measurement policy activation conditions are uniformly encapsulated into a policyTag to obtain the PCC policy. Of course, the encapsulated content is not limited to the above. The above-mentioned profile version reference information can also be encapsulated together. This embodiment does not impose any restrictions.

[0082] In this embodiment, the PCC strategy output by PCF incorporates the measurement control upper limit, thereby achieving constraint control over who to measure, when to measure, what to measure, and how much to measure.

[0083] In step 102, a predictive measurement template is generated based on the spatiotemporal behavior profile and the corresponding PCC strategy. The predictive measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile.

[0084] This step includes: 31) Analyzing the spatiotemporal behavior profile and the corresponding PCC strategy to obtain the prediction path, spatiotemporal behavior features and profile confidence.

[0085] In this step, gNB-CU-CP receives the PCC policy carrying the policyTag transmitted by AMF, parses the profile association information carried in the PCC policy, extracts the prediction path, spatiotemporal behavioral features, profile confidence, and can also read the backoff threshold, spatiotemporal effective domain, version reference information, etc., pre-configured by the PCC policy.

[0086] The predicted path may include: cells, frequencies, and beam sets that users are highly likely to access; the spatiotemporal behavioral characteristics include at least: user mobility rate, handover probability distribution, and active service periods; and the profile confidence score, which represents the degree of matching between the current spatiotemporal behavioral profile and the user's actual behavior.

[0087] 32) Based on the switching probability of each resource in the predicted path, divide the set of priority measurement objects into a set of skippable measurement objects.

[0088] In this step, the handover probability corresponding to each cell, frequency point, and beam within the predicted path is read; and the frequency points, neighboring cells, and beams with handover probabilities greater than a preset probability threshold are assigned to the priority measurement object set as resources that the terminal needs to focus on scanning and measuring; and the frequency points, neighboring cells, and beams that are not within the predicted path or whose handover probabilities are less than a preset probability threshold are assigned to the skippable measurement object set, allowing the terminal to be exempt from scanning and measuring.

[0089] Furthermore, for the template reference identifier of objectSelection within this PCC strategy, the local organization of measurement object rules is completed.

[0090] 33) Configure measurement gap parameters for the spatiotemporal behavioral features, and set the upper limit of measurement resource budget according to the confidence level of the profile.

[0091] In this step, the measurement gap parameters are configured based on the user movement rate from the analyzed spatiotemporal behavioral characteristics. The configuration rules include: when the user is moving at high speed, a shorter measurement gap period is configured to increase the measurement sampling frequency; when the user is moving at low speed or stationary, the measurement gap duty cycle is increased to reduce terminal measurement overhead. Subsequently, the upper limit of the measurement resource budget is constrained according to the profile confidence level. Specifically, when the profile confidence is high (high confidence), a relaxed resource budget is adopted, allowing for lower measurement reporting frequency and lower energy consumption limits; when the profile confidence is medium, the measurement resource budget is moderately increased, and the scope of exempted resources is reduced; when the profile confidence is low, the measurement resource budget is further increased, converging towards the budget level of the baseline full measurement, suppressing the movement risk caused by excessive reduction. The upper limit of the measurement resource budget includes the maximum number of measurement reports per unit time and the terminal measurement energy consumption limit.

[0092] 34) Integrate the set of priority measurement objects, the set of skippable measurement objects, the measurement gap parameter, and the upper limit of the measurement resource budget to generate a reduction measurement strategy adapted to the spatiotemporal behavior profile; In this step, the differentiated measurement object rules, consisting of the priority measurement object set and the skippable measurement object set, are integrated with the configured measurement gap parameters and the upper limit of the measurement resource budget after hierarchical constraints; thus, a reduced measurement strategy adapted to the spatiotemporal behavior profile is obtained.

[0093] Furthermore, this implementation can also construct a complete reduction measurement strategy based on the spatiotemporal effective domain (effective time window, effective geographical area) and behavior deviation backoff threshold of the PCC strategy. This reduction measurement strategy restricts the terminal to perform lightweight measurements only within the spatiotemporal effective domain.

[0094] 35) Encapsulate the reduction measurement strategy to obtain the prediction measurement template of the spatiotemporal behavior profile.

[0095] In this step, the aforementioned reduction measurement strategy can be standardized and encapsulated to generate a predictive measurement template. The predictive measurement template fully carries the priority measurement object set, the skippable measurement object set, measurement gap parameters, measurement resource budget upper limit, spatiotemporal effective domain, and backoff threshold. Furthermore, this predictive measurement template, carrying a corresponding profile version identifier, is sent to the terminal UE so that the terminal can parse and execute targeted reduction measurements.

[0096] In step 103, a predictive measurement template configured with the reduction measurement strategy is sent to the terminal so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

[0097] In this step, within the effective domain of the spatiotemporal behavior profile (a specific time window or area), a predictive measurement template configured with the reduction measurement strategy is sent to the terminal, so that the terminal can perform targeted reduction wireless access measurements based on the reduction measurement strategy. In this embodiment, the specific time window can be the user's regular business activity period, periodic commuting stay period, or the overall effective lifecycle range of the profile, etc.

[0098] In this embodiment, a spatiotemporal behavioral profile of a user or user group and corresponding policies and billing control (PCC) policies are obtained. The spatiotemporal behavioral profile is constructed based on the user's or user group's historical cell dwell sequence, location trajectory, and service data. The PCC policies are generated based on the spatiotemporal behavioral features of the spatiotemporal behavioral profile. A predictive measurement template is generated based on the spatiotemporal behavioral profile and the corresponding PCC policies. The predictive measurement template is configured with a reduction measurement policy adapted to the spatiotemporal behavioral profile. The predictive measurement template configured with the reduction measurement policy is sent to the terminal so that the terminal can perform targeted reduction wireless access measurements based on the reduction measurement policy. In other words, this application accurately depicts the user's mobile behavior patterns by constructing a user spatiotemporal behavioral profile, adaptively generates differentiated measurement policies based on the profile features, and solidifies them into a predictive measurement template adapted to user behavior, which is then sent to the terminal to enable the terminal to perform targeted reduction wireless measurements. This application achieves step-by-step optimization from precise behavior modeling and progressive policy adaptation to lightweight terminal measurement. While ensuring mobility and measurement reliability, it enables the terminal to effectively reduce measurement signaling overhead caused by invalid RF scanning, reduce terminal power consumption and network load, and achieve wireless measurement accuracy.

[0099] Optionally, in another embodiment, based on the above embodiments, the method may further include: receiving a behavior deviation reported by the terminal, wherein the behavior deviation is the deviation value between the measured behavior data obtained by the terminal performing decremental measurement based on the prediction measurement template and the predicted behavior data of the spatiotemporal behavior profile; obtaining a preset backoff threshold configured based on the profile confidence in the PCC strategy; and when the behavior deviation exceeds the preset backoff threshold, sending a baseline full measurement configuration to the terminal to trigger a fast backoff mechanism so that the terminal terminates the decremental measurement and switches to baseline full measurement.

[0100] In this embodiment, firstly, during the directional reduction measurement process performed by the terminal based on the predicted measurement template, the measured wireless trajectory, signal quality, and predicted behavior of the spatiotemporal behavior profile are compared, and the behavior deviation is calculated based on the comparison results; the terminal then sends a signal to the gNB. CU The CP periodically reports a behavioral deviation value, which characterizes the degree of discrepancy between the terminal's actual movement behavior and the predicted behavior. This is equivalent to gNB. CU The CP receives the behavior deviation value periodically reported by the terminal.

[0101] Secondly, when receiving the behavior deviation value reported by the terminal, the preset backoff threshold carried in the PCC policy stored locally is read. This backoff threshold is configured by PCF based on the profile confidence level: the higher the profile confidence, the smaller the backoff threshold is set, and the easier it is to trigger a backoff; the lower the profile confidence, the more relaxed the backoff threshold is.

[0102] Next, the behavior deviation is compared with a preset rollback threshold. When the behavior deviation is less than or equal to the preset rollback threshold, it is determined that the user's actual behavior still conforms to the profile prediction, the current prediction measurement template remains unchanged, and the terminal continues to perform reduced measurement. When the behavior deviation is greater than the preset rollback threshold, it is determined that the user's actual behavior has significantly deviated from the profile prediction path, the reduced measurement has a risk of missed switching trigger, and the fast rollback mechanism is triggered.

[0103] Finally, the baseline full measurement configuration was distributed. In this step, after the fast rollback mechanism is triggered, gNB CU The CP generates a baseline full measurement configuration and sends it to the terminal via RRC reconfiguration signaling. The baseline full measurement configuration cancels the distinction between priority / skip measurement objects, restores the full scan measurement of all neighboring cells, frequency points, and beams, and cancels the measurement gap and measurement resource budget restrictions corresponding to the reduction mode.

[0104] In this embodiment, when the terminal receives the baseline full measurement configuration, it switches the measurement mode and completes the fallback. That is, when the terminal receives the baseline full measurement configuration, it stops executing the reduction measurement strategy corresponding to the predictive measurement template and switches to the baseline full measurement mode to perform wireless access measurement; Optionally, in another embodiment, based on the above embodiments, the method may further include: receiving measurement acceptance and budget consumption data reported by the terminal; dynamically adjusting the upper limit of the measurement resource budget within the predicted measurement template based on the measurement acceptance and budget consumption data; and / or periodically acquiring various key performance indicators after the terminal reduces the measurement according to the predicted measurement template; and transmitting the various key performance indicators back to the core network processing node so that the core network processing node iteratively updates the spatiotemporal behavior profile and the corresponding PCC strategy, thereby realizing dynamic calibration and continuous optimization of the wireless measurement strategy.

[0105] In this embodiment, gNB CU CP can use this rollback event as a KPI indicator and feed it back to NWDAF so that NWDAF can use it for subsequent spatiotemporal behavior profiling model iteration and optimization.

[0106] Optionally, in another embodiment, based on the above embodiments, the method is also applied to low-altitude security scenarios, where the user or user group is a low-altitude flight device equipped with a cellular communication module; the spatiotemporal behavior profile is used to characterize the flight path trajectory, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of the low-altitude flight device; the PCC strategy and predictive measurement template are adapted to the authorized flight time periods, permitted altitude zones, restricted flight areas, and security levels of low-altitude routes in airport airspace, energy parks, ports, and cities, so as to achieve differentiated and targeted reduction of wireless measurement and control in low-altitude scenarios.

[0107] In this embodiment, for low-altitude security scenarios, the UDR / UDM manages the spatiotemporal behavior profile and corresponding PCC strategy in a versioned manner. When airspace authorization rules, restricted flight zones, or flight mission windows change, the NWDAF generates a new version of the spatiotemporal behavior profile, and the PCF generates a new version of the PCC strategy, invalidating the old version, thus achieving auditability, revocation, and rollback of the security strategy. The gNB-CU-CP compares the cell / beam sequence, position, altitude, and speed obtained from flight measurements with the predicted path of the spatiotemporal behavior profile, calculates the behavior deviation with weights, and identifies route deviation, intrusion into restricted flight zones, abnormal hovering, and signal issues. Abnormal low-altitude behaviors such as sudden changes; when the deviation exceeds a preset threshold or a high-level alarm is received from the low-altitude security platform via AF, the gNB-CU-CP and the aircraft terminal UE synchronously exit the reduced measurement mode and restore the baseline full measurement configuration; the gNB-CU-UP, gNB-CU-CP, or OAM aggregation network runs KPI data and feeds it back to NWDAF, forming a three-level closed-loop calibration mechanism of terminal local fast loop, RAN near loop, and core network far loop. The NWDAF iteratively optimizes the spatiotemporal behavior profile, and the PCF iteratively optimizes the PCC strategy to achieve adaptive optimization of low-altitude flight measurement performance and security monitoring accuracy. The specific implementation process is detailed in the corresponding embodiments below and will not be repeated here.

[0108] Please also see Figure 2 This is another flowchart of a measurement and control method based on user behavior profiles provided in this application embodiment, the method comprising: Step 201: Receive the prediction measurement template sent by the network side. The prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group.

[0109] The content of the reduction measurement strategy in this step is detailed above and will not be repeated here.

[0110] Step 202: Perform directional wireless access reduction measurement according to the reduction measurement strategy.

[0111] This step includes: 41) parsing the reduction measurement strategy in the predicted measurement template to obtain the target measurement object, the skippable measurement object, the measurement gap parameter and the upper limit of the measurement resource budget; In this step, the terminal receives the predictive measurement template issued by gNB-CU-CP, parses the reduction measurement strategy in the predictive measurement template, and extracts the target measurement object, the skippable measurement object, the measurement gap parameter, and the upper limit of the measurement resource budget; at the same time, it parses the spatiotemporal effective domain bound to the strategy and the rollback related configuration.

[0112] The target measurement objects are frequency points, neighboring cells, beams, etc., that require focused scanning; skippable measurement objects are frequency points, neighboring cells, beams, etc., that are allowed to be exempted from scanning; measurement gap parameters include measurement gap period, measurement gap duty cycle, etc.; the upper limit of measurement resource budget includes the maximum number of RF scans per unit time, measurement power consumption limit, etc. However, it is important to note that there were actually two Yan states during the Zhou Dynasty. The aforementioned Yan state, which originated from Duke Shao, is historically known as Northern Yan. There was also a Southern Yan, located west of Ji County, Henan Province, which was a fiefdom of a descendant of the Yellow Emperor. In practical applications, it is not limited to this.

[0113] 42) When the current location and time period match the spatiotemporal effective domain of the spatiotemporal behavior profile, enable the reduction measurement.

[0114] In this step, the terminal obtains its current real-time location information and system time; it then compares whether the current location and current time period simultaneously match the spatiotemporal effective domain (effective geographical area and effective time window) of the spatiotemporal behavior profile. If both the location and time period match the spatiotemporal effective domain, the reduced measurement mode is activated, and the subsequent directional reduced measurement process is executed; if the location does not match, or the time period is not within the effective time window, or both are inappropriate, the reduced measurement is not activated, and baseline full-scale wireless measurement is executed directly.

[0115] 43) Filter the skippable measurement objects, perform periodic wireless measurements on the target measurement objects according to the measurement gap parameters, monitor the number of radio frequency scans, and detect that the energy consumption is controlled within the upper limit of the measurement resource budget.

[0116] In this step, before the terminal performs cell and beam scanning, it matches and compares the set of resources to be scanned with the objects that can be skipped for measurement. It directly filters and skips the radio frequency scanning of the objects that can be skipped for measurement, and no longer allocates radio frequency measurement resources to them.

[0117] In this embodiment, wireless access measurements are periodically performed only on the target measurement object, strictly following the period and duty cycle set by the measurement gap parameters, and wireless measurement data such as RSRP and SINR are collected.

[0118] The terminal locally counts the number of RF scans and the energy consumption generated by the measurement in real time. When either the number of RF scans or the energy consumption reaches the upper limit of the measurement resource budget, the addition of new measurement tasks is suspended and the statistical cycle is reset to ensure that the measurement resources are not exceeded.

[0119] 44) Calculate the behavior deviation by combining the wireless data of the measured behavior with the predicted behavior data of the spatiotemporal behavior profile.

[0120] In this step, the terminal extracts the measured wireless data and the actual cell sequence obtained from the measurement, and compares them with the predicted behavior data (predicted cell, predicted signal level, and predicted movement path) corresponding to the spatiotemporal behavior profile carried by the local predicted measurement template. That is, it quantifies the behavior deviation value, which represents the degree to which the measured behavior deviates from the predicted behavior. The behavior deviation value is cached locally and awaits periodic reporting to the gNB. CU CP is used by the network side to make rollback decisions.

[0121] Step 203: Calculate the deviation of the measured behavior data from the predicted behavior data of the spatiotemporal behavior profile.

[0122] The statistical methods used in this step are already familiar to skilled technicians and will not be elaborated upon here.

[0123] Step 204: Report the behavior deviation to the network side so that the network side can determine whether to trigger the fast rollback mechanism of the baseline full measurement.

[0124] In this step, after the terminal completes the behavior deviation calculation locally, it determines whether to report according to the preset reporting rules. The reporting method can be periodic reporting or triggered reporting. Periodic reporting is triggered according to the preset reporting period; triggered reporting is reported immediately when the behavior deviation reaches the preset local alarm threshold.

[0125] In this step, the terminal encapsulates the calculated behavioral deviation value, along with the current cell identifier and measurement timestamp, into an RRC signaling measurement reporting message. The message carries the corresponding profile ID, which facilitates the network side in associating the corresponding spatiotemporal behavioral profile with the PCC policy.

[0126] In this step, the terminal sends a reporting message to the gNB-CU-CP. Specifically, the encapsulated measurement reporting message can be sent to the gNB via the air interface. CU After the CP is reported, the terminal continues to maintain the current reduction measurement process and waits for the network side to issue a measurement mode reconfiguration command.

[0127] Furthermore, the method may also include: receiving a measurement reconfiguration command issued by the network side, and maintaining the reduced measurement mode or switching to the baseline full measurement mode according to the command.

[0128] In this step, the calculated behavioral deviation is encapsulated into a measurement reporting message and reported to the network-side gNB. CU CP is used by the network side to determine whether to trigger a fast rollback mechanism for full baseline measurement based on a preset rollback threshold; it receives measurement reconfiguration instructions from the network side and maintains the reduced measurement mode or switches to the full baseline measurement mode according to the instructions.

[0129] Specifically, the terminal receives and responds to the network's decision. Specifically, if the terminal does not receive the baseline full measurement configuration signaling, it indicates that the network determines no rollback is needed, and the terminal continues to perform reduced measurement, calculate, and report the behavior deviation as needed. If the terminal receives the RRC reconfiguration signaling from the network, and the signaling carries the baseline full measurement configuration, the terminal stops reduced measurement, unloads the reduced measurement strategy parameters from the predictive measurement template, and switches to the baseline full measurement mode.

[0130] In this embodiment, the terminal performs targeted reduction measurement based on the prediction measurement template, calculates the deviation of the measured behavior from the predicted profile, and reports it to the network side as needed. While reducing the terminal's measurement power consumption and reducing air interface signaling overhead, the reporting of behavior deviation enables controllable fallback and ensures mobile access performance.

[0131] Please also see Figure 3 This figure illustrates an application of a measurement and control method based on user behavior profiling, as provided in this embodiment. The network involved in this embodiment is divided into three layers: the core network layer (NWDAF / PCF / AMF / UDR), the radio access network layer (gNB-CU-CP, gNB-DU, gNB-CU-UP), and the terminal UE layer. The signaling interactions between network elements and interfaces at each layer correspond one-to-one with four major technical mechanisms: offline profiling, predictive template distribution, online execution backoff, and closed-loop calibration. Specifically, these include: I. Core network layer elements, used to complete offline profile construction, policy orchestration, and persistent profile storage, including: 1) Network Data Analytics Function (NWDAF), i.e. No. 5: Based on the user's historical cell sequence, location trajectory, and service type data, complete the inference of individual / group spatiotemporal behavior profiles, output profile ID, confidence level, validity period, etc., and realize offline profile modeling; push the profile results to PCF via interface A1.

[0132] In this embodiment, the NWDAF is responsible for: generating spatiotemporal behavioral profiles of users / groups and providing confidence scores and effective domains (time period, geographical range). It also receives RAN feedback KPIs for model closed-loop updates.

[0133] The acquired location / mobility events, historical measurement statistics, user plane / control plane KPIs (optionally via OAM / SMF / UPF), and service / slice prompts (AF, optional) are input into the NWDAF for processing. The processing results are then output to the PCF / AMF, etc. The processing results may include: behaviorProfileId (profile ID, de-identified), profileType (GROUP / INDIV), confidence (0~1), validityTime (start and end or time period rule), validityArea (TA / cell list or GeoHash). (Optional) predictedPath (summary of frequently visited cells / frequency points / beam sets).

[0134] The NWDAF (Non-Wideband Data Environment) structures the predictable patterns of "person-time-place" to form the basis for the data reduction strategy. In this embodiment, the key innovations of NWDAF are: ⑤ a two-level profile of groups and individuals; and ⑥ a closed-loop calibration data source. In other words, without sacrificing privacy and scalability, it uses a group prototype to cover the majority of users and individual incremental services for differentiation, forming a low-cost, high-precision profile of "prototype + individual Δ".

[0135] The innovative details of the two-level profiling system (group / individual) are as follows: NWDAF, based on de-identified mobile and wireless statistics (cell / GeoHash sequences, parking and event triggering, RSRP distribution, and service time information), first extracts "group prototype profiles" offline, clustering users with similar spatiotemporal paths and wireless patterns into several prototypes, and generating a Top-N candidate cell / frequency / beam set within a specific time window and geographical range. Based on this, for each individual user, only the "individual increment" relative to the prototype is learned, and the prototype is fine-tuned using a lightweight sequence model or Markov transition probability to form a low-cost personalized profile of prototype + Δ. The system outputs behaviorProfileId, profileType (GROUP / INDIV), confidence, and validityTime / Area (with predictedPath summary if necessary) for each UE, and hosts the version and valid domain on the UDR to meet governance and backtracking requirements. The operational strategy (i.e., the robust profile switching strategy) is as follows: cold starts prioritize the use of group profiles, and individual incremental profiles are automatically switched / overlaid as data accumulates; when the confidence level decreases or goes out of bounds, the profiles are automatically downgraded back to the group / baseline to ensure robustness and support the subsequent measurement strategy orchestration of PCF.

[0136] This embodiment also provides innovative data source implementation for closed-loop calibration, specifically including: RAN side (CU-UP / CP or OAM) periodically or event-wise feedback of execution effects and KPIs to NWDAF, including: measurement reporting frequency and RRC measurement signaling volume, handover triggering / failure and RLF, throughput / latency retention, UE power consumption estimation, and budget utilization and deviation indicators reported by the UE through UAI; NWDAF performs online monitoring and drift detection of these signals, adjusting according to a "three-layer closed loop": UE local fast backoff to ensure RRM, RAN near-loop fine-tuning of PMT objects / gap / budget, and NWDAF far-loop reassessing profile confidence and effective domain and driving PCF to issue new strategies. To balance benefits and risks, the system can adopt multi-arm gambling or Bayesian strategies to adaptively select between different "budget strengths," and gradually take effect through A / B canary releases and UDR version governance; when observations deteriorate or domain / version mismatch occurs, backoff or reversal is triggered. The result of the closed loop is to continuously reduce measurement and signaling while maintaining handover and experience without degradation.

[0137] 2) Policy Control Function (PCF): Receives profile data and orchestrates policies, encapsulating measurement budgets, measurement rules, and applicable time / area scope into PCC policies, which are then sent to the AMF via interface A2. Its function is to translate spatiotemporal behavioral profiles into executable PCC policies and set the policy's boundary conditions and governance constraints.

[0138] In this embodiment, the spatiotemporal behavioral profile, confidence level, and effective domain generated by NWDAF are obtained; the version, consent, and minimization rules of the spatiotemporal behavioral profile are obtained from UDR; and the operation-side policies are output to PCF for processing. After processing, a policy tag is output to AMF, which includes: measurement budget: maximum number of measurement reports per minute (maxReportsPerMin), measurement gap duty cycle percentage (gapDutyCyclePct), and may also include measurement energy consumption limit (energyCap), etc.

[0139] Measurement target object selection: priority / skip frequency points / neighboring cells / beam sets (referencing network-side template ID).

[0140] Validity: time window / geographic range; fallback thresholds, such as HO missed trigger rate, RSRP deviation, etc.

[0141] Version references: Profile version reference (profileVersionRef) points to the spatiotemporal behavior profile version identifier in UDR storage, used for version verification and backtracking; Template version reference (templateVersionRef) points to the prediction measurement template version identifier in UDR storage. Both refer to UDR records.

[0142] This application defines the upper limit framework for "who to measure, when to measure, what to measure, and how much to measure".

[0143] 3) Access and Mobility Management Function (AMF): Transmits UE context, profile label, and policy label to the gNB via A3 (NGAP / N2 interface). CU CP;AF is an optional application strategy unit, and SMF / UPF is an optional KPI auxiliary unit, which work together with the main process to complete the policy transmission.

[0144] The Access and Mobility Management Function (AMF) is responsible for access / mobility control and UE context maintenance; it delivers user profiles and policies to the RAN along with the context. Its inputs are the policy tag issued by the PCF and UE registration / mobility events. Its outputs to the gNB-CU-CP carry the following in the N2 / NGAP context creation / modification: behavior profile ID, policy tag, and necessary execution prompts (such as whether to immediately trigger RRC reconfiguration). The behavior profile ID is a unique identifier for the spatiotemporal behavior profile of a user or user group; the policy tag is a tag carrier encapsulating the entire set of PCC constraint parameters measured by the PCF. In this application, the AMF bearer channel ensures that the RAN can obtain the profile and policy at the UE level.

[0145] 4) User Data Repository / Management Function (UDR / UDM, User Data Repository / Management) (labeled 3): Persistently store the profile version, template version, and minimized identifier, and feed back the profile validity period and version information to NWDAF and PCF through interface A9, and limit the valid domain of the profile (time window, geographical region). UDR / UDM is used to store behavioral profile IDs, profile version references, template version references, and data minimization identifiers, providing version verification and data retrieval capabilities to NWDAF and PCF. In other words, it governs profile / template versions and effective domains, as well as privacy-minimizing information (such as consent status, de-identification mapping, etc.).

[0146] Input / Output: Allows NWDAF / PCF to read and write profileVersion, templateVersion, validityArea / Time, minimizationFlags, etc.

[0147] In this application, UDR / UDM can guarantee that the policy is retrospective, revocable, and can be upgraded on a rolling basis.

[0148] In this application embodiment, the key innovations of UDR / UDM include: ③ Valid Domain and Version Control: This application abstracts the governance information of "profiles / templates" into governance records in UDR / UDM, uniformly managing their versions, validity periods, and applicable regions, and binding them to privacy minimization. UDR maintains two types of objects: ProfileRecord and Measurement Template Policy (TemplateRecord). Key fields include: {id, version, validityTime, validityArea (cell / TA or GeoHash set), consentRef (agreement / withdrawal), minimizationFlags (de-identification / granularity restrictions), rolloutState (draft / canary / active / deprecated / withdrawn), compat (RAN capability constraints), signature (integrity)}. When publishing or updating a profile, NWDAF first writes to / upgrades the ProfileRecord. PCF references ProfileVersion and TemplateVersion when generating policies. AMF / CU-CP performs version / domain verification before taking effect. Policies exceeding validityTime or accessed from outside the domain will not take effect or will automatically downgrade to the baseline policy. UDR supports atomic version transitions and rollbacks (draft→canary→active→deprecated→withdrawn) and canary deployments by region / time period, while retaining audit logs and signatures to ensure traceability and tamper-proofing. ConsentRef and MinimizationFlags ensure that profiles are only referenced in de-identified form (e.g., behaviorProfileId), and the actual identifier mapping is stored in a closed loop only on the UDM side, without being leaked to the RAN. By carrying profileVersionRef / templateVersionRef in policyTag and PMT, a consistent version view is formed across all network layers: one-stop upgrades are visible across the entire network, and one-click reversal is possible in case of anomalies, thus achieving standardized governance capabilities that are traceable, reversible, and allow for rolling upgrades.

[0149] Optionally, AF (Area Advisory) provides scenario-based suggestions (such as park schedules, activity periods, and private network slice preferences) to NWDAF / PCF, influencing validityTime / Area and object priority in the form of non-PII summary (time window labels, area labels, and business categories). This information is also registered in UDR's TemplateRecord.rolloutState / compat for canary deployment and auditing. AF does not directly determine the template, but only modulates the profile and strategy to enable the private network / park to achieve better spatiotemporal matching without increasing the exposure of personal data.

[0150] Optional, SMF / UPF: Closed-Loop Supply of User-Face KPIs (Overall Description) SMF / UPF provides user-face KPIs (throughput, latency, packet loss, jitter, etc.) to NWDAF through aggregation and sampling. This is used to calculate experience retention after template activation and trigger remote calibration. Data is aggregated by profile / template version, constrained by minimizationFlags and retentionPolicy in UDR, ensuring minimization and revocability. NWDAF updates confidence / validity accordingly and drives PCF to generate a new templateVersion. UDR is responsible for version integration and canary rollout, ensuring controllable and auditable closed-loop optimization.

[0151] II. Wireless access network layer elements, used to parse PCC policies, generate predictive measurement templates, and collect operational metrics for closed-loop optimization, including: 1) gNB-CU-CP (label 1): Receives the profile and policy label issued by AMF, generates Predictive Measurement Template (PMT), and writes the priority measurement neighbor cell / beam list, exempted objects, measurement gap duty cycle, and energy consumption / number budget limit into the template; sends the PMT to the terminal via A4 (Uu / RRC interface) so that the UE can carry out targeted reduction measurement, and implements the template management logic in step 2.

[0152] The gNB-CU-CP (control plane, PMT orchestrator) is responsible for RRC control, neighbor cell / frequency / beam management, translating the behaviorProfileId + policyTag from the core network into an executable measurement template (PMT). It also determines at the UE level whether the UE is in a valid domain, deciding when to issue / update the PMT; and triggers fast-back-out RRC reconfiguration when necessary.

[0153] gNB-CU-CP inputs from AMF (A3): behaviorProfileId, policyTag (including budget / object selection / valid domain / backoff threshold / version reference). Local inputs: neighboring cells / frequency points / beam library, UE capabilities (whether certain measurements / gap are supported), cell / TA information.

[0154] Output / Interface: For UE (A4): RRCReconfiguration carries PMT. For DU (A6): F1AP issues Predictive-Meas-Config (for DU execution). For NWDAF (A7, can coordinate with CU-UP / OAM): Reports execution results KQI / KPI (optionally returned by CU-UP / OAM).

[0155] In this embodiment, the key innovation of the gNB-CU-CP network element is as follows: Innovation ① focuses on profile-driven PMT; it also incorporates soft constraints such as budget / backoff thresholds into the UE execution layer. In this application, gNB-CU-CP acts as a "PMT orchestrator": it combines the behaviorProfileId and policyTag (including object selection, measurement budget, effective domain and backoff threshold, version reference, etc.) issued by the core network along with N2 / NGAP with local neighbor cells / frequency points / beams and UE capabilities to compile an executable predictive measurement template (PMT), and then issues it to the UE via RRC (A4). The orchestration process uses the effective domain and confidence threshold as prerequisites, enabling measurement reduction only when the profile is reliable and within the domain. Subsequently, frequency points / neighboring cells / beams are scored based on "profile hit rate, handover requirements, skip risk, and experience benefits," generating priority / skip sets and Top-K targets. Then, the measurement budget, gap duty cycle, and event thresholds for strong fallback and weak exploration are constrained by the policy tag, and written into the security guard (back-off threshold and hold time) to ensure that the 3GPP RRM baseline is not exceeded under any circumstances. The same intent is deployed to the DU execution configuration via F1AP (A6) to reduce irrelevant scans. During execution, the UE provides extended feedback on acceptance / budget / deviation through UAI, and the CU-CP performs near-online fine-tuning or triggers fast back-off accordingly. The PMT carries templateVersionRef / profileVersionRef and validityTime / Area, aligning with the UDR version / domain governance closed loop. KPIs can be fed back from CU-UP / OAM to NWDAF (A7) to support remote calibration. Thus, the process of profiling → PMT → execution / feedback → governance / closed loop forms an end-to-end link, significantly reducing measurement and signaling overhead without sacrificing handover and coverage reliability.

[0156]

[0157] 2) gNB-CU-UP (No. 6, optional, user plane / KPI aggregation): Collects KPI performance data such as handover hold rate, handover failure rate, and measurement reporting hit rate, and transmits the performance indicators back to the upper-layer network elements to provide a data source for closed-loop calibration, realizing the closed-loop learning of the periodic iterative profiling and optimization template in step 4.

[0158] Among them, gNB-CU-UP is used to aggregate PDCP / RLC side statistics, latency / throughput, RLC retransmission, etc.; together with OAM, it forms execution effect / KPI feedback.

[0159] Its inputs come from statistics from DU / CU-CP, OAM counters, and UE-side reports (indirectly).

[0160] Output / Interface: For NWDAF (A7): Periodic or event-driven reflux of KPI / KQI. For CU-CP: Assisted parameter tuning / triggering template updates (optional for local optimization).

[0161] The key innovation of this gNB-CU-UP network element lies in innovation ⑥: data provider for closed-loop calibration. As the convergence point of user plane and near real-time statistics, gNB-CU-UP aggregates PDCP / RLC layer counters, latency / throughput, retransmission / packet loss, jitter, and measurement-related counts reported by CU-CP / DU in a unified manner according to version and profile dimensions: using {templateVersionRef,profileVersionRef, cell / TA, timeBucket} as the key, it forms an objective profile of "template effectiveness - experience retention". Its output is in two parallel paths: ① pushes KPIs / KQIs (such as measReportsCount, rrcMeasSigLoad, hoTriggerRate / FailRate, RLF, QoSkeep (throughput / RTT retention)) to NWDAF via Nnwdaf / O1 periodically or event-driven, along with budget utilization and power consumption proxy (power consumption estimates composed of DRX / Gap duty cycle, reporting frequency and RLC retransmission); ② provides near-loop parameter tuning hints to CU-CP for fine-tuning object priority, threshold and gap duty cycle in PMT. To ensure robustness and governability, CU-UP performs sliding window aggregation and drift detection (ADWIN / CUSUM) and benefit assessment locally: gain = (baseline_meas - current_meas) / baseline_meas. If any RRM guardrail (such as HO failure rate, RLF) exceeds its limit or the benefit falls below the threshold, a "risk template" is immediately marked, triggering a rapid rollback or reduction in strength in CU-CP. Simultaneously, abnormal events and statistical evidence are fed back to NWDAF, driving PCF to re-orchestrate the strategy and complete version canary release / rollback in UDR. In strategy exploration, CU-UP can abstract "budget strength (strong / medium / weak)" as a multi-armed gambling problem (UCB / Thompson). Under a guardrail optimization framework that satisfies SLO (such as "HO failure rate not exceeding baseline + δ"), it adaptively selects the optimal strength and gradually increases the release volume through a canary → active canary release path. The entire closed loop solidifies the process of "facts (KPI / KQI) → calibration (NWDAF / PCF) → activation (PMT / F1) → re-observation" into a standardized data surface, ensuring both privacy minimization and auditability (aggregation to version / region granularity, without PII), while also enabling the sustainable achievement of the goal of "increased testing benefits and no degradation in user experience".

[0162] 3) gNB-DU (Distributed Unit): Receives F1 configuration commands sent by gNB-CU-UP through A6 (F1AP interface) and performs cell scanning and local measurement tasks of beam list.

[0163] Among them, gNB-DU is responsible for performing MAC / PHY measurements / scans at the near end; it performs cropping and prioritization of neighboring cells / frequency points / beams according to the configuration issued by F1; and it ensures RRM compliance.

[0164] gNB-DU inputs come from CU-CP (A6): allowedScanSet, scanSuppression, taskSchedule. Local inputs include: cell load / interference status, anchor point neighbor table.

[0165] Output / Interface: To CU-CP: Execution results and necessary exception indications (such as "target missing"). To KPI (optional): Local counters.

[0166] In this application, the network-side portion of PMT is implemented locally, reducing unnecessary measurements.

[0167] III. Terminal UE layer network elements, used for template execution, status reporting, and anomaly rollback, including: 1) UE Measurement Agent: Used for template-driven execution / budget control. Specifically, it is used to parse the received PMT predictive measurement template, perform targeted decrement measurement according to the template constraints, and manage the measurement budget and terminal power consumption.

[0168] The UE Measurement Agent is used to parse PMT; reduce measurements by objectSelection + budget + gapPattern; maintain local counts and deviation detection; and proactively alert or trigger rollback when the budget is about to run out or the deviation is abnormal.

[0169] Inputs: PMT (A4); UE's own capabilities / power consumption status; actual measurement samples (RSRP / RSRQ / SINR / SSB Beam, etc.).

[0170] Output / Interface: Measurement report (tailored to template); UAI extension (A5); rollback trigger (A8). In this application, it serves as the template execution end and the first line of defense for robustness.

[0171] Local bias detection (using this as an example, but not limited to this in actual applications): Top-N neighbor hit rate: The overlap between predicted Top-N and actual reported Top-N is less than the threshold. RSRP distribution drift: The KL / KS distance from the template expected (or profile derived) distribution is greater than the threshold. Event missed triggers / timeouts: The missed detection rate of events such as A3 / A5 is greater than the threshold. Budget pressure: usedReportsPerMin / maxReportsPerMin is greater than β (e.g., 0.8).

[0172] 2) UE Assistance Info Extension Unit (Label 2): ​​Sends data to gNB via A5 (Uu / RRC). CU UPs report feedback data such as measurement acceptance, budget consumption, and behavioral deviation.

[0173] Among them, the UE Assistance Info extension (UAI, end-to-end network collaboration) performs quantitative negotiation with the network: indicating the template's acceptance, acceptable budget, and observation bias, driving network fine-tuning or rollback. Interfaces / Fields (examples, can be written into patent IE semantics): pmtAcceptance: ACCEPT / PARTIAL / REJECT + cause (insufficient capacity / power consumption limit / conflict). measurementBudget: maxReportsPerMin, maxMeasPerPeriod, gapDutyCycleHint, energyCapHint. observedDeviation: quantifiable metrics such as topNHitRate, rsrpDriftScore, eventMissRate, locationDrift, etc. validityFeedback: a notification indicating out-of-domain / expired status (used for version / domain verification).

[0174] The key innovation of the UE Assistance Info network element lies in innovation ②: focusing on end-to-end network collaboration and quantifiable test reduction, allowing the network to adjust PMT according to the UE's actual capabilities. This application extends UE Assistance Information (UAI) on the air interface RRC, making the UE's real-time declaration of the template a quantifiable input. This includes: template acceptance and reasons (executable / partially executable / rejected), acceptable measurement budget (reporting per minute / number of measurements per cycle / gap duty cycle / power consumption limit, etc.), observed deviation indicators (such as Top-N neighbor cell hit rate, RSRP drift, event missed trigger, location drift), and spatiotemporal / version validity feedback (outside the domain, expired, version inconsistency). Based on this, gNB-CU-CP performs lightweight adaptive testing: first, it determines the basic state (maintain / de-enhance / rollback) based on acceptance, then combines the comprehensive pressure score of "budget utilization rate + deviation indicators" to choose incremental adjustment of PMT (tightening Top-K objects, raising the threshold, reducing duty cycle or budget) or triggering fast rollback; the entire process is constrained by the 3GPP RRM baseline and UE capability guardrail, and hysteresis and minimum reconfiguration intervals are set, with small-scale gray-scale verification performed when necessary. Sessions adjusted by UAI are tagged and fed back to NWDAF along with KPIs for remote updates of policies and profiles. This transforms the "network's assumptions about the UE" into "the UE's real-time claims," ​​forming a quantifiable measurement reduction closed loop through end-network collaboration, which significantly reduces measurement and signaling without sacrificing handover and coverage reliability.

[0175] 3) Fast fallback mechanism (Fallback), i.e., label 4: When the deviation between the UE's actual movement trajectory, service behavior, and profile prediction exceeds a preset threshold, an alarm is triggered via A8 (Uu / RRC), gNB CU The CP immediately issues the baseline full measurement configuration, the terminal terminates the reduction mode and restores the traditional full measurement, avoiding the risk of missing neighboring cells and switching failure, and fully implementing the fault-tolerant logic of step 3 "reducing measurement when matching and rolling back when exceeding the limit".

[0176] The fast rollback mechanism immediately restores baseline measurements when predictions are inaccurate or environmental changes occur, ensuring the reliability of handover and coverage. Its triggering conditions (any one of which must be met) are: deviation score > threshold and persists for T_hold; key KPIs (e.g., HO missed trigger / failure rate) exceed the threshold; budget depletion or low battery; validityTime / Area failure or version mismatch.

[0177] When the UE switches to the baseline measurement configuration locally, it sends a fallback reason to the network; the network freezes the current PMT and reports the KPI, waiting for NWDAF / PCF to be updated.

[0178] Based on the above, as shown in the figure, the core network connection lines are explained as follows: A1|NWDAF → PCF 〔Nnwdaf〕—Portrait Output → Strategy Arrangement.

[0179] The process is triggered by: completion / refresh of new profile training; expiration of PCF subscription; and scheduled push notifications. Payload: behaviorProfileId, profileType, confidence, validityTime / Area, and (optional) predictedPath. Validation / Constraints: PCF needs to check whether the lower confidence limit and valid domain are consistent with the UDR; if the confidence is low, it can be downgraded to the baseline policy. Design Intent: To decouple "learning results" from "policy orchestration," allowing the policy layer to consume only necessary metadata and avoiding sending PIIs to the RAN.

[0180] A2|PCF → AMF 〔Npcf〕—Policy Deployment → Control Surface.

[0181] This process is triggered by: PCF generating / updating policies; policy expiration or slice changes. Payload: policyTag (including budget, object selection, valid domains, backoff threshold, and version reference). Validation / Constraints: AMF must archive this tag and associate it with the UE; when RAN capabilities do not support a certain field, PCF can issue a compatibility degradation. Design Intent: By placing the budget / backoff threshold at the beginning of the policy, the RAN only needs to generate the PMT based on this, reducing end-to-end coupling.

[0182] A3|AMF → gNB-CU-CP 〔N2 / NGAP〕—Contextualized image / strategy →CU-CP.

[0183] Triggering: UE registration, handover, context modification, or policy update. Payload: behaviorProfileId, policyTag, execution prompt (e.g., "PMT is triggered upon entering the valid domain"). Verification / Constraint: CU-CP needs to perform a secondary check on the version number / valid domain; if they do not match, it requests the latest tag from the AMF or reverts to the baseline. Design Intent: To unify the bearer at the N2 layer, avoiding the need for custom-built interfaces; to facilitate interoperability among multiple vendors.

[0184] A9|UDR / UDM NWDAF / PCF [Nudr] — Version / Valid Domain Governance (optional connection).

[0185] Triggers: Profile / template release, replacement, and revocation; changes in consent status; changes in regional policies. Payload: profileVersion, templateVersion, validityTime / Area, consentRef, minimizationFlags. Validation / Constraints: NWDAF / PCF must check version and valid domain before external release; expired or out-of-domain NWDAFs are invalid. Design Intent: To fix compliance and governance at the UDR level; policies / learning platforms should only reference, not copy.

[0186] Wireless access network: A4|gNB-CU-CP → UE 〔Uu / RRC〕—PMT issued (directional signal reduction).

[0187] Target: CU-CP → UE; Interface / Layer: Air interface Uu / RRC (RRCReconfiguration); Trigger: UE enters / leaves the valid domain; new version of policy / template; after handover / reconfiguration; or UE actively requests an update.

[0188] Payload Details (PMT): header: behaviorProfileId, policyTagRef, templateVersionRef, confidence, validityTime / Area; objectSelection: prioritizedFreqList, prioritizedNeighborList{NR-ARFCN, PCI, beamId}, skipList; measurementControl: reportConfig (events / periods and thresholds), gapPattern (T_gap, duty cycle), measurementBudget{maxReportsPerMin, maxMeasPerPeriod, energyCap}; SafetyGuard: Backoff thresholds (missed trigger rate / RSRP drift / positioning deviation, etc.) and corresponding actions; Constraints / Guardrails: Must not exceed 3GPP RRM baselines (e.g., 38.133); Emergency / safety incidents may be reported beyond authorized scope; Purpose / Innovation Mapping: Deliver the "what to measure / when to measure / how much to measure" decisions to UEs in one go, and implement innovation ① Profile-driven PMT (including budget and rollback soft constraints).

[0189] A5|UE → gNB-CU-CP 〔Uu / RRC〕—UAI (Budget / Variation Feedback).

[0190] Target: UE → CU-CP; Interface / Layer: Air Interface Uu / RRC (UE Assistance Information Extension); Trigger: New PMT confirmation; Budget pressure / observation bias during execution; Changes in user status / battery level.

[0191] Loading considerations: pmtAcceptance: ACCEPT / PARTIAL / REJECT + cause; measurementBudget: UE's tolerable upper limit / recommendation (reporting per minute, number of cycles, interval duty cycle, energy consumption); observedDeviation: topNHitRate, rsrpDriftScore, eventMissRate, locationDrift validityFeedback: Messages indicating external domain / expiration / version inconsistency; Constraints / Guardrails: Feedback does not change the baseline; the network side makes minor adjustments or rolls back based on the feedback. Purpose / Innovation Mapping: To form a closed loop of end-to-end network collaboration and quantitative measurement reduction, and to implement innovation ② Measurement Budget Negotiation (UAI).

[0192] A6|gNB-CU-CP → gNB-DU [F1AP]—Predictive-Meas-Config.

[0193] Object: CU-CP → DU; Interface / Layer: F1 / F1AP (Control Plane); Trigger: PMT Generation / Update; DU Load or Topology Change.

[0194] Payload key points: allowedScanSet (frequency / neighboring cell / beam), scanSuppression (suppression condition / period), taskSchedule (task sequence / priority), validity (time period / area); Constraints / Guardrails: Do not excessively suppress emergency / safety triggers; Purpose / Innovation Mapping: Decentralize template intent into executable tasks to reduce irrelevant scans; Support the network-side implementation of innovation ①; A7|gNB (CU-UP / CP or OAM) → NWDAF 〔Nnwdaf / O1〕—KPI backflow.

[0195] Object: gNB (CU-UP / CP or OAM) → NWDAF; Interface / Level: Nnwdaf (event / analysis input), or O1 (control channel); Trigger: Periodic reporting at the end of the policy window; or KPI exception trigger.

[0196] Loading key points: measReportsCount, rrcMeasSignalingLoad, hoTriggerRate / FailRate, RLF, UEpowerEst, QoSkeep (throughput / RTT), etc., and aggregated by {templateVersionRef,profileVersionRef, cell / TA, timeBucket}; Constraints / Guardrails: Aggregated and minimized by version / region, excluding PII; Purpose / Innovation Mapping: Feeding facts back to NWDAF for remote calibration, implementing innovation ⑥ Closed-loop update.

[0197] A8|UE gNB-CU-CP 〔Uu / RRC〕—Revert to reconfiguration (dashed line).

[0198] Target: UE CU-CP; Interface / Layer: Air interface Uu / RRC (reconfiguration); Trigger (any one satisfied): Deviation score > threshold and continues for T_hold; HO missed trigger / failure rate exceeds threshold; Budget depletion / low power; validityTime / Area invalid or version mismatch.

[0199] Loading points: rollback reason / threshold hit, baseline measurement configuration and holding time; constraints / guardrails: RRM baseline is met immediately after rollback, and then grayscale recovery is performed later; purpose / innovation mapping: quick fallback, ensuring experience and switching reliability, and implementing innovation ④ quick rollback.

[0200] Operation sequence description of core network elements (A1→A2→A3→A9).

[0201] Spatiotemporal behavior profile generation (NWDAF): Training / inference yields behaviorProfileId and metadata such as confidence and validity, which are then written into the UDR to generate profileVersion.

[0202] Policy Generation (PCF): PCF reads profile and governance information, forms a policy tag (including budget, object selection, rollback threshold, and version reference), and archives it to the UDR's templateVersion.

[0203] Policy delivery (PCF→AMF): The policyTag is delivered to the AMF via Npcf.

[0204] Context Carrying (AMF→CU-CP): When the UE context is established / modified, the behaviorProfileId and policyTag are sent to gNB-CU-CP via N2 / NGAP.

[0205] Version / Valid Domain Verification (UDR): CU-CP (or AMF / PCF) queries Nudr again if necessary to confirm the version / valid domain and prevent the use of expiration policies.

[0206] After that, the RAN / UE phase begins: CU-CP generates PMT and sends it to UE; UE responds with UAI; DU is executed; KPI is fed back to NWDAF for closed-loop operation.

[0207] Decoupling: NWDAF only produces "profile metadata," PCF is responsible for "policy orchestration," and AMF is responsible for "contextualization." This clear layering facilitates the replacement / upgrading of any component. Governance Priority: Before any template / profile takes effect, it is bound to the UDR version / valid domain / consent status to ensure minimal identification and compliance. Degradability: When confidence is low, outside the domain, or version mismatch occurs, PCF / AMF / CU-CP can trigger baseline policies to ensure reliability. Scalability: Group profile policies are prioritized for distribution, with fine-tuning of individual coverage in PCF to reduce policy explosion. End-to-End Closed Loop: The core network holds both ends of the "expectation" (budget / object / threshold) and "fact" (feedback KPIs), allowing NWDAF to continuously optimize and ensure long-term sustainable benefits.

[0208] In this embodiment, following the core network's A1→A2→A3 (profile→policy→context), after entering the RAN / UE segment, the runtime sequence of the radio access network elements is described as follows: A4: PMT Deployment (CU-CP→UE): When the valid domain and confidence level meet the requirements, CU-CP compiles the profile and policy into a PMT deployment, carrying the object / budget / gap / rollback threshold / version and valid domain. Purpose: To initiate targeted test reduction execution.

[0209] UE performs PMT (local): The UE performs measurements / reports by object and budget, while calculating budget utilization and deviation metrics. Purpose: To reduce irrelevant scans and reporting.

[0210] A5: UAI Feedback (UE→CU-CP): UE reports acceptance / budget / deviation / effectiveness; Purpose: To allow the network to perceive the UE's true tolerable intensity and on-site deviations, for fine-tuning or rollback.

[0211] Near-loop parameter tuning / or rollback (CU-CP, Local Action): CU-CP, based on UAI and RRM guardrails, makes lightweight incremental adjustments to PMT (such as tightening Top-K, raising the threshold, and reducing the duty cycle), triggering A8 rollback when necessary. Simultaneously, the changes are mapped to the execution plan sent to the DU via A6. Purpose: To quickly stabilize UE and network-side behavior without interrupting services.

[0212] A6: DU Execution Configuration (CU-CP→DU): DU executes according to allowedScanSet / scanSuppression / taskSchedule to reduce meaningless scans. Purpose: To reduce testing consistently on both the network and terminal sides.

[0213] KPI Aggregation (CU-UP / CP / OAM, Local): Aggregates measurement / signaling overhead, HO success rate, RLF, QoS retention, power consumption proxy, etc., according to template / profile version. Purpose: To prepare "fact data" for remote loop calibration.

[0214] A7: KPI Feedback (gNB→NWDAF): Periodically or event-driven feedback of KPIs / KQIs to NWDAF. Purpose: NWDAF updates profile confidence / valid domain, and PCF may reprogram the strategy (entering a new round of A1→A2→A3).

[0215] A8: Quick rollback (e.g., triggered): If the deviation / failure rate exceeds the threshold or the domain / version mismatch occurs, the UE and the network side reconfigure via RRC to return to the baseline measurement and report the reason; wait for the new template / new policy to take effect before gradually resuming the gray-scale rollout. Purpose: Prioritize user experience and ensure reliable handover and coverage.

[0216] This application constructs a spatiotemporal behavioral profile based on user historical dwell sequences, location trajectories, and service data. It then uses a PCC (Pressure, Control, and Compatibility) strategy that adaptively orchestrates and matches the user's real spatiotemporal behavioral characteristics to generate and distribute personalized measurement reduction strategy templates. Compared to traditional network-wide unified baseline measurement methods, this application achieves refined and hierarchical wireless measurement control based on users' differentiated mobility patterns and behavioral stability. On one hand, by accurately selecting priority and skippable measurement targets and adaptively adjusting measurement gaps and measurement resource budgets, it significantly reduces invalid RF scans and redundant measurement reporting from terminals, effectively lowering terminal measurement power consumption and air interface signaling overhead. On the other hand, this application leverages profile confidence and spatiotemporal effective domains to achieve dynamic adaptation and risk control of measurement strategies, avoiding insufficient measurement and missed handover triggers caused by fixed reduction strategies. While ensuring user mobility reliability and wireless access performance, it maximizes the reduction of terminal measurement load and network scheduling pressure, achieving a dynamic optimal balance between wireless access measurement accuracy, terminal power consumption, and network signaling overhead.

[0217] In this embodiment, the terminal receives a predictive measurement template from the network side and performs directional wireless access reduction measurement based on the reduction measurement strategy within the template. By filtering, measurement objects can be skipped, measurement resource overhead can be constrained, unnecessary RF scanning can be reduced, terminal measurement power consumption can be lowered, and the number of air interface measurement signaling reports can be reduced. The terminal compares the measured behavior data with the profile-predicted behavior data to calculate the behavior deviation, and reports the deviation to the network side when the reporting conditions are met. The network side then makes a unified decision on whether to trigger a fast fallback mechanism. This achieves lightweight measurement in scenarios with stable user behavior, and can also trigger full baseline measurement in a timely manner when the user's actual behavior deviates from the profile prediction, avoiding mobility risks such as missed neighbor cell measurements and handover failures caused by excessive reduction, thus balancing terminal energy saving and power consumption reduction with wireless access reliability.

[0218] Optionally, in another embodiment, based on the above embodiments, the method is also applied to low-altitude security scenarios; the user or user group is a low-altitude flight device equipped with a cellular communication module, including: logistics drones, inspection drones, police drones, and electric vertical take-off and landing (eVTOL) aircraft, etc., and is not limited to these in actual application; the spatiotemporal behavior profile is used to characterize the flight path trajectory, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of the low-altitude flight device; the PCC strategy and predictive measurement template are adapted to the authorized flight time periods, permitted altitude zones, restricted flight areas, and security levels of airport airspace, energy parks, ports, and urban low-altitude routes to achieve differentiated and targeted reduction of wireless measurement and control in low-altitude scenarios.

[0219] Optionally, in this implementation, for low-altitude security scenarios, the UDR / UDM manages the spatiotemporal behavior profile and corresponding PCC strategy in a versioned manner. When airspace authorization rules, restricted flight zones, or flight mission windows change, the NWDAF generates a new version of the spatiotemporal behavior profile, and the PCF generates a new version of the PCC strategy, invalidating the old version, thus achieving auditability, revocation, and rollback of the security strategy. The gNB-CU-CP compares the cell / beam sequence, position, altitude, and speed obtained from flight measurements with the predicted path of the spatiotemporal behavior profile, calculates the behavior deviation with weights, and identifies route deviation, intrusion into restricted flight zones, abnormal hovering, etc. Signal mutations and other abnormal low-altitude behaviors; when the behavior deviation exceeds the preset threshold or a high-level alarm is received from the low-altitude security platform via AF, gNB-CU-CP and the aircraft terminal UE synchronously exit the reduced measurement mode and restore the baseline full measurement configuration; gNB-CU-UP, gNB-CU-CP or OAM aggregation network run KPI data and feed it back to NWDAF, forming a three-level closed-loop calibration mechanism of terminal local fast loop, RAN near loop, and core network far loop. NWDAF iteratively optimizes the spatiotemporal behavior profile and PCF iteratively optimizes the PCC strategy to achieve adaptive optimization of low-altitude flight measurement performance and security monitoring accuracy.

[0220] For ease of understanding, please refer to the following: Figure 3 This example demonstrates a basic low-altitude security application scenario. The scenario focuses on key areas such as airport airspace, energy parks, ports, and urban low-altitude air routes, with monitored targets including logistics drones, inspection drones, police drones, and electric vertical takeoff and landing (eVTOL) aircraft equipped with cellular communication modules. The low-altitude security platform can... Figure 3 The AF in the system provides scenario-based information in the form of non-personally identifiable information to the NWDAF and PCF, including flight mission type, authorized route, mission time period, permitted altitude zone, restricted / no-fly zone, security level, and emergency indicator. This type of information is only used for the effective domain of the modulation profile and measurement strategy, and does not directly replace external low-altitude detection equipment such as radar and electro-optical systems. Specifically, it includes: 1) Generating spatiotemporal behavior profiles for low-altitude flight provides a benchmark behavior template for subsequent strategy orchestration.

[0221] The low-altitude flight spatiotemporal behavior profile in this step can also be called a behavior profile or profile, etc. The AF (Airborne Detection and Analysis) of the low-altitude security platform first pushes low-altitude scenario alerts without personally identifiable information to the NWDAF (Non-Wide Air Detection and Analysis) and PCF (Public Security Detection and Analysis). These alerts specifically include airspace control elements such as flight mission type, authorized flight route, mission time period, permitted altitude zone, restricted / no-fly zones, security level, and emergency event identifiers, used to constrain the effective scope and measurement strategy boundaries of the network profile. Based on this, the NWDAF generates low-altitude spatiotemporal behavior profiles for individual aircraft or similar mission groups based on de-identified historical serving cell sequences, beam switching sequences, flight trajectories, speeds, altitude ranges, dwell times, RSRP / RSRQ / SINR distributions, and mission time periods. The low-altitude spatiotemporal behavior profile includes at least a profile identifier (behaviorProfileId), profile type (profileType), confidence level, validity time (validityTime), validity area (validityArea), predicted flight path (predictedPath), and predicted cell, frequency point, and beam set (beam candidate set). UDR / UDM saves the profile version, template version, authorization status, and spatiotemporal valid domain. When the route approval, restricted flight area, or mission window changes, a new version is generated and the old version is invalidated to ensure that the security policy is auditable, revocable, and rollbackable, and to ensure the legality and traceability of subsequent policy iterations. The generated low-altitude spatiotemporal behavior profile is output to PCF for policy orchestration.

[0222] 2) Based on low-altitude spatiotemporal behavior profiling and dynamic arrangement of security levels, differentiated security measurement strategies are developed, and executable control strategies are generated from the profiling results. This is the low-altitude security strategy arrangement.

[0223] In this step, the PCF reads the low-altitude spatiotemporal behavior profile generated by the NWDAF and the security level provided by the AF to form a policy tag bound to the flight mission. The policy tag includes priority measurement of along-route cells, frequencies, and beam sets, suppressable non-associative scanning objects, measurement budget, gapDutyCyclePct, validityTime / Area, and fallbackThresholds. For ordinary authorized routes, reduced measurement can be used to lower the power consumption of airborne communication terminals; for key protection areas, temporary control areas, or high-risk missions, the priority of measurement and reporting is increased, and the anomaly confirmation time is shortened.

[0224] 3) Policy delivery and predictive measurement template generation enable the core network policy to be implemented and converted into the access network and terminals.

[0225] In this step, based on the policyTag generated by the PCF, the entire network policy is distributed and the template is compiled. Specifically, the PCF sends the policyTag to the AMF via the A2 interface. When the UE registers, establishes a context, performs a handover, or updates the policy, the AMF sends the behaviorProfileId and policyTag to the gNB-CU-CP via the N2 / NGAP interface (A3). The gNB-CU-CP combines local neighboring cells, frequency points, beam libraries, and UE capabilities to compile the profile and policy into a predictive measurement template (PMT), which is then distributed to the UE via the Uu / RRC interface (A4). Simultaneously, the allowedScanSet, scanSuppression, and taskSchedule, consistent with the PMT, are distributed to the gNB-DU via the F1AP interface (A6), ensuring that the terminal and network sides execute the same low-altitude route measurement intent.

[0226] 4) End-to-end collaborative directional measurement (i.e. directional reduction measurement) performs refined data collection and feedback on measurement status based on a unified template, providing real-time data for anomaly identification.

[0227] In this step, the UE Measurement Agent in the aircraft UE (Aircraft Terminal) prioritizes measuring the cells, frequencies, and beams expected to be traversed by the authorized route according to the PMT, and reduces periodic scans and measurement reports unrelated to the current route within the budget. The aircraft UE reports pmtAcceptance, acceptable measurement budget, Top-N candidate hit rate, signal distribution drift, location or route deviation, and version validity through the UAI extension shown in A5. gNB-CU-CP makes incremental adjustments based on the feedback, while gNB-DU performs the corresponding cell and beam scanning tasks.

[0228] 5) Based on the comparison between measured data and benchmark profiles, quantitative identification of low-altitude flight anomalies is achieved, and deviation behavior is determined based on the preceding benchmark profile. This is known as low-altitude anomaly identification.

[0229] In this step, the network continuously gathers real-time measured data such as the aircraft's cell handover sequence, beam dwell sequence, actual position, flight altitude, and flight speed, and compares this data in real time with the predicted flight path and effective airspace range in the baseline profile generated in step 1). When situations such as flight path deviation from the authorized trajectory, intrusion into restricted or prohibited flight areas, abnormal hovering or abnormal acceleration / deceleration, a continuous decrease in the beam hit rate of the Top-N serving cells, abrupt changes in signal distribution, or mismatch in the profile template version are detected, a quantified behavioral deviation is calculated through weighted calculation of multi-dimensional indicators. Combined with the dynamic adaptation and discrimination threshold of airspace security level configured in step 2), lower fallback thresholds and shorter abnormal holding times (i.e., shorter holding times) are used in key protection areas to improve the sensitivity of anomaly identification in high-risk airspace, accurately distinguish between compliant flight fluctuations and illegal abnormal behaviors, output standardized anomaly discrimination results, and trigger subsequent handling procedures.

[0230] 6) Rapid policy rollback and cross-system collaborative handling after anomaly triggering, taking over the security fallback control based on anomaly identification results. This includes anomaly handling and rapid rollback.

[0231] In this step, when the behavioral deviation exceeds the preset fallback threshold and continues to maintain the abnormal duration (T_hold), or when the external low-altitude security platform sends a high-level alarm via AF, the gNB-CU-CP and the aircraft UE immediately stop the reduced measurement and restore the baseline full measurement configuration via A8. Simultaneously, the measurement reporting frequency can be increased, neighboring cells, frequency points, and beam sets expanded, and the fallback reason reported. The network side can also notify the low-altitude security platform of the minimum necessary information such as profile ID, template version, deviation type, time, and area, allowing it to coordinate with flight control, alarm verification, or handling processes. The communication-side fallback and external security handling are decoupled, ensuring that RRM reliability is still guaranteed even if external handling is not completed.

[0232] 7) Three-level closed-loop calibration (end-near-far) iteratively updates profiles and strategies to achieve end-to-end adaptive continuous optimization. This is known as closed-loop calibration.

[0233] In this step, gNB-CU-UP, gNB-CU-CP, or OAM aggregates measurement report volume, RRC signaling volume, handover success rate, RLF, latency, throughput, aircraft UE power consumption estimation, abnormal hits and false alarms, and other KPIs according to profile and template version, and then feeds them back to NWDAF via A7. NWDAF adjusts the profile confidence, spatiotemporal effective domain range, and predicted flight path (predicted flight trajectory) of the behavioral profile based on real operational data. PCF generates a new policy version, thus forming a low-altitude security closed loop combining the aircraft UE local fast loop, RAN near loop, and core network far loop. This achieves dynamic adaptive optimization of low-altitude security measurement strategies and behavioral profiles according to airspace environment and flight behavior, completing the closed-loop iteration of the entire low-altitude security control process.

[0234] For example, an energy park configures a flight path R1 for its inspection drones, with mission hours from 09:00 to 11:00, an allowed altitude range of 80 to 120 meters, and a predicted cell set of {Cell-1, Cell-2, Cell-3}, with a corresponding priority beam set configured for each cell. The PMT can, for example, set maxReportsPerMin to 6 and the measurement gap duty cycle to 10%. A fast backoff is triggered when the Top-N candidate hit rate is below 0.6 for three consecutive periods, the lateral deviation of the flight path exceeds 100 meters, the flight altitude exceeds the authorized altitude range, or the drone enters a restricted GeoHash area. The above values ​​are implementation parameters for illustrative purposes only and can be dynamically configured according to area level, aircraft capabilities, and operational requirements.

[0235] In this embodiment, the application can also be extended to non-cooperative targets. Specifically, for non-cooperative flight targets that have not installed cellular communication modules or completed network registration, this embodiment does not directly perform UE-side PMT on them. Instead, external systems such as radar, photoelectric, and radio detection generate target events, which are then sent to the network via the low-altitude security platform and AF to provide threat areas, time windows, and security level alerts. The PCF can then temporarily adjust the measurement templates of connected inspection terminals, ground security terminals, or collaborative drones within the relevant cells, and trigger the gNB-DU to enhance communication measurements and reporting in designated areas to assist in event verification and network protection.

[0236] In this embodiment, when the authorized aircraft is running stably along the predetermined route, this embodiment can reduce irrelevant frequency points, neighboring cells and beam scanning, and reduce air interface measurement signaling and airborne terminal power consumption; when the route deviates, restricted flight areas are entered or the wireless environment changes suddenly, the baseline measurement can be quickly restored and auditable abnormal information can be output.

[0237] Therefore, in the embodiments of this application, as... Figure 3 The application architecture shown is not only suitable for regular commuting or campus mobility scenarios, but can also be used for low-altitude communication support, flight compliance monitoring, and security linkage in key areas.

[0238] In this embodiment, starting with scene prompt input, the process sequentially completes profile construction, strategy orchestration, template compilation and distribution, end-to-end network collaborative measurement, and anomaly identification and handling. Finally, the strategy is updated in a closed loop through KPI feedback. Each step is interdependent, data flows step by step, and strategies are optimized generation by generation, realizing an end-to-end network collaborative low-altitude security governance system for low-altitude aircraft under 5G network, which includes hierarchical control, reduced measurement, anomaly identification, and adaptive iteration.

[0239] In this embodiment, taking a low-altitude security scenario as an example, a spatiotemporal behavior profile of the aircraft is constructed using historical loitering sequences, flight trajectories, and communication service data. Based on the spatiotemporal characteristics of this profile, a differentiated PCC measurement strategy is programmed. Furthermore, a predictive measurement template equipped with a reduced measurement mechanism is generated and finally distributed to the terminal for targeted reduced-capacity wireless access measurement. This reduces terminal power consumption and network signaling load while ensuring measurement reliability. Combined with real-time feedback from the terminal, the measurement strategy is dynamically fine-tuned, improving adaptability to complex low-altitude scenarios. This effectively reduces the probability of false alarms and missed alarms, achieving multiple technical benefits in low-altitude scenarios, including energy saving and load reduction in wireless measurement, high-precision monitoring, intelligent security management and control, and system self-optimization iteration.

[0240] This application accurately depicts the movement patterns of low-altitude aircraft by constructing a user spatiotemporal behavior profile. Based on the profile features, it adaptively generates differentiated wireless measurement strategies and solidifies the strategy logic into predictive measurement templates that match user movement characteristics, uniformly distributing them to terminals and base stations. This enables terminals to perform targeted, reduced-volume, and precise intelligent wireless measurements. While fully ensuring aircraft mobility and wireless network measurement reliability, this application effectively avoids a large amount of invalid radio frequency scanning behavior in the traditional full-scan mode, significantly reduces redundant measurement signaling overhead at the terminal, and significantly reduces the power consumption of the airborne terminal and the overall wireless load, while maintaining stable core measurement accuracy. Based on this, this application combines the airspace management needs of low-altitude security scenarios, relies on the collaboration of 5G core network and access network elements, utilizes low-altitude scenario constraint information to standardize the effective domain of profiles and the boundaries of measurement strategies, and achieves auditable, revocable, and rollback security strategies through versioned profile management, meeting the compliance management requirements of key areas such as airports and energy parks; it achieves differentiated management capabilities for reducing energy consumption on regular air routes and strengthening monitoring of high-risk airspaces through hierarchical strategy adaptation, and ensures a high degree of consistency between network-side and terminal-side measurement intentions through a unified measurement template for the terminal network, and dynamically fine-tunes measurement strategies based on real-time feedback from the terminal, improving the adaptability to complex low-altitude scenarios. Meanwhile, this application achieves quantitative identification of abnormal flight behavior through weighted comparison of multi-dimensional flight and communication indicators. In abnormal scenarios, it can quickly revert to baseline full measurement. Communication measurement control and external low-altitude security handling processes are decoupled to ensure the continuous reliability of wireless network measurement. Finally, through a three-level closed-loop calibration mechanism of terminal local fast loop, RAN near loop, and core network far loop, the behavior profile and measurement strategy are continuously iterated and optimized based on real network operation KPIs. It adapts to the dynamic changes of low-altitude airspace environment, flight trajectory and wireless channel, effectively reducing the probability of false alarms and missed alarms. It achieves multiple technical benefits in low-altitude scenarios, including energy saving and burden reduction of wireless measurement, high-precision monitoring, intelligent security control and management, and system self-optimization iteration.

[0241] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to this application.

[0242] Figure 4 This is a block diagram of a measurement and control device based on user behavior profiles provided in an embodiment of this application. The device includes: an acquisition module 401, a generation module 402, and a first sending module 403, wherein... The acquisition module 401 is used to acquire the spatiotemporal behavior profile of a user or user group and the corresponding policies and billing control (PCC) policies. The spatiotemporal behavior profile is constructed based on the user's or user group's historical cell dwell sequence, location trajectory and service data. The PCC policy is generated based on the spatiotemporal behavior features of the spatiotemporal behavior profile. The generation module 402 is used to generate a prediction measurement template based on the spatiotemporal behavior profile and the corresponding PCC strategy. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile. The first sending module 403 is used to send a predictive measurement template configured with the reduction measurement strategy to the terminal, so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

[0243] Optionally, the generation module includes: The parsing module is used to parse the spatiotemporal behavior profile and the corresponding PCC strategy to obtain the prediction path, spatiotemporal behavior features and profile confidence. The partitioning module is used to partition the priority measurement object set and the skippable measurement object set based on the switching probability of each resource in the predicted path. The configuration module is used to configure measurement gap parameters based on the spatiotemporal behavioral characteristics, and to set the upper limit of the measurement resource budget according to the confidence level of the profile. The strategy generation module is used to integrate the priority measurement object set, the skippable measurement object set, the measurement gap parameter, and the measurement resource budget limit to generate a reduction measurement strategy adapted to the spatiotemporal behavior profile. The encapsulation module is used to encapsulate the reduction measurement strategy to obtain the prediction measurement template of the spatiotemporal behavior profile.

[0244] Optionally, the device further includes: The first receiving module is used to receive the behavior deviation reported by the terminal, wherein the behavior deviation is the deviation value between the measured behavior data obtained by the terminal performing reduction measurement based on the prediction measurement template and the predicted behavior data of the spatiotemporal behavior profile. The threshold acquisition module is used to acquire the preset fallback threshold configured based on the profile confidence in the PCC strategy. The judgment module is used to determine whether the value of the behavior deviation exceeds a preset backoff threshold; The second sending module is used to send a baseline full measurement configuration to the terminal when the judgment module determines that the value of the behavior deviation exceeds a preset backoff threshold, triggering a fast backoff mechanism so that the terminal terminates the reduced measurement and switches to baseline full measurement.

[0245] Optionally, the device further includes: a second receiving module and a dynamic adjustment module; and / or, an indicator acquisition module and a feedback module, wherein, The second receiving module is used to receive the measurement acceptance rate and budget consumption data reported by the terminal; The dynamic adjustment module is used to dynamically adjust the upper limit of the measurement resource budget within the predicted measurement template based on the measurement acceptance and budget consumption data. The indicator acquisition module is used to periodically acquire various key performance indicators of the terminal after the measurement is reduced according to the prediction measurement template. The backhaul module is used to backhaul the various key performance indicators to the core network processing node so that the core network processing node can iteratively update the spatiotemporal behavior profile and the corresponding PCC strategy.

[0246] Optionally, the device further includes: a pre-construction module, used to pre-construct spatiotemporal behavioral profiles of users or user groups based on the user's or user group's historical cell dwell sequence, location trajectory, and business data.

[0247] Optionally, the pre-built module includes: The preprocessing module is used to perform de-identification preprocessing on the historical cell dwell sequences, location trajectories, and business data of the acquired users or user groups; The profile building module is used to build group prototype profiles and individual incremental profiles based on the preprocessing results and user behavior similarity clustering. The learning module is used to learn the individual increment based on the group prototype profile, and to superimpose and fuse the basic features of the group prototype profile with the features of the individual increment to obtain a personalized spatiotemporal behavior profile. The encapsulation and transmission module is used to uniformly encapsulate personalized behavioral profiles carrying profile identifiers, profile types, confidence levels, spatiotemporal effective domains, prediction paths, and profile version information to obtain standardized spatiotemporal behavioral profiles; and to send the profile version information and spatiotemporal effective domain of the spatiotemporal behavioral profiles to the unified data repository UDR to realize the storage, management, and backtracking of version profiles. The configuration module is used to configure robust profile switching strategies for user cold start scenarios, including: prioritizing the reuse of group profiles in user cold start scenarios, or automatically downgrading to the use of group profiles or switching to baseline measurement mode when the profile confidence is insufficient or the user exceeds the spatiotemporal validity domain of the profile.

[0248] Optionally, the device further includes: a pre-generation module, used to pre-generate PCC strategies based on the spatiotemporal behavioral features of the spatiotemporal behavioral profile through the policy control function PCF.

[0249] Optionally, the pre-generation module includes: The parsing module is used to parse the received spatiotemporal behavior profile to obtain the profile confidence, spatiotemporal effective domain, prediction path, and spatiotemporal behavior features. The level determination module is used to determine the constraint level of the measurement strategy based on the confidence level of the profile; The object partitioning module is used to partition the target measurement object and the skippable object according to the predicted path and constraint level, and set the spatiotemporal effective domain as the measurement strategy effective condition; The parameter configuration module is used to configure measurement gap parameters based on the spatiotemporal behavioral characteristics and set the upper limit of the measurement resource budget in conjunction with the profile confidence. The threshold configuration module is used to configure the boundary conditions for the policy to take effect and the fallback threshold; The strategy encapsulation module is used to encapsulate the target measurement object, skippable object, measurement gap parameter, measurement resource budget limit, backoff threshold, and spatiotemporal effective domain to generate the corresponding PCC strategy.

[0250] Optionally, the device is also applied to low-altitude security scenarios. The acquisition module is further used to acquire spatiotemporal behavior profiles of users or user groups and corresponding strategies and billing control (PCC) strategies for low-altitude flight equipment equipped with cellular communication modules. The spatiotemporal behavior profiles are used to depict the flight path trajectory, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of low-altitude flight equipment. The PCC strategies and predictive measurement templates are adapted to authorized flight periods, permitted altitude zones, restricted flight areas, and security levels of low-altitude routes in airport airspace, energy parks, ports, and cities to achieve differentiated and targeted reduction of wireless measurement and control in low-altitude scenarios.

[0251] Please also see Figure 5 This is another block diagram of a measurement and control device based on user behavior profiles provided in an embodiment of this application. The device includes: a receiving module 501, a measuring module 502, a determining module 503, and a sending module 504, wherein... The receiving module 501 is used to receive the prediction measurement template sent by the network side. The prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group. Measurement module 502 is used to perform directional wireless access reduction measurement according to the reduction measurement strategy; The determination module 503 is used to determine the degree of behavioral deviation between the measured behavioral data and the predicted behavioral data of the spatiotemporal behavioral profile; The sending module 504 is used to report to the network side when the behavior deviation meets the reporting conditions, so that the network side can determine whether to trigger the fast backoff mechanism of the full baseline measurement.

[0252] Optionally, the measurement module includes: The parsing module is used to parse the reduction measurement strategy in the predicted measurement template to obtain the target measurement object, the measurement object that can be skipped, the measurement gap parameter and the upper limit of the measurement resource budget; The measurement module is activated to enable reduction measurement when the current location and time period match the spatiotemporal effective domain of the spatiotemporal behavior profile. The wireless measurement module is used to filter the skippable measurement objects, perform periodic wireless measurements on the target measurement objects according to the measurement gap parameters, monitor the number of radio frequency scans, and control the power consumption within the upper limit of the measurement resource budget.

[0253] Optionally, embodiments of this application also provide an electronic device, including: It includes a processor, a memory; and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0254] Optionally, embodiments of this application also provide a readable storage medium storing a program or instructions, which, when executed by a processor of an electronic device, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0255] Optionally, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor of an electronic device, implement the steps of the measurement and control method based on user behavior profiles as described above.

[0256] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0257] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0258] Figure 6 This application provides a frame of an electronic device 600. Figure 6 As shown in the figure, it includes a processor 601, a communication interface 602, a memory 603, and a communication bus 604. The processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604. Memory 603 is used to store the processor-executable instructions; The processor 601, when executing executable instructions on the memory 603, implements the method described above.

[0259] In this embodiment, the communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not mean that there is only one bus or one type of bus.

[0260] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0261] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0262] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0263] In another embodiment provided in this application, a readable storage medium is also provided, on which a program or instruction is stored. When the program or instruction is executed by a processor of an electronic device, the processor is able to perform the various processes of the measurement and control method embodiment based on user behavior profiles as described above, and achieve the same technical effect. To avoid repetition, it will not be described again here. For example, the readable storage medium includes computer-readable storage media, such as ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices.

[0264] In another embodiment provided in this application, a computer program product is also provided, including a computer program or instructions. When the computer program or instructions are executed by the processor of an electronic device, they implement the various processes of the measurement and control method embodiment based on user behavior profiles as described above, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0265] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0266] Figure 7 This is a block diagram of a device 700 for measurement and control based on user behavior profiles, provided in an embodiment of this application. For example, device 700 can be provided as a server. See also... Figure 7 The apparatus 700 includes a processing component 722, which further includes one or more processors, and memory resources represented by memory 732 for storing instructions, such as application programs, that can be executed by the processing component 722. The application programs stored in memory 732 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 722 is configured to execute instructions to perform the methods described above.

[0267] Device 700 may also include a power supply component 726 configured to perform power management of device 700, a wired or wireless network interface 750 configured to connect device 700 to a network, and an input / output (I / O) interface 758. Device 700 can operate on an operating system stored in memory 732, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0268] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0269] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A measurement and control method based on user behavior profiles, characterized in that, The method includes: The system acquires spatiotemporal behavioral profiles of users or user groups and corresponding policies and billing control (PCC) policies. The spatiotemporal behavioral profiles are constructed based on the historical cell dwell sequences, location trajectories, and service data of users or user groups. The PCC policies are generated based on the spatiotemporal behavioral features of the spatiotemporal behavioral profiles. Based on the spatiotemporal behavior profile and the corresponding PCC strategy, a prediction measurement template is generated. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile. The predictive measurement template configured with the reduction measurement strategy is sent to the terminal so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

2. The measurement and control method based on user behavior profiles according to claim 1, characterized in that, The prediction measurement template is generated based on the spatiotemporal behavior profile and the corresponding PCC strategy. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile, including: The spatiotemporal behavior profile and the corresponding PCC strategy are analyzed to obtain the prediction path, spatiotemporal behavior features and profile confidence. Based on the switching probability of each resource in the predicted path, a set of priority measurement objects and a set of skippable measurement objects are divided. Based on the spatiotemporal behavioral characteristics, the measurement gap parameters are configured, and the upper limit of the measurement resource budget is set according to the confidence level of the profile. By integrating the priority measurement object set, the skippable measurement object set, the measurement gap parameter, and the measurement resource budget limit, a reduction measurement strategy adapted to the spatiotemporal behavior profile is generated. The reduction measurement strategy is encapsulated to obtain the prediction measurement template of the spatiotemporal behavior profile.

3. The measurement and control method based on user behavior profiles according to claim 1, characterized in that, The method further includes: The measured behavior data obtained by the terminal after reducing the measurement according to the predicted measurement template is received, and the behavior deviation is compared with the predicted behavior data of the spatiotemporal behavior profile. When the value of the behavior deviation exceeds the preset rollback threshold, a baseline full measurement configuration is sent to the terminal to activate the fast rollback mechanism. The system receives the behavior deviation reported by the terminal, whereby the behavior deviation is the deviation value between the measured behavior data obtained by the terminal through reduction measurement based on the prediction measurement template and the predicted behavior data of the spatiotemporal behavior profile. Obtain the preset fallback threshold configured based on profile confidence in the PCC strategy; When the deviation of the behavior exceeds the preset backoff threshold, the baseline full measurement configuration is sent to the terminal to trigger the fast backoff mechanism so that the terminal terminates the reduced measurement and switches to the baseline full measurement.

4. The measurement and control method based on user behavior profiles according to claim 1, characterized in that, The method further includes: Receive measurement acceptance and budget consumption data reported by the terminal; dynamically adjust the upper limit of the measurement resource budget within the predicted measurement template based on the measurement acceptance and budget consumption data; and / or The system periodically acquires various key performance indicators of the terminal after reducing the measurement according to the predicted measurement template; and transmits these key performance indicators back to the core network processing node so that the core network processing node can iteratively update the spatiotemporal behavior profile and the corresponding PCC strategy.

5. The measurement and control method based on user behavior profiles according to any one of claims 1 to 4, characterized in that, The spatiotemporal behavioral profile is pre-constructed using the Network Data Analysis Function (NWDAF) based on the user's or user group's historical cell dwell sequence, location trajectory, and service data in the following manner: De-identification preprocessing is performed on the historical cell dwell sequence, location trajectory and business data of the acquired user or user group; Based on the preprocessing results, group prototype profiles are constructed by clustering users according to the similarity of their behaviors. Based on the group prototype profile, individual increments are learned, and the basic features of the group prototype profile are superimposed and fused with the individual increment features to obtain a personalized spatiotemporal behavioral profile. A standardized spatiotemporal behavioral profile is obtained by uniformly encapsulating personalized behavioral profiles that carry profile identifiers, confidence levels, spatiotemporal effective domains, prediction paths, and profile version information. The version information and spatiotemporal effective domain of the portrait are sent to the unified data repository UDR to realize the storage, management and backtracking of the version portrait; Configure robust profile switching strategies for user cold start scenarios, including: prioritizing the reuse of group profiles in user cold start scenarios, or automatically downgrading to use group profiles or switching to baseline measurement mode when profile confidence is insufficient or users exceed the spatiotemporal validity domain of profiles.

6. The measurement and control method based on user behavior profiles according to any one of claims 1 to 4, characterized in that, The PCC strategy is pre-generated by the policy control function PCF according to the spatiotemporal behavioral features of the spatiotemporal behavioral profile in the following manner: The received spatiotemporal behavior profile is parsed to obtain profile confidence, spatiotemporal effective domain, predicted path, and spatiotemporal behavior features; The constraint level of the measurement strategy is determined based on the confidence level of the profile; Based on the predicted path and constraint level, target measurement objects and skippable objects are divided, and the spatiotemporal effective domain is set as the effective condition for the measurement strategy. Based on the spatiotemporal behavioral characteristics, the measurement gap parameters are configured, and the upper limit of the measurement resource budget is set in combination with the profile confidence. Configure the boundary conditions and fallback thresholds for the policy to take effect; The target measurement object, skippable object, measurement gap parameter, measurement resource budget limit, backoff threshold, and spatiotemporal effective domain are encapsulated to generate the corresponding PCC strategy.

7. The measurement and control method based on user behavior profiles according to claim 1, characterized in that, The user or user group is a low-altitude flight device equipped with a cellular communication module; The spatiotemporal behavioral profile is used to depict the flight path, flight speed, flight altitude, airspace dwell range, and cell and beam switching behavior patterns of low-altitude flight equipment. The PCC strategy and predictive measurement template are adapted to authorized flight times, permitted altitude zones, restricted flight areas, and security levels of airport airspace, energy parks, ports, and urban low-altitude routes, so as to achieve differentiated and targeted reduction of wireless measurement control in low-altitude scenarios.

8. A measurement and control method based on user behavior profiles, characterized in that, The method includes: Receive a prediction measurement template sent by the network side, the prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group; Perform directional wireless access reduction measurement according to the reduction measurement strategy; Determine the degree of behavioral deviation between the measured behavioral data and the predicted behavioral data from the spatiotemporal behavioral profile; When the behavior deviation meets the reporting conditions, it is reported to the network side so that the network side can determine whether to trigger the fast rollback mechanism of the full baseline measurement.

9. The measurement and control method based on user behavior profiles according to claim 8, characterized in that, The step of performing targeted wireless access reduction measurement according to the reduction measurement strategy includes: The reduction measurement strategy in the predicted measurement template is analyzed to obtain the target measurement object, the measurement object that can be skipped, the measurement gap parameter, and the upper limit of the measurement resource budget. When the current location and time period match the spatiotemporal effective domain of the spatiotemporal behavior profile, a reduction measurement is enabled. The system filters out the skippable measurement objects, performs periodic wireless measurements on the target measurement objects according to the measurement gap parameters, monitors the number of radio frequency scans, and controls energy consumption within the upper limit of the measurement resource budget.

10. A measurement and control device based on user behavior profiles, characterized in that, The device includes: The acquisition module is used to acquire the spatiotemporal behavior profile of a user or user group and the corresponding policies and billing control (PCC) policies. The spatiotemporal behavior profile is constructed based on the user or user group's historical cell dwell sequence, location trajectory and service data. The PCC policy is generated based on the spatiotemporal behavior features of the spatiotemporal behavior profile. The generation module is used to generate a prediction measurement template based on the spatiotemporal behavior profile and the corresponding PCC strategy. The prediction measurement template is configured with a reduction measurement strategy adapted to the spatiotemporal behavior profile. The first sending module is used to send a predictive measurement template configured with the reduction measurement strategy to the terminal, so that the terminal can perform targeted reduction wireless access measurement based on the reduction measurement strategy.

11. A measurement and control device based on user behavior profiles, characterized in that, The device includes: The receiving module is used to receive the prediction measurement template sent by the network side. The prediction measurement template is configured with a reduction measurement strategy that adapts to the spatiotemporal behavior profile of the user or user group. The measurement module is used to perform directional wireless access reduction measurement according to the reduction measurement strategy; The determination module is used to determine the degree of behavioral deviation between the measured behavioral data and the predicted behavioral data of the spatiotemporal behavioral profile; The sending module is used to report to the network side when the behavior deviation meets the reporting conditions, so that the network side can determine whether to trigger the fast backoff mechanism of the full baseline measurement.

12. An electronic device, characterized in that, include: Including processor and memory; And a program or instructions stored on the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the measurement and control method based on user behavior profiles as described in any one of claims 1 to 9.

13. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor of an electronic device, implement the steps of the measurement and control method based on user behavior profiles as described in any one of claims 1 to 9.

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