Priority determination method, resource allocation method and device

Abstract

The invention relates to the field of sensing communication, and provides a priority determination method and a resource allocation method and device, and the priority determination method comprises the steps: obtaining sensing results corresponding to N sensing regions; wherein the N sensing areas are obtained by dividing the sensing range of at least one sensing device, and N is an integer greater than or equal to 2; obtaining priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; wherein the priorities are used for carrying out resource allocation on the sensing services corresponding to the N sensing areas. By determining the priorities of the sensing areas, the flexibility of sensing resource allocation can be improved.

Description

Technical Field

[0001] This application relates to the field of sensing communication, and more particularly, to a method and apparatus for determining priority and a method and apparatus for resource allocation. Background Art

[0002] With the evolution of communication technology, communication frequency bands have derived sensing capabilities, especially in the millimeter-wave band. When sensing devices such as base stations or terminals have sensing capabilities, they can sense and identify specific regions, specific objects, specific events, etc. Utilizing the advantages of wide coverage, long coverage, large bandwidth, high precision, low latency, etc. possessed by communication networks, extremely high sensing capabilities can be achieved.

[0003] Currently, there are diverse sensing requirements in the integrated communication and sensing scenario, and the realization of sensing requirements depends on the allocation of sensing resources. However, in existing standards, the allocation methods for sensing resources are not flexible enough to adapt to the diverse sensing requirements in the integrated communication and sensing scenario.

[0004] Therefore, how to improve the flexibility of sensing resource allocation has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method and apparatus for determining priority and a method and apparatus for resource allocation. By determining the priority of sensing regions, it is beneficial to improve the flexibility of sensing resource allocation.

[0006] In a first aspect, a method for determining priority is provided, which is applied to a core network device and includes: obtaining sensing results corresponding to N sensing regions; wherein the N sensing regions are obtained by dividing the sensing ranges of at least one sensing device, and N is an integer greater than or equal to 2; obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions; wherein the priorities are used for resource allocation for sensing services corresponding to the N sensing regions.

[0007] In the above technical solution, the concept of priority is proposed for sensing regions, and the priorities of the N sensing regions are determined according to the sensing results corresponding to the N sensing regions. The change of sensing results will affect the priority. As the sensing results corresponding to the sensing regions change, the priority of the sensing region may also change, that is, the priority can be dynamically changed according to the sensing results rather than being fixed. The change of the priorities of the N sensing regions will affect the resource allocation of sensing services corresponding to the N sensing regions, which is beneficial to improving the flexibility of sensing resource allocation and adapting to the diverse sensing requirements in the integrated communication and sensing scenario.

[0008] For example, the sensing resources are preferentially allocated to the sensing services corresponding to the sensing regions with high priority, so that the targets located in the sensing regions with high priority can be quickly identified and marked as high-priority targets, thereby realizing the preferential sensing and processing of these targets.

[0009] Optionally, according to the sensing results corresponding to the N sensing regions, the priorities of the N sensing regions can be obtained. The priorities of the N sensing regions can be initialized or updated.

[0010] Combined with the first aspect, in some implementation manners of the first aspect, obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions includes: obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements.

[0011] In the above technical solution, on the basis of the sensing results, the sensing service requirements are further introduced as factors for determining the priorities, so that the priorities of each sensing region can be comprehensively evaluated and dynamically adjusted under the dual drive of the sensing results and the sensing service requirements. Through this implementation manner, the priorities of the sensing regions can not only reflect the complexity of the environment and the potential risk level of the sensing regions, but also reflect the service requirements of the sensing services carried by them, which is beneficial to improving the rationality and scientificity of the priority evaluation.

[0012] Combined with the first aspect, in some implementation manners of the first aspect, obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements includes: obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions, the sensing service requirements and the target information; where the target information includes the spatio-temporal feature information and / or the regional attribute information of the N sensing regions; the regional attribute information includes the urgency of the sensing region and / or the preset importance level of the sensing region.

[0013] In the above technical solution, on the basis of the sensing results and the sensing service requirements, the spatio-temporal feature information and / or the regional attribute information are further introduced as influencing factors for priority evaluation. Thus, the priorities of each sensing region can be comprehensively evaluated and dynamically adjusted under the joint drive of multi-dimensional factors such as the sensing results, the sensing service requirements, the spatio-temporal feature information and / or the regional attribute information. By introducing the above multi-dimensional information, the complexity of the environment where the sensing region is located, the importance of the sensing task, the time sensitivity of the event occurrence and the attributes of the region itself can be more comprehensively reflected, thereby improving the rationality and scientificity of the priority evaluation, and further contributing to the flexible allocation of sensing resources to meet the diverse sensing needs of different sensing regions.

[0014] Optionally, the urgency level is used to indicate whether a preset emergency event has occurred in the sensing area and the urgency level of the preset emergency event.

[0015] In combination with the first aspect, in some implementation manners of the first aspect, according to the sensing results corresponding to N sensing areas, sensing service requirements, and target information, the priorities of the N sensing areas are obtained, including: determining the priority scores corresponding to the N sensing areas according to the sensing results corresponding to the N sensing areas, sensing service requirements, and target information; obtaining the priorities of the N sensing areas according to the priority scores corresponding to the N sensing areas; wherein, the priority score is positively correlated with the priority.

[0016] In the above technical solution, through the above quantization scoring mechanism, the sensing results, sensing service requirements, and target information can be uniformly converted into comparable and computable priority scores, which helps to intuitively reflect the priorities of each sensing area through the priority scores, provides an objective basis for determining the priorities of the sensing areas, and improves the scientificity and rationality of the determined priorities.

[0017] In combination with the first aspect, in some implementation manners of the first aspect, the N sensing areas include a first sensing area, and the first sensing area is any one of the N sensing areas; determining the priority scores corresponding to the N sensing areas according to the sensing results corresponding to the N sensing areas, sensing service requirements, and target information includes: determining a first target score corresponding to the first sensing area according to the sensing result corresponding to the first sensing area; determining a second target score corresponding to the first sensing area according to the sensing service requirement of the first sensing area; determining a third target score corresponding to the first sensing area according to the target information of the first sensing area; and determining the priority score corresponding to the first sensing area according to the first target score, the second target score, and the third target score.

[0018] In the above technical solution, quantization scores are respectively given to the sensing results, sensing service requirements, and target information. Through the multi-dimensional scoring mechanism, the sensing results, service requirements, and target information are comprehensively considered, which improves the scientificity and practicality of the judgment of the priorities of the sensing areas.

[0019] In combination with the first aspect, in some implementation manners of the first aspect, the sensing result includes multiple sensing environment characteristics, and the first target score is obtained by weighting the scores corresponding to the respective sensing environment characteristics in the multiple sensing environment characteristics by the coefficients corresponding to the respective sensing environment characteristics.

[0020] In the above technical solution, various sensing environment characteristics can be uniformly converted into comparable and computable scores, which helps to more accurately reflect the influence of the actual environment of each sensing area on the priority, thereby providing an objective basis for the dynamic update of the priorities of the sensing areas and enhancing the scientificity and rationality of the priority update.

[0021] In combination with the first aspect, in some implementations of the first aspect, the scores corresponding to the respective perceived environmental characteristics are positively correlated with the severity of the environments characterized by the respective perceived environmental characteristics.

[0022] In the above technical solution, in the perceived area with a more severe environment, a higher priority score will be reflected in the scoring result. Thus, in the subsequent process of allocating sensing resources, sensing resources can be preferentially allocated to the perceived areas with higher priority scores and more complex environments, improving the adaptability to complex environments and the timeliness and reliability of obtaining sensing data. By preferentially sensing the areas with severe environments, potential risk factors can be discovered earlier, thereby providing high-quality data support for subsequent decision-making and enhancing the security guarantee ability and service quality level of the system in critical scenarios.

[0023] In combination with the first aspect, in some implementations of the first aspect, the sensing service requirements include multiple service requirement parameters, and the second target score is obtained by weighting the scores corresponding to the respective service requirement parameters among the multiple service requirement parameters with the corresponding coefficients of the respective service requirement parameters.

[0024] In the above technical solution, the respective service requirement parameters can be uniformly converted into comparable and computable scores, which helps to more intuitively reflect the influence of the sensing service requirements of each perceived area on the priority, thereby providing an objective basis for the dynamic update of the priority of the perceived area and enhancing the scientificity and rationality of the priority update.

[0025] In combination with the first aspect, in some implementations of the first aspect, the multiple service requirement parameters include QoS parameters, and the score corresponding to the QoS parameter is negatively correlated with the level of the QoS parameter.

[0026] In the above technical solution, for the perceived area corresponding to the sensing service with a lower QCI value, which is the level of the QoS parameter, a higher priority score is reflected in the scoring result, thereby reasonably quantifying the influence of the QCI value on the priority of the perceived area. This solution helps to preferentially allocate limited sensing resources to the perceived areas with more complex sensing environments or higher requirements for service quality of services during the resource allocation process, improving the service guarantee ability for high-quality services and meeting the sensing and communication service quality requirements in diverse scenarios.

[0027] In combination with the first aspect, in some implementations of the first aspect, the third target score is obtained based on the fourth target score corresponding to the spatio-temporal feature information and / or the fifth target score corresponding to the regional attribute information.

[0028] In combination with the first aspect, in some implementations of the first aspect, the spatio-temporal feature information includes time information and information about the space where the first sensing area is located. The fourth target score is obtained by weighting the product of the score corresponding to the time information and the coefficient corresponding to the time information, and the product of the score corresponding to the information about the space and the coefficient corresponding to the information about the space.

[0029] In the above technical solution, the time information and the position information can be uniformly converted into comparable and computable scores, which helps to more intuitively reflect the influence of the spatio-temporal feature information of each sensing area on the priority, thereby providing an objective basis for the dynamic update of the priority of the sensing area and enhancing the scientificity and rationality of the priority update.

[0030] In combination with the first aspect, in some implementations of the first aspect, the score corresponding to the time information is determined based on the time period in which the time information is located; when the time period in which the time information is located is the preset peak time period corresponding to the first sensing area, the score corresponding to the time information is the first preset score; when the time period in which the time information is located is the preset flat peak time period corresponding to the first sensing area, the score corresponding to the time information is the second preset score; when the time period in which the time information is located is the preset low peak time period corresponding to the first sensing area, the score corresponding to the time information is the third preset score; wherein, the first preset score is greater than the second preset score, and the second preset score is greater than the third preset score.

[0031] In the above technical solution, for the sensing area in the preset peak time period, its corresponding score is higher, so that it is in a relatively preferential position in resource allocation, while for the sensing area in the low peak time period, it correspondingly obtains a lower score, so that it is in a relatively secondary position in resource allocation. Determining the score corresponding to the time information based on whether the time information is in the peak time period, the low peak time period or the flat peak time period is beneficial to improving the resource utilization efficiency and the sensing service quality guarantee ability in the dynamic time-varying environment.

[0032] In combination with the first aspect, in some implementations of the first aspect, the score corresponding to the position information is negatively correlated with the target distance, and the target distance is the distance between the first sensing area and the preset sensing area.

[0033] In the above technical solution, the closer the distance between the first sensing area and the preset sensing area is, the higher the score corresponding to the position information it obtains, so that it is in a relatively preferential position in resource allocation compared with other sensing areas that are farther away from the preset sensing area, which is beneficial to preferentially allocating limited sensing resources to the sensing areas adjacent to the preset sensing area and improving the sensing response ability and safety guarantee level of the surrounding environment of the key area.

[0034] In combination with the first aspect, in some implementations of the first aspect, the priorities of the N sensing regions are obtained according to the sensing results corresponding to the N sensing regions and the sensing service requirements, including: determining the priority scores corresponding to the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements; obtaining the priorities of the N sensing regions according to the priority scores corresponding to the N sensing regions; wherein, the priority scores are positively correlated with the priorities.

[0035] In combination with the first aspect, in some implementations of the first aspect, the N sensing regions include a second sensing region, and the second sensing region is any one of the N sensing regions; determining the priority scores corresponding to the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements includes: determining a first target score corresponding to the second sensing region according to the sensing result corresponding to the second sensing region; determining a second target score corresponding to the sensing region according to the sensing service requirement of the second sensing region; determining the priority score corresponding to the second sensing region according to the first target score and the second target score.

[0036] In combination with the first aspect, in some implementations of the first aspect, the priorities of the N sensing regions are obtained according to the sensing results corresponding to the N sensing regions, including: if a preset trigger condition is satisfied, obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions; wherein, the preset trigger condition includes at least one of the following: the current time is a preset periodic update time, the environmental characteristics of the sensing region satisfy a preset condition, the predicted probability of a preset event occurring in the sensing region is greater than a preset threshold, or an instruction to update the priority is received from a user.

[0037] In combination with the first aspect, in some implementations of the first aspect, the regional attribute information includes the urgency level and a preset importance level of the first sensing region, and the fifth target score is obtained by weighting the product of the score corresponding to the urgency level and the coefficient corresponding to the urgency level, and the product of the score corresponding to the preset importance level and the coefficient corresponding to the preset importance level.

[0038] In the above technical solution, the urgency level and the preset importance level of the first sensing region can be uniformly converted into comparable and computable scores, which helps to more intuitively reflect the influence of the regional attribute information of each sensing region on the priority, thereby providing an objective basis for the dynamic update of the priority of the sensing region and enhancing the scientificity and rationality of the priority update.

[0039] In combination with the first aspect, in some implementations of the first aspect, the score corresponding to the urgency level is positively correlated with the urgency level, and the score corresponding to the preset importance level is positively correlated with the preset importance level.

[0040] In the above technical solution, the higher the emergency level or the preset importance level of the first sensing area, the higher the corresponding score, making it in a relatively prioritized position in resource allocation compared to other sensing areas with a lower emergency level or preset importance level. This is beneficial for preferentially allocating limited sensing resources to the sensing areas with a high emergency level or preset importance level, and improving the sensing response ability and security guarantee level for the sensing areas with a high emergency level or preset importance level.

[0041] Combined with the first aspect, in some implementation manners of the first aspect, after obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas, the method further includes: sending a first message to the base station; wherein, the first message carries first information, and the first information is used to indicate the priority information of M sensing areas among the N sensing areas, and M is an integer less than or equal to N.

[0042] In the above technical solution, through the design of the above multi-dimensional trigger conditions, it is possible to flexibly control the update of priorities and the rhythm of sensing resource scheduling in different service scenarios, realize the dynamic and intelligent management of the priorities of sensing areas, and is beneficial to meeting the personalized sensing resource scheduling requirements.

[0043] Combined with the first aspect, in some implementation manners of the first aspect, after obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas, the method further includes: sending a first message to the base station; wherein, the first message carries first information, and the first information is used to indicate the priority information of M sensing areas among the N sensing areas, and M is an integer less than or equal to N.

[0044] In the above technical solution, after determining the priorities of the N sensing areas, sending the priority information to the base station facilitates the base station to allocate sensing resources according to the received priorities, and improves the flexibility of resource allocation.

[0045] Combined with the first aspect, in some implementation manners of the first aspect, after obtaining the priorities of the N sensing areas, according to the priorities of the N sensing areas, obtaining a resource allocation result; wherein, the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the N sensing areas; sending a second message to the base station; wherein, the second message carries the resource allocation result.

[0046] Combined with the first aspect, in some implementation manners of the first aspect, the resource allocation result further includes: information of the target sensing device; wherein, the target sensing device is determined based on the reported data of at least one sensing device, and the reported data includes the location data and device attribute data of at least one sensing device.

[0047] In the above technical solution, it is possible to dynamically select target sensing devices suitable for participating in the sensing services corresponding to each sensing area based on the location data and device attribute data reported by the sensing devices, and combine the sensing area priorities and resource requirements to achieve intelligent scheduling and optimal allocation of sensing resources, which is beneficial to meeting the diverse sensing requirements of different sensing areas.

[0048] Combined with the first aspect, in some implementation manners of the first aspect, the first information is information on the priority levels of M sensing areas, and the priority levels of the M sensing areas are obtained based on the priority scores of each sensing area in the M sensing areas.

[0049] Combined with the first aspect, in some implementation manners of the first aspect, the first message further includes: identification information and / or the validity period of the priority of the M sensing areas; wherein, the identification information is determined based on the center of the sensing area and the offset of the boundary of the sensing area relative to the center.

[0050] Combined with the first aspect, in some implementation manners of the first aspect, the sensing result includes the characteristics of the sensing environment.

[0051] Combined with the first aspect, in some implementation manners of the first aspect, the sensing device includes at least one of the following: a non-3GPP device, a UE, and a base station.

[0052] In the above technical solution, at the data collection end, i.e., the core network side, the sensing data of three types of sensing devices can be collected, which expands the range of data collected by the core network devices and is beneficial to improving the decision-making accuracy of the core network devices.

[0053] In a second aspect, a resource allocation method is provided, which is applied to a base station and includes: receiving a first message sent by a core network device; wherein, the first message carries first information, and the first information is used to indicate the priority information of M sensing areas among N sensing areas. The N sensing areas are obtained by dividing the sensing ranges of at least one sensing device, and the priorities of the N sensing areas are obtained by the core network device according to the sensing results corresponding to the N sensing areas. M is an integer less than or equal to N, and N is an integer greater than or equal to 2; obtaining a resource allocation result according to the first information; wherein, the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the M sensing areas.

[0054] In the above technical solution, delegating the permissions of the core network device and dynamically allocating sensing resources by the base station side is beneficial to improving the allocation efficiency of sensing resources. Based on the physical location advantage that the base station is closer to the sensing devices (such as UEs, non-3GPP devices, etc.) than the core network device, in scenarios where sensing tasks require quick response (such as traffic warning, emergency monitoring, etc.), the base station can complete resource scheduling decisions faster, reduce control latency, and improve the overall timeliness of the system. After receiving the first message sent by the core network device, the base station can directly perform sensing resource allocation locally according to the priorities of the M sensing regions without waiting for the resource allocation result sent by the core network device. If the priorities of the M sensing regions change, the resource allocation result obtained by the base station for sensing resource allocation will also change, which is beneficial to improving the flexibility of sensing resource allocation, realizing preferential allocation of sensing resources to the sensing regions with higher priorities, and adapting to the diverse sensing requirements in the integrated communication and sensing scenario.

[0055] Combined with the second aspect, in some implementation manners of the second aspect, the first information is the information of the priority levels of the M sensing regions, and the priority levels of the M sensing regions are obtained based on the priority scores of each sensing region in the M sensing regions.

[0056] Combined with the second aspect, in some implementation manners of the second aspect, obtaining the resource allocation result according to the first information includes: determining L groups of sensing regions according to the priority levels of the M sensing regions; where the priority levels of each sensing region in each group of sensing regions are the same, and L is an integer greater than or equal to 1; obtaining the resource allocation result according to the sorting of the priority levels of the L groups of sensing regions, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to each sensing region.

[0057] In the above technical solution, the method of allocating resources according to the sorting of the priority levels is beneficial to ensuring that the sensing services corresponding to the sensing regions with higher priorities are preferentially allocated sensing resources, so as to realize the preferential sensing and processing of the sensing regions with higher priorities.

[0058] Combined with the second aspect, in some implementation manners of the second aspect, when the resource requirements of the sensing services corresponding to each sensing region in the i-th group of sensing regions are less than or equal to the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to each sensing region in the i-th group of sensing regions are determined based on the resource requirements of the sensing services corresponding to each sensing region; where 1 ≤ i ≤ L.

[0059] In combination with the second aspect, in some implementation manners of the second aspect, when the resource requirement of the sensing service corresponding to each sensing area in the i-th group of sensing areas is greater than the total amount of resources to be allocated, the resource allocation amount of the sensing service corresponding to each sensing area in the i-th group of sensing areas is determined based on the total number of sensing areas in the i-th group of sensing areas and the total amount of resources to be allocated.

[0060] In the above technical solution, it is possible to preferentially guarantee the sensing capabilities of high-priority sensing areas under resource constraints and achieve fair resource allocation among low-priority sensing areas, so as to achieve efficient utilization of sensing resources and reasonable control of service quality on a global scale.

[0061] In combination with the second aspect, in some implementation manners of the second aspect, the resource allocation result further includes: information of a target sensing device; wherein the target sensing device is determined based on the reported data of at least one sensing device, and the reported data includes the location data and device attribute data of at least one sensing device.

[0062] In combination with the second aspect, in some implementation manners of the second aspect, after receiving the first message sent by the core network device, the method further includes: sending a data reporting request to at least one sensing device; receiving the reported data of at least one sensing device and sending the reported data to the core network device; wherein the reported data includes the location data and device attribute data of at least one sensing device.

[0063] In a third aspect, a priority update device is provided, including: an acquisition module, configured to acquire sensing results corresponding to N sensing areas; wherein the N sensing areas are obtained by dividing the sensing ranges of at least one sensing device, and N is an integer greater than or equal to 2; an update module, configured to obtain the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; wherein the updated priorities are used for resource allocation of the sensing services corresponding to the N sensing areas.

[0064] It should be understood that the extensions, limitations, explanations and descriptions of the relevant content in the first aspect above also apply to the same content in the third aspect.

[0065] Fourthly, a resource allocation device is provided, including: a receiving module, configured to receive a first message sent by a core network device; wherein, the first message carries first information for indicating the priorities of M sensing areas among N sensing areas, the N sensing areas are obtained by dividing the sensing ranges of at least one sensing device, the priorities of the N sensing areas are obtained by the core network device according to the sensing results corresponding to the N sensing areas, M is an integer less than or equal to N, and N is an integer greater than or equal to 2; an allocation module, configured to obtain a resource allocation result according to the first information; wherein, the resource allocation result includes the resource allocation amounts for the sensing services corresponding to the M sensing areas.

[0066] It should be understood that the extensions, limitations, explanations, and descriptions of the relevant content in the above second aspect also apply to the same content in the fourth aspect.

[0067] Fifthly, a core network device is provided, including: a memory, configured to store executable program code; a processor, configured to call and run the executable program code from the memory, so that the core network device executes the above priority determination method.

[0068] Sixthly, a base station is provided, including: a memory, configured to store executable program code; a processor, configured to call and run the executable program code from the memory, so that the base station executes the above resource allocation method.

[0069] Seventhly, a computer-readable storage medium is provided. The computer-readable storage medium stores computer program code. When the computer program is executed by the core network device, the core network device is caused to execute the above priority determination method. When the computer program is executed by the base station, the base station is caused to execute the above resource allocation method.

[0070] Eighthly, a computer program product is provided. The computer program product includes: computer program code. When the computer program code is executed by the core network device, the core network device is caused to execute the priority determination method in the first aspect or any implementation manner in the first aspect. When the computer program code is executed by the base station, the base station is caused to execute the resource allocation method in the second aspect or any implementation manner in the second aspect. Description of the Drawings

[0071] Figure 1 is a schematic diagram of the architecture of a mobile communication system applicable to the embodiments of the present application; Figure 2 is a schematic diagram of a scenario for environmental sensing on a railway track provided by the embodiments of the present application; Figure 3It is a schematic diagram of a vehicle - to - everything (V2X) communication and perception system architecture based on a 5G network provided by an embodiment of the present application; Figure 4 It is a schematic diagram of a weather perception scenario provided by an embodiment of the present application; Figure 5 It is a schematic flowchart of a priority determination method provided by an embodiment of the present application; Figure 6 It is a schematic diagram of a hierarchical structure adopted by a comprehensive evaluation model provided by an embodiment of the present application; Figure 7 It is a schematic process of a resource allocation method provided by an embodiment of the present application; Figure 8 It is a schematic flowchart of resource allocation according to the sorting of priority levels provided by an embodiment of the present application; Figure 9 It is an interaction schematic diagram related to the priority determination method and the resource allocation method provided by an embodiment of the present application; Figure 10 It is a schematic diagram of a scenario for updating the priority of a perception area provided by an embodiment of the present application; Figure 11 It is a schematic diagram of the structure of a priority determination device provided by an embodiment of the present application; Figure 12 It is a schematic diagram of the structure of a resource allocation device provided by an embodiment of the present application; Figure 13 It is a schematic diagram of the structure of a core network device provided by an embodiment of the present application; Figure 14 It is a schematic diagram of the structure of a base station provided by an embodiment of the present application. Detailed implementation manners

[0072] In the embodiments of the present application, the following terms "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second" may explicitly or implicitly include one or more of such features. In the description of this embodiment, unless otherwise stated, the meaning of "a plurality" is two or more.

[0073] The technical solution of the embodiment of the present application can be applied to various communication systems, such as: Long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD), Universal Mobile Telecommunication System (UMTS), New Radio (NR) in the 5th Generation (5G) mobile communication system, and future mobile communication systems, such as the 6th generation mobile communication system, etc.

[0074] The technical solution provided by the present application can also be applied to Machine Type Communication (MTC), Long Term Evolution - Machine (LTE - M), Device - to - Device (D2D) network, Machine - to - Machine (M2M) network, Internet of Things (IoT) network or other networks. Among them, the IoT network can include, for example, the vehicle - to - everything network; the communication methods in the vehicle - to - everything network are collectively referred to as Vehicle - to - X (V2X), where X can represent anything. For example, the V2X can include: vehicle - to - vehicle (V2V) communication, vehicle - to - infrastructure (V2I) communication, vehicle - to - pedestrian (V2P) communication, or vehicle - to - network (V2N) communication, etc. The embodiments of the present application do not limit this.

[0075] Figure 1 It is a schematic diagram of the architecture of the mobile communication system applicable to the embodiment of the present application.

[0076] As Figure 1 shown, the communication system includes a radio access network 100, a core network 200, and the Internet 300. The radio access network 100 may include at least one access network device (such as Figure 1 110a and 110b in Figure 1120a, 120b, 120c, 120d, 120e, 120f, 120g, 120h, 120i, and 120j in (collectively referred to as terminal 120). Terminals 120a - 120j are connected to access network devices 110a and 110b wirelessly. Access network devices 110a and 110b are connected to core network 200 wirelessly or by wire. Core network devices in core network 200 and access network devices in the radio access network may be different physical devices, or may be the same physical device integrating the core network logical function and the radio access network logical function, or may be a physical device integrating part of the functions of core network devices and part of the functions of radio access network devices. Terminals can be connected to each other by wire or wirelessly, and access network devices can be connected to each other by wire or wirelessly. It should be understood that Figure 1 This is only a schematic diagram, and other network devices may also be included in this communication system. For example, it includes wireless relay devices and / or wireless backhaul devices, etc., which are not shown in Figure 1 it.

[0077] The access network device 110 in the radio access network 100 of the embodiment of this application is sometimes also referred to as an access node. The access network device 110 has a wireless transceiver function and is used to communicate with the terminal 120. The access network device 110 includes, but is not limited to, base stations in the above communication systems, evolved NodeBs (eNodeBs), transmission reception points (TRPs), next-generation base stations (gNBs) in 5G mobile communication systems, next-generation base stations in 6th generation (6G) mobile communication systems, access network devices or modules of open RAN (ORAN) systems, base stations in future mobile communication systems, or access nodes in WiFi systems, etc. The access network device 110 can also be a module or unit capable of implementing some functions of a base station. For example, the access network device 110 can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU) described below. Among them, in the ORAN system, the CU can also be called an O-CU, the DU can also be called an open (O)-DU, the CU-CP can also be called an O-CU-CP, the CU-UP can also be called an O-CUP-UP, and the RU can also be called an O-RU. The access network device 110 can be a macro base station (such as Figure 1 110a in Figure 1 ), a micro base station or an indoor station (such as 110b in Figure 1 ), a relay node or a donor node, or a radio controller in a cloud radio access network (CRAN) scenario. Optionally, the access network device 110 can also be a server, a wearable device, or a vehicle-mounted device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU). Multiple access network devices in a communication system can be of the same type of base station or different types of base stations. A base station can communicate with a terminal or communicate with a terminal through a relay station. A terminal can communicate with multiple base stations in different access technologies. The embodiments of this application do not limit the specific technologies and specific device forms adopted by the access network device 110. In this application, the access network device is abbreviated as a "network device". Unless otherwise specified, in this application, network devices all refer to access network devices.

[0078] A terminal can also be referred to as a terminal device, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely applied in various communication scenarios. For example, they can be applied in device-to-device (D2D) communication, vehicle to everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, or smart city scenarios. A terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, a drone, a helicopter, an airplane, a ship, a robot, a robotic arm, or a smart home device, etc. The embodiments of this application do not limit the device form of the terminal.

[0079] The access network device and / or the terminal can be fixed or movable. The access network device and / or the terminal can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and can also be deployed on airplanes, balloons, and artificial satellites in the air. The embodiments of this application do not limit the application scenarios of the access network device and the terminal. In addition, the access network device and the terminal can be deployed in the same scenario or different scenarios. For example, the access network device and the terminal are both deployed on land; or, the access network device is deployed on land and the terminal is deployed on water, and no further examples are given one by one.

[0080] Taking the base station as an example of the network device, the roles of the base station and the terminal can be relative. For example, Figure 1 the helicopter or drone 120i in [Figure] can be configured as a mobile base station. For those terminals 120j that access the radio access network 100 through 120i, the terminal 120i is a base station; but for the base station, 120i is a terminal. The communication between 110a and 120i is through the radio air interface protocol, or the communication between 110a and 120i can also be through the interface protocol between base stations. At this time, relative to 110a, 120i is also a base station. Therefore, both the base station and the terminal can be uniformly referred to as communication devices. Figure 1 110a and 110b in [Figure] can be referred to as communication devices with base station functions. Figure 1 120a - 120j in [Figure] can be referred to as communication devices with terminal functions.

[0081] In the embodiments of the present application, the communication device with the function of an access network device may be an access network device, or a module in the access network device (such as a chip, a chip system, or a software module, etc.), or a control subsystem including the function of the access network device. For example, the control subsystem with the function of the access network device may be a control center in scenarios applicable to terminals such as a smart grid, industrial control, intelligent transportation, or a smart city.

[0082] In the embodiments of the present application, the communication device with the function of a terminal may be a terminal, or a module in the terminal (such as a chip, a chip system, a modem, or a software model, etc.), or a device including the function of the terminal. In the embodiments of the present application, for ease of description, subsequent descriptions will be made by taking a network device (such as a base station) and a terminal device (UE) as examples.

[0083] The communication between the base station and the terminal, between the base station and the base station, and between the terminal and the terminal can be carried out through authorized spectrum, or through unlicensed spectrum, or through both authorized spectrum and unlicensed spectrum at the same time; it can be carried out through the spectrum below 6 gigahertz (GHz), or through the spectrum above 6 GHz, or through both the spectrum below 6 GHz and the spectrum above 6 GHz at the same time. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0084] The technical solutions provided in the embodiments of the present application can be applied to the wireless communication between communication devices. The wireless communication between communication devices may include: the wireless communication between the base station and the terminal, the wireless communication between the base station and the base station, and the wireless communication between the terminal and the terminal. It can be understood that in the embodiments of the present application, the physical downlink share channel (PDSCH), the physical downlink control channel (PDCCH), and the physical uplink share channel (PUSCH) are only examples of the downlink data channel, the downlink control channel, and the uplink data channel respectively. In different systems and different scenarios, the data channel and the control channel may have different names, and the embodiments of the present application do not limit this. In the present application, the base station sends downlink signals or downlink information to the terminal, and the downlink information is carried on the downlink channel; the terminal sends uplink signals or uplink information to the base station, and the uplink information is carried on the uplink channel.

[0085] To facilitate the understanding of the present application, the following terms related to the present application are introduced: 1. Sensing Sensing can refer to a communication entity in a wireless network determining information about the surrounding environment by sending and receiving signals that have been affected by objects. Among them, the information about the surrounding environment can include information about one or more objects in the environment. The information about an object can include the position, speed, size, or shape of the object, etc. These objects can change the transmission characteristics of the signal. For example, they can change the transmission direction of the signal, change the transmission gain of the signal, change the transmission delay of the signal, or change the frequency of the signal, etc. Therefore, the communication entity can achieve sensing by obtaining the changes in the signal transmission characteristics. Sensing can also be called detection, which refers to detecting a target by emitting electromagnetic waves and analyzing the echo signals reflected from the object.

[0086] The above-mentioned objects can be moving or stationary, and can also be active or passive. Active can mean that the object has data processing capabilities, such as base stations, mobile phones, routers, vehicles, drones, radio frequency identification (RFID) devices, etc. Passive can mean that the object does not have data processing capabilities, such as humans, animals, plants, vehicles, buildings, etc.

[0087] It should be understood that an object can also be called a scatterer, reflector, refractor, blocker, obstacle, target, or sensing target, etc. It should also be understood that the above-mentioned communication entity can also be called a sensing device, network entity, communication device, communication equipment, communication node, or station.

[0088] 2. Sensing Function (SF) The sensing function SF is mainly involved in receiving sensing service requests and obtaining corresponding sensing requirements, selecting and requesting relevant sensing devices to perform sensing operations and receiving corresponding sensing measurement data, and opening the sensing measurement data or the sensing results obtained based on the sensing measurement data to the sensing requester. The sensing function can at least include the ability to send sensing signals and receive echo signals of the sensing signals.

[0089] Optionally, SF can be separated into a control plane and a user plane. That is, the SF control plane (SF-C) function and the SF user plane (SF-U) function are separated. Among them, SF-C can send control signaling, such as sensing control requests, to the sensing device through the control plane. SF-U can receive sensing measurement data from the sensing device through the data plane and optionally process the sensing measurement data to obtain sensing results. SF-C can control SF-U. For example, SF-C can select a suitable SF-U and configure one or more of the identification rules, processing rules, or routing rules for the sensing measurement data to this SF-U.

[0090] 3. Sensing Devices A sensing device is a device that performs sensing tasks, which can also be referred to as sensing services, sensing operations, or sensing services. The sensing operations performed by the sensing device include at least one of the following: 1) The sensing device sends a sensing signal; 2) The sensing device receives a sensing signal; 3) The sensing device obtains sensing data based on the received sensing signal.

[0091] Sensing devices include devices with sensing functions such as base stations, UEs, and non-3GPP devices. Non-3GPP devices have been widely used in fields such as residential life, industrial production, and environmental monitoring. Non-3GPP devices may include, for example, wearable devices (such as Bluetooth bracelets, Bluetooth watches, or Bluetooth headsets), sensing devices (such as temperature, humidity, or barometric pressure sensors using ZigBee technology), or tag devices (such as RFID tag devices).

[0092] The sensing task can be performed by the UE, or by the base station, or by the UE and the base station in cooperation. For example: The UE sends a sensing signal and receives the sensing signal (also called the echo signal) reflected by the target object from the sensing signal. For example: The base station sends a sensing signal and receives the sensing signal reflected by the target object from the sensing signal. For example: The UE sends a sensing signal, and the base station receives the sensing signal reflected by the target object from the sensing signal. For example: The base station sends a sensing signal, and the UE receives the sensing signal reflected by the target object from the sensing signal. After the sensing device receives the sensing signal, it processes the sensing signal to generate sensing data, and uploads the sensing data to the SF network element, and the SF network element processes the sensing data to obtain a sensing result.

[0093] A sensing signal is a signal used to sense (or detect) a sensed target (or object). A sensing signal is also called a detection signal, a chirp signal, a radar signal, a radar sensing signal, a radar detection signal, an environmental sensing signal, etc. The sensing signal can be a pulse signal or a signal possible in a wireless communication system, such as an orthogonal frequency division multiplexing (OFDM) signal, a Channel State Information Reference Signal (CSI-RS), or a Sounding Reference Signal (SRS). Relative to the sensing signal, the signal transmitted between the network device and the terminal device can be considered a communication signal. For example, the signal carried on the physical downlink shared channel (PDSCH).

[0094] The echo signal refers to the signal that the sensing signal reflects back to the receiver after being transmitted from the transmitter to the object, also known as the reflected signal. Autocorrelation processing is performed on the echo signal and the sensing signal, and then through transformation, the time delay of the echo signal in the time domain relative to the sensing signal can be analyzed, so as to reflect the distance of the sensing target from the emission source. By comparing the echo signals reflected from the same target by different transmitted signals, it can be converted into the Doppler domain; combining the Doppler domain and the distance domain can analyze the distance and speed of the sensing target. In addition, through the beam direction of the antenna that emits the sensing signal, the direction of the sensing target relative to the emission source can be obtained.

[0095] 4. Sensing Modes The sensing mode refers to the working mode of the sensing device when performing sensing tasks, including the self-transmitting and self-receiving sensing mode and the cooperative sensing mode. Taking the sensing device as a base station as an example, the following three sensing modes are introduced: base station self-transmitting and self-receiving sensing, base station-to-base station transceiver sensing, and UE transmitting and base station receiving sensing. Among them, both base station-to-base station transceiver sensing and UE transmitting and base station receiving sensing belong to the cooperative sensing mode.

[0096] Base station self-transmitting and self-receiving sensing: The base station uses the reflected / diffracted signals of its own transmitted signals for sensing. Base station self-transmitting and self-receiving sensing enables the base station to sense the surrounding environment information. This sensing mode can be applied to systems where the sensing receiver and transmitter are jointly deployed at the same location, such as a single-station radar, and requires the base station to have full-duplex capabilities or equivalent capabilities.

[0097] Base station-to-base station transceiver sensing: One base station uses the downlink communication signals received from other base stations for sensing. This sensing mode can be applied to bistatic or multistatic radars, where the transmitter and receiver are spatially separated and the clock requirements are synchronous.

[0098] UE transmitting and base station receiving sensing: The base station uses the uplink signals transmitted from the UE transmitter for sensing. This sensing mode is similar to base station-to-base station transceiver sensing, where the transmitter and receiver are spatially separated but asynchronous.

[0099] Optionally, the sensing device can also be a UE. Taking the sensing device as a UE as an example, the sensing mode can be base station transmitting and UE receiving sensing: The UE uses the downlink signals transmitted from the base station transmitter for sensing.

[0100] 5. Sensing Range The sensing range refers to the maximum spatial coverage area within which a sensing device can effectively perform sensing tasks, which is usually jointly determined by the physical performance of the device (such as transmit power, receive sensitivity, beam width, etc.) and environmental factors (such as occlusion, interference, propagation loss). For example, if the sensing device is a base station, its sensing range can be the service range of the base station, that is, the wireless signal coverage range, specifically a circular area with a radius of several kilometers. If the sensing device is a vehicle-mounted millimeter-wave radar, its sensing range may be a fan-shaped area, and the maximum detection distance can reach hundreds of meters. If the sensing device is a camera, its sensing range is limited by the viewing angle, lighting conditions, and lens focal length.

[0101] 6. Sensing Area The sensing area can be used to indicate the geographical location or area where the sensing device needs to perform sensing operations, which can be represented by absolute coordinates or relative coordinates. The sensing area is a specific area for sensing operations divided based on the sensing range, which represents the geographical area that needs to be focused on or where sensing tasks need to be performed within the sensing range. The sensing area can be statically divided or dynamically adjusted according to task requirements. The shape of the sensing area can be diverse, such as rectangular, fan-shaped, circular, polygonal, etc. For example, the sensing area where a river is located can be in the shape of the river. Sensing areas include, for example, residential areas, park areas, high-speed rail areas, school areas, river areas, street areas, etc.

[0102] Exemplarily, the sensing area can be statically divided, that is, the sensing area can be pre-divided based on the sensing range, so that the shape and number of the sensing area can be fixed. For example, for the roadside radar on a highway, its sensing area is divided by lanes.

[0103] Exemplarily, the sensing area can be dynamically divided. For example, multiple sensing areas can be dynamically divided from the sensing range according to the characteristics of the sensing environment and actual needs, so that the shape, size, and number of the sensing area can be dynamically changed. For example, an autonomous vehicle dynamically adjusts the shape and size of the forward sensing area according to the traffic density ahead.

[0104] Exemplarily, when dividing the sensing area, it can be divided based on the union of the sensing ranges of multiple sensing devices, or based on the sensing range of a single sensing device.

[0105] 7. Sensing Resources Sensing resources can be resources used for sensing, that is, the wireless resources of sensing services. Sensing resources include: time-domain resources, frequency-domain resources, space-domain resources, and code-domain resources, etc., which can be considered as resource pairs formed by time-domain resources, frequency-domain resources, space-domain resources, and code-domain resources.

[0106] Time-domain resources refer to the OFDM symbols occupied in the time domain. The embodiments of the present application do not limit the minimum granularity of time-domain resources. For example, the minimum granularity of time-domain resources is 1 OFDM symbol, or it can be a mini-slot, a slot, etc. One mini-slot can include 2 or more OFDM symbols, and one slot includes 14 OFDM symbols.

[0107] Frequency-domain resources refer to the frequency resources occupied in the frequency domain. The minimum granularity of frequency-domain resources can be 1 subcarrier or a resource element (RE), or it can be a physical resource block (PRB), or a resource block group (RBG), etc. One PRB includes 12 REs in the frequency domain, and one RBG can include 2 PRBs, 4 PRBs, 8 PRBs, or 16 PRBs.

[0108] Spatial-domain resources, which can also be referred to as beam resources, can be divided into transmission beams and reception beams. A transmission beam refers to the distribution of signal strength formed in different directions in space after a signal is transmitted through an antenna. A reception beam refers to the signal strength distribution of the signal received by an antenna in different directions in space. One beam can include one or more antenna ports. Different beams can be regarded as different resources.

[0109] Code-domain resources, which can also be referred to as sequences or symbol resources for sensing, such as ZC (Zadoff–Chu) sequences. The code-domain resources for sensing can be different from the code-domain resources for communication, or they can be the same as the code-domain resources for communication. There is no specific limitation.

[0110] 8. Integrated Sensing and Communications (ISAC) Integrated Sensing and Communications (ISAC) refers to a new information processing technology that simultaneously realizes the coordination of sensing and communication functions based on the sharing of software and hardware resources or information sharing. ISAC can share the spectrum, hardware platform, and even the baseband waveform and signal processing methods between communication and sensing, thereby improving the spectrum efficiency, energy efficiency, and hardware efficiency of the system to obtain an integration gain. Currently, integrated sensing and communications can be achieved through the air interface. Currently, the wireless frequency bands used by base stations or terminals, such as the millimeter-wave band and the terahertz band, have sensing capabilities. The wireless communication system can sense and identify specific regions, objects, or events to meet the sensing requirements in many scenarios and achieve integrated sensing and communications.

[0111] It should be understood that the definitions of the above terms can refer to the prior art. However, with the continuous development of technology, the above definitions may also change, and the embodiments of the present application do not make any restrictions.

[0112] According to the requirements defined in the 3GPP TR 22.837 standard, the 5G network needs to build a multi-dimensional service priority division and dynamic processing mechanism based on the criticality level of services (such as public safety and emergency command), service urgency (such as millisecond-level disaster warning), and Quality of Service (QoS) requirements. In the scenario of limited network resources, the system ensures that critical services (such as emergency rescue communication and vital sign monitoring) can obtain higher-priority network access, spectrum resources, and computing resources compared to ordinary commercial services (such as video streaming and file transfer) through a differential resource preemption strategy, so as to ensure service continuity under extreme conditions. The standard divides priorities for services. In existing standards, there are also other priority-related management mechanisms, which are introduced separately below: In the 3GPP TR 23.700 standard, for high-priority services, such as Mission Critical Push Services (MPS) or Mission Critical Services (MCS), the Access and Mobility Management Function (AMF) network element will add a message priority header to indicate priority information to ensure that these high-priority services are processed in a timely manner.

[0113] In federated learning, multiple User Equipments (UEs) participate in model training, and the network needs to assign priorities to the UEs participating in training to ensure that high-priority devices (such as mission-critical devices) can upload training results first, while optimizing the overall training efficiency.

[0114] In the 3GPP TS 38.300 standard, for the priority management of QoS flows, resources are allocated according to different service types to ensure that high-priority services can obtain better network performance. High-priority QoS flows can obtain resources through a preemption mechanism to ensure the transmission of important services.

[0115] In the Radio Resource Control (RRC) protocol layer, each logical channel is assigned a corresponding priority. In the Medium Access Control (MAC) sublayer, through the logical channel priority mechanism, the priority transmission of high-priority data is ensured.

[0116] The inventors of the present application have found through research that although the above-mentioned standards have established a priority management mechanism from the service dimension, terminal dimension, QoS flow dimension, and logical channel dimension respectively, a solution for setting priorities based on the sensing area has not been proposed yet. For the allocation of sensing resources, in the existing standards, the corresponding sensing resources are usually allocated according to the sensing capabilities of the sensing devices, without considering that priorities can also be set for the sensing area, resulting in an inflexible allocation method for sensing resources and difficulty in adapting to the diverse sensing requirements in the communication-sensing integration scenario. That is to say, in the existing standards, the priorities of the sensing areas are not considered, and there is a lack of a mechanism for linking the priorities of the sensing areas with resource allocation, resulting in an inflexible allocation method for sensing resources and difficulty in meeting diverse and differentiated sensing requirements.

[0117] Based on this, the embodiments of the present application provide a priority determination method, which can realize the initialization and dynamic update of the priorities of the sensing areas. Different from the existing standards that do not consider the priorities of the sensing areas, in the embodiments of the present application, not only the concept of priorities is proposed for the sensing areas, but also the priority order of different sensing areas can be dynamically adjusted according to actual needs to optimize the allocation of sensing resources, improve the flexibility and adaptability of the sensing resource allocation, and meet the complex and diverse sensing requirements in the communication-sensing integration scenario.

[0118] In the embodiments of the present application, sensing can have the following typical application scenarios, namely infrastructure scenarios, driving scenarios, and disaster weather sensing scenarios. Different application scenarios have different sensing service types, and different sensing service types correspond to different sensing requirements.

[0119] For infrastructure scenarios, tasks such as security inspections and track management can be carried out at airports through sensing, tasks such as personnel counting and personnel positioning can be carried out in factories, and tasks such as imaging and environment reconstruction can be carried out in buildings. Refer to Figure 2 , Figure 2 is a schematic diagram of a scenario for environmental sensing on a railway track provided by the embodiments of the present application. In Figure 2Among them, by performing environmental perception in the sensing area where the railway track is located, all-weather detection of foreign object intrusion around high-speed trains can be achieved. For example, through environmental perception, it is detected that someone has entered area 201. Exemplarily, sensing devices can be deployed within the sensing area where the railway track is located to achieve comprehensive monitoring of the environment around high-speed trains. For example, the deployed sensing devices include: sensing devices such as lidar, millimeter-wave radar, and cameras. Install lidar sensors on both sides of the track, and by emitting laser beams and receiving reflected signals, obstacles and foreign objects around the track can be detected in real time. Utilizing the characteristics of high precision and strong penetration of millimeter-wave radar, foreign object intrusion can be accurately detected even under harsh weather conditions such as rain and snow. Install high-definition cameras at key positions, and through image recognition technology, analyze video data to identify abnormal objects and behaviors. By reasonably setting the sensing area and deploying advanced sensing devices, all-weather monitoring of the environment around high-speed trains and detection of foreign object intrusion can be achieved, which not only improves the safety of railway transportation but also provides strong support for emergency response and maintenance management.

[0120] For the driving scenario, tasks such as posture recognition of the driving device, in-vehicle behavior perception, anti-collision sensing, traffic management, and pedestrian detection can be performed through perception. Refer to Figure 3 , Figure 3 is a schematic diagram of a vehicle-to-everything (V2X) communication and sensing system architecture based on a 5G network provided by an embodiment of the present application. It involves direct communication between vehicles, communication between vehicles and infrastructure, and interaction with the cloud service platform. Through this multi-level communication and sensing mechanism, safer and more efficient intelligent transportation management can be achieved. In Figure 3 each vehicle can communicate with the Radio Access Network (RAN) through wireless signals. The RAN transmits the collected data to the Service Gateway Controller (SGC). The SGC further processes the data and interacts with third-party applications. Third-party applications can be various vehicle-to-everything (V2X)-based application programs. For example, the application program is a navigation application that provides the driver with the optimal route suggestion according to the real-time traffic conditions, a traffic management application for monitoring and managing urban traffic flow and optimizing the signal light control strategy, etc. Vehicles are equipped with on-vehicle units for sending and receiving Sensing Signals to measure the perception data of the surrounding environment. Vehicles can achieve vehicle-to-vehicle (V2V) communication through these sensing signals, thereby improving driving safety and efficiency. In Figure 3In it, the RAN covers the entire road area through multiple base stations, ensuring that vehicles can maintain a continuous communication connection during movement. After receiving the sensing signals sent by the vehicles, the base stations converge and forward them to the SGC.

[0121] For the disaster weather sensing scenario, all-weather monitoring of major disasters such as floods and tsunamis can be achieved through sensing. Refer to Figure 4 , Figure 4 which is a schematic diagram of a weather sensing scenario provided by an embodiment of this application. In Figure 4 it, there are two base stations, which are located on the Figure 4 left and right sides respectively. The base stations detect the surrounding environment by transmitting sensing signals and receiving reflected signals. The sensing signal is emitted by the base station and propagates into the environment. The reflected signal, when the sensing signal encounters an obstacle or object, will be reflected back to the base station. Under disaster weather conditions, due to environmental changes (such as water accumulation, muddy roads, etc.), the intensity and path of the reflected signal will also change.

[0122] This change can be used to infer the environmental state by analyzing the characteristics of the reflected signal (such as amplitude, phase, time delay, etc.). For example, by analyzing the attenuation degree and scattering characteristics of the signals between the base stations, the rainfall intensity and distribution can be monitored in real time. Under disaster weather conditions such as heavy rain and floods, dangerous situations such as road water accumulation and landslides can be monitored through the changes in the sensing signals.

[0123] The embodiments of this application can be applied to the Figure 1 shown mobile communication system, and the priority determination method can be applied to the core network devices in the Figure 1 core network 200. The following specifically introduces the priority determination method in the embodiments of this application: Figure 5 which is a schematic flowchart of a priority determination method provided by an embodiment of this application.

[0124] Exemplarily, as Figure 5 shown, the priority determination method for this sensing area includes: S501: Obtain the sensing results corresponding to N sensing areas; where the N sensing areas are obtained by dividing the sensing range of at least one sensing device, and N is an integer greater than or equal to 2.

[0125] S502: Obtain the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; where the priorities are used for resource allocation for the sensing services corresponding to the N sensing areas.

[0126] In Figure 5 In the illustrated embodiment, the concept of priority is proposed for the sensing regions. According to the sensing results corresponding to N sensing regions, the priorities of the N sensing regions are determined. The change of the sensing results will affect the priorities. As the sensing results corresponding to the sensing regions change, the priority of the sensing region may also change, that is, the priority can be dynamically determined according to the sensing results, rather than being fixed. The change of the priorities of the N sensing regions will affect the resource allocation of the sensing services corresponding to the N sensing regions, which is beneficial to improving the flexibility of sensing resource allocation and adapting to the diverse sensing requirements in the communication-sensing integrated scenario.

[0127] Next, Figure 5 the specific implementation manners of the steps in the illustrated embodiment are described: In S501, the core network device obtains the sensing results corresponding to N sensing regions. The sensing result corresponding to each sensing region includes but is not limited to the sensing targets, sensing target features, sensing environment features, etc. within the sensing region. The sensing environment features may include features related to the environment such as weather, temperature, humidity, season, etc. The sensing target features may include features related to the sensing targets such as the number, shape, position, speed, etc. of the sensing targets.

[0128] Exemplarily, the sensing device can measure the sensing data to obtain the sensing data. Then the sensing device transmits the sensing data to the core network device. Correspondingly, the core network device receives the sensing data uploaded by each sensing device, and then the core network device parses and processes the received sensing data to obtain the sensing results corresponding to N sensing regions.

[0129] Exemplarily, the sensing device includes at least one of the following: non-3GPP device, UE, base station. As described above, non-3GPP devices, UEs, and base stations can all perform sensing tasks, so they can all be used as sensing devices. In this example, at the data collection end, i.e., the core network side, the sensing data of three types of sensing devices can be collected, expanding the data range collected by the core network and facilitating the next decision-making of the core network device. The next decision-making can be understood as the initialization / updating of priorities in S502.

[0130] Optionally, the number of non-3GPP devices uploading sensing data to the core network can be one or more, the number of UEs uploading sensing data to the core network can be one or more, and the number of base stations uploading sensing data to the core network can also be one or more. In the embodiments of the present application, the number of sensing devices is not limited.

[0131] Next, taking the sensing device as a non-3GPP device, UE, and base station as examples respectively, the measurement of the above sensing data is introduced: Non-3GPP device measures sensing data: The non-3GPP device collects environmental data through sensors or signal processing modules (such as radar, cameras, etc.), encapsulates the data in the format required by the network, and obtains the sensing data of the non-3GPP device.

[0132] UE measures the sensing data: UE's measurement of sensing data includes the following task requests, data measurement, and preprocessing.

[0133] Task request: The sensing task of the UE can be triggered by the core network device or initiated by the UE itself. When the sensing task is triggered by the core network device, the core network device sends sensing service control information to the UE, including information such as sensing service type, sensing target, and sensing quantity. The UE configures sensing measurements according to the received sensing service control information, such as parameters like the waveform of the sensing signal and the subcarrier spacing.

[0134] Data measurement and preprocessing: The UE can obtain sensing measurement data by self-transmitting and self-receiving sensing signals, and perform data preprocessing according to its own capabilities to obtain processing results, such as information about the position, distance, speed, etc. of the sensing target.

[0135] Base station measures the sensing data: Multiple base stations send sensing signals (such as CSI-RS or SRS) to the sensing targets within their respective service ranges, receive the echo signals of the transmitted sensing signals for processing, and multiple base stations upload the data after their respective signal preprocessing to one of the base stations for information fusion. For example, one of the multiple base stations can be selected as the base station for information fusion, and after this base station performs information fusion, it can upload the fused sensing data to the core network device.

[0136] After introducing the measurement of sensing data, the following takes the non-3GPP device, UE, and base station as examples of sensing devices to introduce the upload of the above-mentioned sensing data: Process of non-3GPP device uploading sensing data: The non-3GPP device sends a reporting request to the network exposure function (NEF) network element, and the device identifier of the non-3GPP device can be carried in the reporting request.

[0137] After receiving the reporting request, the NEF network element checks the permissions of the non-3GPP device corresponding to the device identifier carried in the reporting request. If the permissions are legal, it allocates communication resources to the non-3GPP device and issues configurations.

[0138] The non-3GPP device uploads the sensing data to the core network device through the NEF interface according to the configuration issued by the NEF network element.

[0139] Procedure for the UE to upload sensing data: The UE is configured to perform environmental sensing operations on the sensing area and encapsulate the acquired sensing data. The sensing data includes, but is not limited to, the number of sensing targets, point cloud map information, sensing environment characteristics, etc. The UE encapsulates the sensing data in a preset standardized format and transmits it to the base station through the radio air interface. The base station receives the sensing data from the UE and performs integrity verification and outlier filtering operations on the received data to ensure the reliability and availability of the sensing data. Subsequently, the base station extracts key information from the sensing data, such as abnormal event information, statistical results, etc., and uploads the key information to the core network device so that the core network can perform further processing and decision-making based on a global perspective. In terms of the design of the decision-making mechanism, a hierarchical decision-making mechanism is adopted: for tasks with low latency requirements, such as local environment modeling, fast response control, etc., real-time processing is completed by the base station side; while for tasks with higher complexity, involving cross-base station cooperation or requiring global resource optimization, they are uniformly coordinated and processed by the core network, so as to achieve efficient allocation of sensing tasks and reasonable utilization of network resources.

[0140] Procedure for the base station to upload sensing data: Based on network configuration and service requirements, the base station selects an appropriate transmission path and uploads the sensing data to the core network device through the control plane, such as the AMF network element. Alternatively, the sensing data is uploaded to the core network device through the user plane, such as the user plane function (UPF) network element.

[0141] As described above, the UE uploads the sensing data to the core network device through the base station. Therefore, the sensing data uploaded by the base station to the core network device includes the UE's sensing data and the base station's own sensing data.

[0142] After receiving the sensing data from non-3GPP devices, UEs, and base stations, the core network device parses and processes the received sensing data to obtain sensing results. The core network device includes a sensing function SF network element, which can fuse and analyze the data from multiple sensing devices to generate comprehensive and accurate sensing results.

[0143] In S502, the core network device has obtained the sensing results corresponding to N sensing regions, and the sensing result is one of the influencing factors for the priority of the sensing region. The core network device can obtain the priorities of the N sensing regions according to the preset priority update rule and the sensing results. The priority of the sensing region indicates which sensing regions should be preferentially sensed, that is, which sensing services corresponding to the sensing regions should be preferentially allocated sensing resources. The advantage of setting priorities for sensing regions is that it can quickly identify and mark the targets located in the high-priority sensing regions as high-priority targets, so as to achieve the preferential sensing and processing of these high-priority targets to meet the diverse sensing requirements in the communication-sensing integration scenario.

[0144] The sensing service corresponding to the sensing region is related to the sensing requirements of the sensing region. Different sensing regions have different sensing requirements, so corresponding sensing services can be designed to meet these requirements.

[0145] For example, for the sensing region where the railway track is located, the sensing requirement is: all-weather detection of foreign objects intrusion around high-speed trains. Then the corresponding sensing service can be: real-time monitoring of foreign objects intrusion around high-speed trains and sending an alarm when an anomaly is found. The foreign objects around the high-speed train can be understood as specific sensing targets.

[0146] For example, for the sensing region where the river is located, the sensing requirement is: real-time acquisition of river flow velocity, water level and other information during the flood season. Then the corresponding sensing service can be: continuously monitoring the changes in river flow velocity and water level during the flood season and providing early warning services to prevent floods.

[0147] For example, for the sensing region where the airport runway is located, the sensing requirement is: ensuring that there are no foreign objects on the runway and guaranteeing the safety of aircraft takeoff and landing. Then the corresponding sensing service is: continuously scanning the runway surface and its surrounding areas, detecting any foreign objects that may affect flight safety, and quickly clearing the obstacles.

[0148] For another example, for the sensing region where the forest fire prevention area is located, the sensing requirement is: early detection of fire signs for a quick response. Then the corresponding sensing service is: using technologies such as thermal imaging and smoke sensors to continuously monitor a large area of forest area, and immediately sounding an alarm and starting the emergency response mechanism once abnormal temperature rise or smoke is detected.

[0149] As can be seen from the above examples, according to the specific sensing requirements of the sensing regions, highly targeted sensing services can be defined. Each sensing service aims to solve specific problems and meet specific sensing requirements. Since the total amount of resources to be allocated is usually limited, when allocating sensing resources, considering the priorities of each sensing region is beneficial to improving the flexibility of sensing resource allocation, enabling the sensing services corresponding to the sensing regions with high priorities to be preferentially allocated sensing resources and ensuring that the sensing services corresponding to the sensing regions with high priorities are processed in a timely manner.

[0150] In one embodiment, when the priorities of N sensing regions have not been set, obtaining the priorities of the N sensing regions based on the sensing results corresponding to the N sensing regions can be understood as: initializing the priorities of the N sensing regions based on the sensing results corresponding to the N sensing regions to provide a basis for subsequent priority updates.

[0151] In another embodiment, when the priorities of N sensing regions already exist, obtaining the priorities of the N sensing regions based on the sensing results corresponding to the N sensing regions can be understood as: updating the priorities of the N sensing regions based on the sensing results corresponding to the N sensing regions to adapt to changes in the sensing results.

[0152] Optionally, both the above-mentioned initialization of obtaining priorities and the update of obtaining priorities can be understood as determining priorities. That is to say, based on the sensing results corresponding to the N sensing regions, the priorities of the N sensing regions are determined.

[0153] Optionally, default priorities can be pre-configured for the N sensing regions. For example, the priorities of all sensing regions are the same by default. Subsequently, the core network device updates the priorities of each sensing region according to the latest sensing results corresponding to each sensing region and in combination with the preset priority update rules, thereby realizing the differential scheduling and refined management of sensing resources.

[0154] In a possible implementation, the sensing result corresponding to each sensing region includes: the sensing target detected in this sensing region. When determining the priorities of the N sensing regions, the priorities of the sensing targets detected in the N sensing regions can be considered. The higher the priority of the sensing target, the higher the priority of the sensing region where the sensing target is located can be. The priorities of different types of sensing targets can be preset. For example, the priority of sensing target a is higher than that of sensing target b. Then, when sensing target a is in sensing region 1 and sensing target b is in sensing region 2, the priority of sensing region 1 is higher than that of sensing region 2. When sensing target a is in sensing region 2 and sensing target b is in sensing region 1, the priority of sensing region 1 is lower than that of sensing region 2.

[0155] In a possible implementation, the perception result corresponding to each perception area includes: the perception environmental characteristics corresponding to the perception area. The perception environmental characteristics are used to characterize the state information of the physical environment where the perception area is located, such as weather conditions, lighting conditions, temperature, humidity, etc. When updating or initializing the priorities of N perception areas, the core network device is configured to comprehensively evaluate the severity of the environment where each perception area is located based on the perception environmental characteristics corresponding to each perception area, and dynamically determine the priorities of the corresponding perception areas according to the evaluation results.

[0156] Specifically, the priority of the perception area is positively correlated with the severity of the environment characterized by its perception environmental characteristics. That is: when the perception environmental characteristics of a certain perception area indicate that it is in relatively harsh environmental conditions, the priority of this perception area is correspondingly increased; otherwise, its priority can be appropriately reduced. Through this method, limited perception resources can be preferentially allocated to areas with more complex perception environments and higher risks, thereby improving the response efficiency and security guarantee ability of the overall perception system. For example, if the perception environmental characteristics of a certain perception area include adverse factors such as heavy rain, heavy fog, or strong light interference, it indicates that this perception area should be given a higher priority to ensure timely acquisition of high-quality perception data and adopt corresponding control strategies. Such a control strategy can be, for example: sending environmental anomaly warning information to relevant terminal devices or management platforms to cope with possible security risks.

[0157] Exemplarily, in order to quantify the influence of the perception result on the priority of the perception area, the priority scores corresponding to N perception areas can be determined according to the perception results corresponding to the N perception areas, and the priorities of the N perception areas can be determined according to the priority scores corresponding to the N perception areas; among them, the priority score is positively correlated with the priority. That is to say, the higher the priority score of a perception area, the higher its corresponding priority. Optionally, if only considering the influence of the perception result on the priority of the perception area, the priority score corresponding to each perception area can be the first target score corresponding to the perception result.

[0158] Exemplarily, for each perception area, its corresponding perception result includes multiple perception environmental characteristics, and the first target score is obtained by weighting the scores corresponding to each perception environmental characteristic among the multiple perception environmental characteristics with the coefficients corresponding to each perception environmental characteristic.

[0159] Among them, the coefficients corresponding to each perception environmental characteristic can be set according to experience, which characterize the influence degree of each perception environmental characteristic on the priority of the perception area, and this coefficient can also be called weight.

[0160] Specifically, for each perceived environmental feature, multiply the score corresponding to the perceived environmental feature by the coefficient corresponding to the perceived environmental feature to obtain the weighted score corresponding to the perceived environmental feature. Then, add up the weighted scores corresponding to multiple perceived environmental features to obtain the first target score corresponding to the perception result.

[0161] Exemplarily, for any one perceived area, the multiple perceived environmental features corresponding to the perceived area include: weather, temperature, and humidity. Assume that the coefficients corresponding to weather, temperature, and humidity are: a1, a2, and a3 respectively, and the scores corresponding to weather, temperature, and humidity are s1, s2, and s3 respectively. Then, the first target score corresponding to the perceived area is: a1×s1 + a2×s2 + a3×s3.

[0162] In this implementation manner, through the above quantization scoring mechanism, various types of perceived environmental features can be uniformly converted into comparable and computable scores, which helps to more accurately reflect the influence of the actual environment of each perceived area on the priority, thereby providing an objective basis for the dynamic update of the priority of the perceived area and enhancing the scientificity and rationality of the priority update.

[0163] Exemplarily, the score corresponding to each perceived environmental feature is positively correlated with the severity of the environment characterized by each perceived environmental feature. That is to say, for each perceived environmental feature, the higher the severity of the environment characterized by the perceived environmental feature, the higher the score corresponding to the perceived environmental feature.

[0164] In the embodiments of the present application, the perceived environmental features of the perceived area can be divided into two categories: continuous perceived environmental features and discrete perceived environmental features. Among them, the continuous perceived environmental feature refers to an environmental feature whose parameter value continuously changes within a certain range, such as temperature, humidity, light intensity, etc. The discrete perceived environmental feature refers to an environmental feature whose parameter value is a finite number of discrete values, such as weather type, visibility level, etc. In order to facilitate the subsequent quantitative evaluation of the priority of the perceived area, different scoring quantization methods are adopted for different types of perceived environmental features. The following introduces two different scoring quantization methods: For continuous perceived environmental features, the original parameter value can be directly normalized, mapped to a preset scoring interval such as [0, 1], and the normalized value is used as the score corresponding to the perceived environmental feature. For example, linearly map the temperature value from the Celsius range [-20°C, 60°C] to the scoring interval [0, 1] to reflect its influence degree on the priority of the perceived area.

[0165] For discrete perceived environmental features, first, numerical mapping can be performed on each value of the feature according to a preset classification and assignment rule, and then the mapped numerical values are normalized to obtain corresponding scores. For example, for the discrete perceived environmental feature of weather, the following classification and assignment rule can be set: sunny day → 1, rainy day → 2, snowy day → 3, foggy day → 4; then these integer values are normalized to a preset score range such as [0, 1] to represent the influence degree of different weather conditions on the priority.

[0166] It should be noted that the above-mentioned preset score range only takes [0, 1] as an example. In specific implementation, the score range can also be set to other ranges, such as [0, 100], and this embodiment does not limit this.

[0167] Through the above quantitative scoring mechanism, the more severe the perceived area of the environment, the higher the priority score will be reflected in the scoring result. Thus, in the subsequent process of allocating sensing resources, sensing resources can be preferentially allocated to the perceived areas with higher priority scores and more complex environments, so as to improve the adaptability to complex environments and the timeliness and reliability of obtaining sensing data. By preferentially sensing the areas with severe environments, potential risk factors can be discovered earlier, thereby providing high-quality data support for subsequent decision-making and enhancing the safety guarantee ability and service quality level of the system in key scenarios.

[0168] The above mainly introduces the influence of the sensing result on the priority of the sensing area. In specific implementation, there may also be other factors affecting the priority level. For example, the sensing service requirements corresponding to the sensing area can also be used as one of the factors affecting the priority. Based on this, in one embodiment, obtaining the priorities of N sensing areas according to the sensing results corresponding to the N sensing areas includes: obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas and the sensing service requirements. In this embodiment, the factors of both the sensing result and the sensing service requirements can be considered simultaneously to affect the priorities of the N sensing areas.

[0169] Among them, the perceived service requirements corresponding to the perception area refer to the requirements of the perceived service corresponding to the perception area. The perceived service requirements include parameters such as QoS parameters, perception accuracy parameters, and latency tolerance parameters, which are used to characterize the service requirements. The perceived service corresponding to the perception area can be understood as the perceived service carried by the perception area. In this implementation method, the priority of the perception area is not only affected by the corresponding perception result, but may also be affected by the perceived service requirements of the perception area. Therefore, the core network device can be configured to jointly evaluate and update the priorities of each perception area according to the perception results and perceived service requirements respectively corresponding to N perception areas. For example, for a certain perception area, if its perception environment is relatively harsh, the perceived service it carries has a high perception accuracy or a low latency tolerance, then this perception area will be given a higher priority to ensure that key tasks are guaranteed first.

[0170] In this embodiment, on the basis of the perception result, the perceived service requirement is further introduced as a factor for determining the priority, so that the priorities of each perception area can be comprehensively evaluated and dynamically adjusted under the dual drive of the perception result and the perceived service requirement. Through this implementation method, the priority of the perception area can not only reflect the complexity of the environment and the potential risk level where the perception area is located, but also reflect the service requirements of the perceived service it carries. For example, for a perception area with a harsh perception environment or high perceived service requirements, a higher priority can be given to it, so as to achieve differential scheduling and refined management of perception resources, improve the perception response ability and service quality guarantee level under multi-service and multi-environment conditions, and further enhance the adaptability and reliability of the communication perception integrated system in complex application scenarios.

[0171] Exemplarily, in order to quantify the influence of the perception result and the perceived service requirement on the priority of the perception area, the priority scores corresponding to N perception areas can be determined according to the perception results and perceived service requirements corresponding to N perception areas; according to the priority scores corresponding to N perception areas, the priorities of N perception areas are obtained; among them, the priority score is positively correlated with the priority.

[0172] Exemplarily, the N sensing regions include a second sensing region, and the second sensing region is any one of the N sensing regions. Determining the priority scores corresponding to the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements includes: determining a first target score corresponding to the second sensing region according to the sensing result corresponding to the second sensing region; determining a second target score corresponding to the sensing region according to the sensing service requirements of the second sensing region; and determining the priority score corresponding to the second sensing region according to the first target score and the second target score. Optionally, if the impacts of the two factors of the sensing result and the sensing service requirements on the priority of the sensing region are considered, the priority score corresponding to each sensing region may be the sum of the first target score and the second target score. The determination method of the first target score may refer to the above description, and the determination method of the second target score will be described below: Exemplarily, the sensing service requirements include multiple service requirement parameters, and the second target score is obtained by weighting the scores corresponding to the respective service requirement parameters among the multiple service requirement parameters by the coefficients corresponding to the respective service requirement parameters.

[0173] Among them, the coefficients corresponding to the respective service requirement parameters can be set according to experience, which represent the influence degrees of the respective service requirement parameters on the priority of the sensing region, and this coefficient can also be called a weight.

[0174] Specifically, for each service requirement parameter, multiply the score corresponding to the service requirement parameter by the coefficient corresponding to the service requirement parameter to obtain the weighted score corresponding to the service requirement parameter. Then, add up the weighted scores corresponding to the multiple service requirement parameters respectively to obtain the second target score corresponding to the sensing service requirements.

[0175] Exemplarily, for any one sensing region, the multiple service requirement parameters corresponding to the sensing region include: a QoS parameter and a sensing accuracy parameter. Assume that the coefficients corresponding to the QoS parameter and the sensing accuracy parameter are b1 and b2 respectively, and the scores corresponding to the QoS parameter and the sensing accuracy parameter are s3 and s4 respectively. Then, the second target score corresponding to the sensing region is: b1×s3 + b2×s4.

[0176] In this implementation manner, through the above quantization scoring mechanism, each service requirement parameter can be uniformly converted into a comparable and computable score, which helps to more intuitively reflect the influence of the sensing service requirements of each sensing region on the priority, thereby providing an objective basis for the dynamic update of the priority of the sensing region and enhancing the scientificity and rationality of the priority update.

[0177] Exemplarily, multiple service requirement parameters include QoS parameters, and the score corresponding to the QoS parameter is negatively correlated with the level of the QoS parameter (Qos Class Identifier, QCI). That is to say, for the QoS parameter, the larger the QCI, the lower the score corresponding to the QoS parameter.

[0178] Specifically, QCI is a key parameter used to distinguish the priority and performance requirements of different service flows in a communication system (such as an LTE or 5G network). Its value range is usually from 1 to 9 or higher, and each value corresponds to specific transmission characteristics, including maximum delay, acceptable packet loss rate, guaranteed bit rate, etc. The smaller the QCI value, the higher the service quality requirement for this service; conversely, the larger the QCI value, the lower the service quality requirement. Therefore, when scoring the QoS parameter, it can be set that the larger the QCI value, the lower the corresponding score, so as to reflect the priority guarantee requirements of the perceived service in the resource scheduling process.

[0179] Exemplarily, mapping processing can be performed on QCI in advance. For example: QCI = 1 → the mapped value is 1.0; QCI = 5 → the mapped value is 0.6; QCI = 9 → the mapped value is 0.2. Subsequently, the mapped value is normalized, and the normalized value is used as the score corresponding to the QoS parameter. Alternatively, when performing mapping processing on QCI, the QCI is directly mapped to a preset score interval to obtain a mapped value, and this mapped value can be directly used as the score corresponding to the QoS parameter. Among them, QCI and the mapping are in a negative correlation relationship, and the score interval can be preset, such as [0, 1] or [0, 100]. This embodiment does not make any limitations in this regard.

[0180] Through the above scoring mechanism, the perceived area corresponding to the perceived service with a lower QCI value shows a higher priority score in the scoring result, thus reasonably quantifying the impact of the QCI value on the priority of the perceived area. This solution helps to preferentially allocate limited sensing resources to the perceived areas with more complex perceived environments or higher service quality requirements for services during the resource allocation process, improve the service guarantee ability for high-quality services, and meet the perceived and communication service quality requirements in diverse scenarios.

[0181] The above mainly introduced the influence of perception results and perception service requirements on the priority of perception regions. In specific implementation, there may also be other influencing factors affecting the priority, such as spatio-temporal feature information and / or regional attribute information. Based on this, in one embodiment, obtaining the priorities of N perception regions according to the perception results and perception service requirements corresponding to the N perception regions includes: obtaining the priorities of the N perception regions according to the perception results, perception service requirements and target information corresponding to the N perception regions; wherein, the target information includes the spatio-temporal feature information and / or regional attribute information of the N perception regions; the regional attribute information includes the urgency level of the perception region and / or the preset importance level of the perception region. In this embodiment, the influence of perception results, perception service requirements, and target information (spatio-temporal feature information and / or regional attribute information) on the priorities of the N perception regions can be considered simultaneously.

[0182] For each perception region, the spatio-temporal feature information of the perception region includes time information and the location information of the space where the perception region is located. Among them, the time information is used to represent the time dimension information corresponding to the priority update operation, including but not limited to time points or time periods. A time point can be understood as the specific moment when the priority update operation is executed, and a time period can be used to represent a continuous time interval covered by the priority update operation, which can be used to support periodic or event-driven priority adjustment mechanisms. The location information is used to represent the geographical location information of the perception region in the physical space, such as the geographical coordinates of the perception region, the road segment it belongs to, traffic nodes, etc.

[0183] For each perception region, the regional attribute information of the perception region is used to represent the intrinsic characteristics of the perception region, at least including the urgency level of the perception region and / or the preset importance level.

[0184] The urgency level is used to indicate whether a preset emergency event has occurred in the perception region and the urgency level of the preset emergency event. Or, the urgency level is used to indicate whether the perception region is experiencing or may face a preset type of emergency event, and the severity of the emergency event. For example, if an emergency event such as a fire, traffic accident, natural disaster, or public safety event occurs in a certain perception region, the urgency level of the perception region increases. Correspondingly, the priority of the perception region can be dynamically increased according to the emergency event and its severity to achieve priority monitoring and resource guarantee for the perception region. When the emergency event in the perception region is lifted or the urgency level decreases, the priority of the perception region can also be dynamically decreased, thereby releasing perception resources to support the perception task scheduling of other high-priority regions and improving the overall resource utilization efficiency and response flexibility.

[0185] The preset importance level refers to the importance level preset according to the functional positioning or actual requirements of the sensing area in the application scenario. For example, public places such as hospitals, schools, airports, transportation hubs, and government agencies usually have a relatively high importance level. Sensing areas with a higher importance level should be assigned a higher priority to achieve priority monitoring and resource guarantee for sensing areas with a higher importance level, and to ensure the safety and stability of sensing areas with a higher importance level.

[0186] In the above embodiments, based on the sensing results and sensing service requirements, spatio-temporal feature information and / or regional attribute information are further introduced as influencing factors for priority evaluation. Thus, under the joint drive of multi-dimensional factors such as sensing results, sensing service requirements, spatio-temporal feature information, and / or regional attribute information, the priorities of each sensing area can be comprehensively evaluated and dynamically adjusted. By introducing the above-mentioned multi-dimensional information, the complexity of the environment where the sensing area is located, the importance of the sensing task, the time sensitivity of the event occurrence, and the attributes of the area itself can be more comprehensively reflected, thereby improving the rationality and scientificity of priority evaluation, and further contributing to the flexible allocation of sensing resources to meet the diverse sensing needs of different sensing areas.

[0187] Exemplarily, in order to quantify the influence of sensing results, sensing service requirements, and target information on the priority of the sensing area, obtaining the priorities of the N sensing areas according to the sensing results, sensing service requirements, and target information corresponding to the N sensing areas includes: determining the priority scores corresponding to the N sensing areas according to the sensing results, sensing service requirements, and target information corresponding to the N sensing areas; obtaining the priorities of the N sensing areas according to the priority scores corresponding to the N sensing areas; wherein, the priority score is positively correlated with the priority. The higher the priority score of a sensing area, the higher its priority.

[0188] Specifically, the sensing results include, but are not limited to, information reflecting the physical state of the sensing area such as the number of sensing targets and the characteristics of the sensing environment. The sensing service requirements include parameters related to the performance of the sensing service such as the quality of service requirement QoS and the sensing accuracy. The target information includes auxiliary information such as spatio-temporal feature information and / or regional attribute information of the sensing area that helps to evaluate the sensing importance. For each sensing area, according to the preset scoring method, combined with the sensing results, sensing service requirements, and target information corresponding to the sensing area, its corresponding priority score can be determined. For example, if the sensing results of a certain sensing area indicate that its sensing environment is harsh, the sensing targets are dense, or its sensing service requirements have a high requirement for the quality of service, or its target information indicates that the sensing area has a high monitoring necessity (such as being on a traffic artery, an emergency event occurring, etc.), then the priority score corresponding to this sensing area will be increased accordingly to reflect its higher sensing priority.

[0189] In this implementation manner, through the above-mentioned quantization scoring mechanism, the perception results, perception service requirements, and target information can be uniformly converted into comparable and computable priority scores, which helps to intuitively reflect the priorities of each perception area through the priority scores, provides an objective basis for determining the priorities of the perception areas, and improves the scientificity and rationality of the determined priorities.

[0190] Exemplarily, the N perception areas include a first perception area, and the first perception area is any one of the N perception areas. Determining the priority scores corresponding to the N perception areas according to the perception results, perception service requirements, and target information corresponding to the N perception areas includes the following S11 to S14: S11: Determine a first target score corresponding to the first perception area according to the perception result corresponding to the first perception area.

[0191] The first target score can be understood as the score corresponding to the perception result, which reflects the influence of the perception result of the first perception area on the priority. Among them, the perception result includes multiple perception environment features, and the first target score is obtained by weighting the scores corresponding to each perception environment feature and the coefficients corresponding to each perception environment feature among the multiple perception environment features. The score corresponding to each perception environment feature is positively correlated with the severity of the environment characterized by each perception environment feature. The specific determination method of the first target score has been introduced above, and reference can be made to the relevant content above. To avoid repetition, it will not be elaborated here.

[0192] S12: Determine a second target score corresponding to the first perception area according to the perception service requirement of the first perception area. The second target score can be understood as the score corresponding to the perception service requirement, which reflects the influence of the perception service requirement of the first perception area on the priority. Among them, the perception service requirement includes multiple service requirement parameters, and the second target score is obtained by weighting the scores corresponding to each service requirement parameter and the coefficients corresponding to each service requirement parameter among the multiple service requirement parameters. The multiple service requirement parameters include QoS parameters, and the score corresponding to the QoS parameter is negatively correlated with the level of the QoS parameter. The specific determination method of the second target score has been introduced above, and reference can be made to the relevant content above. To avoid repetition, it will not be elaborated here.

[0193] S13: Determine a third target score corresponding to the first perception area according to the target information of the first perception area.

[0194] The third target score can be understood as the score corresponding to the target information, which reflects the influence of the target information of the first perception area on the priority. Among them, the target information includes spatio-temporal feature information and / or regional attribute information, and the third target score is obtained based on the fourth target score corresponding to the spatio-temporal feature information and / or the fifth target score corresponding to the regional attribute information.

[0195] When the target information is spatio-temporal feature information, the third target score is equal to the fourth target score corresponding to the spatio-temporal feature information. When the target information is regional attribute information, the third target score is equal to the fifth target score corresponding to the regional attribute information. When the target information includes spatio-temporal feature information and regional attribute information, the third target score is equal to the sum of the fourth target score and the fifth target score, or the third target score is equal to the weighted sum of the fourth target score and the fifth target score. The following specifically introduces the determination methods of the fourth target score and the fifth target score: Exemplarily, the fourth target score reflects the influence of the spatio-temporal feature information of the first sensing area on the priority. The spatio-temporal feature information includes time information and the position information of the space where the first sensing area is located. The fourth target score is obtained by weighting the product of the score corresponding to the time information and the coefficient corresponding to the time information, and the product of the score corresponding to the position information and the coefficient corresponding to the position information.

[0196] Among them, the coefficient corresponding to the time information and the coefficient corresponding to the position information can both be set according to experience, respectively representing the influence degree of the time information and the position information on the priority of the sensing area. This coefficient can also be called a weight.

[0197] Exemplarily, assume that the coefficients corresponding to the time information and the position information are c1 and a2 respectively, and the scores corresponding to the time information and the position information are s5 and s6 respectively. Then the fourth target score corresponding to this first sensing area is: c1×s5 + c2×s6.

[0198] In this implementation manner, through the above quantitative scoring mechanism, the time information and the position information can be uniformly converted into comparable and computable scores, which helps to more intuitively reflect the influence of the spatio-temporal feature information of each sensing area on the priority, thereby providing an objective basis for the dynamic update of the priority of the sensing area and enhancing the scientificity and rationality of the priority update.

[0199] Exemplarily, the score corresponding to the time information is determined based on the time period in which the time information is located. When the time period in which the time information is located is the preset peak time period corresponding to the first sensing area, the score corresponding to the time information is the first preset score; when the time period in which the time information is located is the preset flat peak time period corresponding to the first sensing area, the score corresponding to the time information is the second preset score; when the time period in which the time information is located is the preset low valley time period corresponding to the first sensing area, the score corresponding to the time information is the third preset score.

[0200] Among them, the first preset score is greater than the second preset score, and the second preset score is greater than the third preset score. These three preset scores can take values within a preset score range. If the score range is [0, 1], the first preset score can be 0.9, the second preset score can be 0.5, and the third preset score can be 0.1. If the score range is [0, 100], the first preset score can be 90, the second preset score can be 50, and the third preset score can be 10. In this example, the values of these three preset scores are just for illustration, and this embodiment does not limit this.

[0201] Combined with the above description, it can be known that the time information can be specifically a time point or a time period. Therefore, the time period in which the time information is located can be determined. Determine the time period to which the time information belongs according to the time information, and determine the score corresponding to the time information according to whether the time period belongs to the preset peak time period, the preset flat peak time period or the preset low peak time period corresponding to the first sensing area.

[0202] The preset peak time period, the preset flat peak time period and the preset low peak time period corresponding to the sensing area can be flexibly configured based on the actual service requirements of the sensing area, and there may be differences in the preset peak time period, the preset flat peak time period and the preset low peak time period corresponding to different sensing areas. For example, the preset peak time period, the preset flat peak time period and the preset low peak time period corresponding to sensing area 1 are morning, noon, and evening in sequence, while the preset peak time period, the preset flat peak time period and the preset low peak time period corresponding to sensing area 2 are evening, noon, and morning in sequence.

[0203] For example, assume that the peak time period of sensing area 1 can be set as the morning commuting time period (such as 7:00 to 9:00), and the peak time period of sensing area 2 can be set as the night time period (such as 21:00 to 23:00). In this case, if the time information is 8:00 in the morning, the score corresponding to the time information of sensing area 1 is higher than the score corresponding to the time information of sensing area 2. Thus, at 8:00 in the morning, the priority of sensing area 1 may be higher than that of sensing area 2. If the time information is 22:00 at night, the score corresponding to the time information of sensing area 1 is lower than the score corresponding to the time information of sensing area 2. Thus, at 22:00 at night, the priority of sensing area 1 may be lower than that of sensing area 2. That is to say, for the same sensing area, its priority at different times may change dynamically.

[0204] Setting the score corresponding to the preset peak period to a higher value is based on the difference in the intensity of perception requirements shown by the perception area at different times. During the peak period, the perception area usually faces higher traffic flow, more complex environmental changes, or more critical business activities, resulting in a significant increase in the importance and urgency of its perception tasks. Therefore, by presetting a higher score for the peak period, the priority of the perception area during the peak period can be enhanced, so that high-priority perception areas during the peak period can be given key guarantees during the subsequent resource allocation process. Correspondingly, since the perception demand is low during the off-peak period, its score can be appropriately reduced to release perception resources for other high-priority areas to use.

[0205] In the above embodiment, for the perception area in the preset peak period, its corresponding score is higher, making it in a relatively priority position in resource allocation. For the perception area in the off-peak period, it correspondingly obtains a lower score, making it in a relatively secondary position in resource allocation. Determining the score corresponding to the time information based on whether it is in the peak period, off-peak period, or flat-peak period is beneficial to improving the resource utilization efficiency and the perception service quality guarantee ability in a dynamic time-varying environment.

[0206] Exemplarily, the score corresponding to the location information of the first perception area is negatively correlated with the target distance, where the target distance is the distance between the first perception area and the preset perception area.

[0207] Among them, the preset perception area is a set important area or key area, such as public areas with high monitoring requirements or security guarantee requirements, such as hospitals, schools, airports, transportation hubs, government agencies, etc. The target distance is the spatial distance between the first perception area and the preset perception area. Optionally, the target distance can be the distance between the center of the first perception area and the center of the preset perception area.

[0208] In an optional score implementation method, the reciprocal of the target distance can be used as the score corresponding to the location information of the first perception area. Or, normalize the reciprocal of the target distance to obtain a score under a unified dimension, so as to be more suitable for subsequent comprehensive priority evaluation.

[0209] Through the above scoring mechanism, the closer the first perception area is to the preset perception area, the higher the score corresponding to the location information it obtains, making it in a relatively priority position in resource allocation compared to other perception areas that are farther away from the preset perception area. This is beneficial to preferentially allocating limited perception resources to the perception areas adjacent to the preset perception area, and improving the perception response ability and security guarantee level of the surrounding environment of key areas.

[0210] Exemplarily, the above-mentioned fifth target score reflects the influence of the regional attribute information of the first sensing area on the priority. The regional attribute information includes the urgency level and the preset importance level of the first sensing area. The fifth target score is obtained by weighting the product of the score corresponding to the urgency level and the coefficient corresponding to the urgency level, and the product of the score corresponding to the preset importance level and the coefficient corresponding to the preset importance level.

[0211] Among them, the coefficient corresponding to the urgency level and the coefficient corresponding to the preset importance level can both be set according to experience, respectively representing the influence degree of the urgency level and the preset importance level on the priority of the sensing area. This coefficient can also be called a weight.

[0212] Exemplarily, assume that the coefficients corresponding to the urgency level and the preset importance level are d1 and d2 respectively, and the scores corresponding to the urgency level and the preset importance level are s7 and s8 respectively. Then the fifth target score corresponding to this first sensing area is: d1×s7 + d2×s8.

[0213] In this implementation method, through the above-mentioned quantitative scoring mechanism, the urgency level and the preset importance level of the first sensing area can be uniformly converted into comparable and computable scores, which helps to more intuitively reflect the influence of the regional attribute information of each sensing area on the priority, thereby providing an objective basis for the dynamic update of the priority of the sensing area and enhancing the scientificity and rationality of the priority update.

[0214] Exemplarily, the score corresponding to the urgency level is positively correlated with the urgency level, and the score corresponding to the preset importance level is positively correlated with the preset importance level. That is to say, the higher the urgency level of the first sensing area, the higher the score corresponding to the urgency level. The higher the preset importance level of the first sensing area, the higher the score corresponding to the preset importance level.

[0215] The score corresponding to the urgency level of the first sensing area has a positive correlation with its urgency level. That is: the higher the urgency level, the higher the corresponding score. If no emergency occurs in this first sensing area, the urgency level score is 0. If an emergency occurs in this first sensing area, the corresponding emergency level is determined according to the event type and severity, and the score is generated through a hierarchical assignment method. For example, the urgency level is divided into levels 0 to 5, corresponding to the values 0 to 5 respectively, and these 0 to 5 are used as the scores corresponding to the urgency level after normalization processing.

[0216] Similarly, the score corresponding to the preset importance level of the first sensing area also has a positive correlation with its importance level. The preset importance level can be set according to factors such as the historical data, geographical location, and business attributes of the sensing area. The scoring method can refer to the urgency level scoring mechanism, and through numerical mapping and normalization processing of different levels, the score corresponding to the preset importance level is obtained.

[0217] In the above example, the higher the urgency or the preset importance level of the first sensing area, the higher the corresponding score, making it in a relatively preferential position in resource allocation compared to other sensing areas with a lower urgency or preset importance level. This is conducive to preferentially allocating limited sensing resources to the sensing areas with a high urgency or preset importance level, and improving the sensing response ability and security guarantee level for the sensing areas with a high urgency or preset importance level.

[0218] S14: Determine the priority score corresponding to the first sensing area according to the first target score, the second target score, and the third target score.

[0219] Specifically, the first target score, the second target score, and the third target score can be added together to obtain the priority score corresponding to the first sensing area. Alternatively, the average value of the first target score, the second target score, and the third target score can be calculated, and the average value can be used as the priority score corresponding to the first sensing area.

[0220] By taking each of the N sensing areas as the first sensing area respectively, and performing the above S11 to S14, the priority scores corresponding to each of the N sensing areas can be obtained.

[0221] In the embodiments of the present application, the first target score (the score corresponding to the sensing result), the second target score (the score corresponding to the sensing service requirement), and the third target score (the score corresponding to the target information) adopt a unified dimension standard during generation. For example, they are all normalized to a preset score interval (such as [0, 1]) to ensure comparability and fusion feasibility between different score dimensions. By mapping the influencing factors of different dimensions to the same dimension, in the subsequent calculation process of the priority score, multiple target scores can be weighted and fused, linearly combined, or comprehensively processed in other forms, so as to generate a comprehensive score result reflecting the overall priority level of the sensing area.

[0222] In one embodiment, the core network device establishes a comprehensive evaluation model, comprehensively considers influencing factors in multiple dimensions such as weather, temperature, humidity, location information, time information, urgency, importance level, and sensing service requirements, and performs priority ranking on the sensing areas. At the same time, the core network device dynamically adjusts the parameters of the comprehensive evaluation model according to the real-time situation, so as to achieve dynamic ranking of priorities. Among them, the parameters of the comprehensive evaluation model can be understood as the coefficients, that is, weights, corresponding to the influencing factors in multiple dimensions.

[0223] Exemplarily, the comprehensive evaluation model can adopt a hierarchical structure design, decomposing the problem of priority ranking of sensing areas into a three-layer structure: the target layer, the main criterion layer, and the sub-criterion layer. Figure 6 It is a schematic diagram of the hierarchical structure adopted by a comprehensive evaluation model provided by the embodiments of the present application.

[0224] Target layer, representing the perception area whose priority is to be evaluated. For example, each of the above N perception areas can be used as the perception area whose priority is to be evaluated.

[0225] Main criterion layer, representing the core factors affecting the priority. For example, the perception environment characteristics, spatio-temporal characteristic information, regional attribute information, and perception service requirements can be understood as 4 main criteria.

[0226] Sub-criterion layer, the sub-criteria under each main criterion. For example, the main criterion of perception environment characteristics includes sub-criteria such as weather, temperature, and humidity. The main criterion of spatio-temporal characteristic information includes two sub-criteria: time information and location information. The main criterion of regional attribute information includes two sub-criteria: urgency level and preset importance level. The main criterion of perception service requirements can include a sub-criterion such as QoS level (QCI value).

[0227] For each sub-criterion in the sub-criterion layer, the corresponding score of each sub-criterion can be determined through the score quantification method in Table 1 below. Specifically, it is quantified according to the influence of the parameters on the perception effect. The higher the score of the perception area, the higher its priority. The principle of score quantification can be: the worse the weather of the perception area, the higher the corresponding score. The higher / lower the temperature of the perception area, the higher the corresponding score. The higher / lower the humidity of the perception area, the higher the corresponding score. The closer the geographical location is to the key area, the higher the corresponding score of the perception area. The perception area with time information during the peak period has a higher corresponding score. The higher the urgency level of the perception area, the higher the corresponding score. The higher the importance level of the perception area, the higher the corresponding score. The smaller the QCI value of the perception area, the higher the corresponding score.

[0228] Table 1

[0229] It should be noted that the score quantification methods corresponding to each influencing factor shown in Table 1 have been introduced above. For details, please refer to the relevant descriptions above. To avoid repetition, they will not be elaborated here.

[0230] In one embodiment, the determination method of the coefficients (hereinafter also referred to as global weights) corresponding to each sub-criterion may include S21 to S24 as follows: S21: Construct the main criterion layer judgment matrix and the sub-criterion layer judgment matrix.

[0231] When constructing the main criterion layer judgment matrix, it can be constructed according to the importance degree of each main criterion for determining the priority. For example, the form of the constructed main criterion layer judgment matrix can refer to Table 2 below: Table 2

[0232] The first row elements [1, 3, 1 / 2, 2] of the main criterion layer judgment matrix can be understood as follows: Taking the perceived environmental characteristics as a benchmark, the importance degrees of each main criterion compared with the perceived environmental characteristics for determining the priority are considered. For example, when determining the priority, if the spatio-temporal feature information is more important than the perceived environmental characteristics, then the element corresponding to the spatio-temporal feature information (3) in the first row elements is 3 times that of the element corresponding to the perceived environmental characteristics (1). Another example is that when determining the priority, if the regional attribute information is less important than the perceived environmental characteristics, then the element corresponding to the regional attribute information (1 / 2) in the first row elements is 1 / 2 of the element corresponding to the spatio-temporal feature information (1). Another example is that when determining the priority, if the perceived service requirements are more important than the perceived environmental characteristics but less important than the spatio-temporal feature information, then the element corresponding to the perceived service requirements (2) is 2 times that of the element corresponding to the perceived environmental characteristics (1).

[0233] The second row elements [1 / 3, 1, 1 / 4, 1 / 2] of the main criterion layer judgment matrix can be understood as follows: Taking the spatio-temporal feature information as a benchmark, the importance degrees of each main criterion compared with the spatio-temporal feature information for determining the priority are considered.

[0234] The third row elements [2, 4, 1, 3] of the main criterion layer judgment matrix can be understood as follows: Taking the regional attribute information as a benchmark, the importance degrees of each main criterion compared with the regional attribute information for determining the priority are considered.

[0235] The fourth row elements [1 / 2, 2, 1 / 3, 1] of the main criterion layer judgment matrix can be understood as follows: Taking the perceived service requirements as a benchmark, the importance degrees of each main criterion compared with the perceived service requirements for determining the priority are considered.

[0236] It should be noted that the values of the elements in the main criterion layer judgment matrix shown in Table 2 are only examples. In specific implementations, according to the importance degrees of each main criterion for determining the priority, the values of the elements in the main criterion layer judgment matrix can be flexibly adjusted.

[0237] When constructing the sub-criterion layer judgment matrix, the sub-criterion layer judgment matrix corresponding to each main criterion can be constructed, specifically according to the importance degrees of each sub-criterion for determining the priority. For example, the sub-criterion layer judgment matrix constructed for the main criterion of perceived environmental characteristics can refer to Table 3 below: Table 3

[0238] The elements in the first row of the sub-criterion layer judgment matrix, [1, 5, 3], can be understood as follows: Taking weather as the benchmark, the importance degrees of each sub-criterion compared with weather for determining the priority. For example, when determining the priority, if temperature is more important than weather, then the element corresponding to temperature (5) in the first row is 5 times the element corresponding to weather (1). When determining the priority, if humidity is more important than weather but less important than temperature, then the element corresponding to humidity (3) in the first row is 2 times the element corresponding to weather (1).

[0239] The elements in the second row of the sub-criterion layer judgment matrix, [1 / 5, 1, 1 / 3], can be understood as follows: Taking temperature as the benchmark, the importance degrees of each sub-criterion compared with temperature for determining the priority.

[0240] The elements in the third row of the sub-criterion layer judgment matrix, [1 / 3, 3, 1], can be understood as follows: Taking humidity as the benchmark, the importance degrees of each sub-criterion compared with humidity for determining the priority.

[0241] It should be noted that the values of the elements in the sub-criterion layer judgment matrix shown in Table 3 are only examples. In specific implementations, according to the importance degrees of each sub-criterion for determining the priority, the values of the elements in the sub-criterion layer judgment matrix can be flexibly adjusted.

[0242] Table 3 exemplarily shows the sub-criterion layer judgment matrix constructed for the main criterion of perceiving environmental characteristics. In a similar way, the corresponding sub-criterion layer judgment matrices can be constructed for the other three main criteria of spatio-temporal feature information, regional attribute information, and perception service requirements respectively.

[0243] S22: Normalize the columns of the main criterion layer judgment matrix, and after averaging the rows, obtain the first weights corresponding to each main criterion.

[0244] Exemplarily, when normalizing each column element in the main criterion layer judgment matrix, the sum of the elements in this column can be calculated, and each element in this column is divided by the sum of the elements in this column to complete the column normalization. For example, after normalizing the columns of the main criterion layer judgment matrix shown in Table 2 above, the following column-normalized matrix shown in Table 4 is obtained: Table 4

[0245] After obtaining the column normalization matrix, the average value of each row element in the column normalization matrix can be calculated to obtain the first weight corresponding to each main criterion. For example, the average value of the elements in the first row of Table 4 above is used as the first weight corresponding to the main criterion of perceiving environmental characteristics, and this first weight = (0.260869565 + 0.3 + 0.24 + 0.307692308) / 4. In a similar manner, the average value of the elements in the second row of Table 4 above is used as the first weight corresponding to the main criterion of spatio-temporal feature information, the average value of the elements in the third row of Table 4 above is used as the first weight corresponding to the main criterion of regional attribute information, and the average value of the elements in the fourth row of Table 4 above is used as the first weight corresponding to the main criterion of perceiving service requirements. The first weights corresponding to each main criterion obtained after performing row averaging on the column normalization matrix shown in Table 4 above can be referred to in Table 5 below: Table 5

[0246] S23: Normalize the columns of the sub-criterion layer judgment matrix, and obtain the second weights corresponding to each sub-criterion after row averaging.

[0247] Among them, the method of column normalization for the sub-criterion layer judgment matrix is the same as that for the main criterion layer judgment matrix above.

[0248] When normalizing each column element in the sub-criterion layer judgment matrix, the sum of the column elements can be calculated, and each element in the column can be divided by the sum of the column elements to complete column normalization. For example, after normalizing the columns of the sub-criterion layer judgment matrix shown in Table 3 above, the column normalization matrix shown in Table 6 below is obtained: Table 6

[0249] After obtaining the column normalization matrix, the average value of each row element in the column normalization matrix can be calculated to obtain the second weights corresponding to each sub-criterion. For example, the average value of the elements in the first row of Table 6 above is used as the second weight corresponding to the sub-criterion of weather, and this second weight = (0.652174 + 0.555556 + 0.692308) / 3 = 0.633346. In a similar manner, the average value of the elements in the second row of Table 6 above is used as the second weight corresponding to the sub-criterion of temperature, and the average value of the elements in the third row of Table 6 above is used as the second weight corresponding to the sub-criterion of humidity. The second weights corresponding to each sub-criterion obtained after performing row averaging on the column normalization matrix shown in Table 6 above can be referred to in Table 7 below: Table 7

[0250] For the sub-criterion layer judgment matrices corresponding to other main criteria, the second weights corresponding to each sub-criterion under the main criterion can be obtained in a similar way.

[0251] S24: For each main criterion, based on the first weight corresponding to the main criterion and the second weights corresponding to each sub-criterion under the main criterion, determine the global weights corresponding to each sub-criterion under the main criterion.

[0252] For example, combining Table 5 and Table 7 above, the global weights corresponding to each sub-criterion (weather, temperature, humidity) under the main criterion of perceiving environmental characteristics can be referred to in Table 8 below: Table 8

[0253] In a similar way to Table 8, the global weights corresponding to each sub-criterion under the main criterion of spatio-temporal feature information, the global weights corresponding to each sub-criterion under the main criterion of regional attribute information, and the global weights corresponding to each sub-criterion under the main criterion of perceiving service requirements can be obtained.

[0254] In one embodiment, assuming that the sensing area is a commercial area in a block, the determination method of the priority score corresponding to the sensing area can be referred to in Table 9 below. The first weights and second weights involved in Table 9 can both be set according to experience: Table 9

[0255] Next, combining Table 9 above and the relevant descriptions above, the determination method of the priority score corresponding to the sensing area will be described: The first target score of the sensing area is weighted based on the score corresponding to the weather (0.8), the coefficient corresponding to the weather (0.205), the score corresponding to the temperature (0.6), the coefficient corresponding to the temperature (0.034), the score corresponding to the humidity (0.7), and the coefficient corresponding to the humidity (0.083). The first target score = 0.205×0.8 + 0.034×0.6 + 0.083×0.7 = 0.164 + 0.020 + 0.058 = 0.242.

[0256] The second target score of the sensing area is weighted based on the score corresponding to the QoS requirement (0.7) and the coefficient corresponding to the QoS requirement (0.173). The second target score = 0.173×0.7 = 0.121.

[0257] The fourth target score of the sensing area is obtained by weighting the product of the score corresponding to the time information (0.5) and the coefficient corresponding to the time information (0.037), and the product of the score corresponding to the location information (0.9) and the coefficient corresponding to the location information (0.056). The fourth target score = 0.056×0.9 + 0.037×0.5 = 0.050 + 0.019 = 0.069.

[0258] The fifth target score of the sensing area is obtained by weighting the product of the score corresponding to the urgency level (0) and the coefficient corresponding to the urgency level (0.384), and the product of the score corresponding to the preset importance level (0.8) and the coefficient corresponding to the preset importance level (0.164). The fifth target score = 0.384×0 + 0.164×0.8 = 0.131.

[0259] The third target score of the sensing area is obtained based on the fourth target score (0.069) and the fifth target score (0.131). The third target score = 0.069 + 0.131 = 0.2.

[0260] The priority score of the sensing area is determined based on the first target score, the second target score, and the third target score. The priority score = 0.242 + 0.121 + 0.2 = 0.563.

[0261] Through the above method, the priority scores of all sensing areas can be calculated. Sort the priority scores of all sensing areas from high to low to obtain the priority ranking list of all sensing areas. This priority ranking list is used for resource allocation of the sensing services corresponding to the N sensing areas.

[0262] Exemplarily, obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas includes: if a preset trigger condition is satisfied, obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas. The preset trigger condition may include at least one of the following: a time-based trigger condition, a sensing environment feature-based trigger condition, a historical data-based trigger condition, or a user demand-based trigger condition.

[0263] The time-based trigger condition can be understood as: the current time is the preset periodic update time. For example, in some scenarios that need to be adjusted regularly (such as: peak traffic hours, low valley hours at night), the priority is updated periodically at fixed intervals, that is, every fixed time (such as every hour, a specific moment every day), the priority of the sensing area is automatically updated to adapt to the periodically changing sensing requirements.

[0264] The triggering condition based on the perceived environmental characteristics can be understood as: the perceived environmental characteristics of the perceived area meet the preset conditions. For example, when the weather conditions change (such as: the rainfall exceeds the set threshold, or weather phenomena such as strong winds and thick fog occur), it can be determined that the perceived environmental characteristics meet the preset conditions, then the priority update is triggered, and the priority of the affected perceived area is adjusted, such as increasing its priority. Another example is when the environmental monitoring values such as temperature and humidity exceed or are lower than the set threshold, it can also be determined that the perceived environmental characteristics meet the preset conditions, thereby triggering the update of the priority, which is applicable to high-risk perception scenarios such as flood monitoring and landslide warning.

[0265] The triggering condition based on historical data can be understood as: the predicted probability of a preset event occurring in the perceived area is greater than the preset threshold, and the preset threshold can be calibrated in advance, such as 60%. In the specific implementation, when the probability of a preset event such as a natural disaster or a traffic peak occurring in the perceived area predicted according to historical data is greater than the preset threshold, the priorities of N perceived areas are updated to optimize the resource allocation in advance and improve the perception response ability of key areas.

[0266] The triggering condition based on user requirements can be understood as: receiving an instruction from the user to update the priority. For example, when the core network device receives a request from a user (such as an emergency management department or a traffic command center) to preferentially perceive a certain perceived area, the priority update is triggered, the priority of the perceived area is dynamically increased, and the priority update process is triggered to meet the personalized perception resource scheduling requirements of the user.

[0267] Based on the above multiple triggering conditions, the update of the priority of the perceived area is decided by the comprehensive analysis of the core network device. The core network device can dynamically adjust the judgment thresholds of various triggering conditions according to the real-time network status, perception task load and service requirements, and at the same time introduce an outlier filtering mechanism to prevent unnecessary frequent priority updates caused by false alarms, abnormal perception data, etc., so as to ensure the stability of the system and the resource scheduling efficiency.

[0268] Through the design of the above multi-dimensional triggering conditions, the above implementation method can flexibly control the rhythm of perception resource scheduling in different service scenarios, realize the dynamic and intelligent management of the priority of the perceived area, and is conducive to meeting the personalized perception resource scheduling requirements.

[0269] The above text mainly introduces the relevant content of determining the priority of the perceived area. Next, a solution for allocating perception resources based on the priority of the perceived area will be further introduced. The allocation of perception resources can be executed on the core network device side or on the base station side, and the following will be introduced separately: In one embodiment, when the allocation of sensing resources is performed on the base station side, the core network device sends a first message to the base station; wherein, the first message carries first information, and the first information is used to indicate the priority information of M sensing regions among N sensing regions, and M is an integer less than or equal to N. After receiving the first message, the base station can parse the first information to know the priority information of the M sensing regions, and based on the priority of the M sensing regions, allocate resources for the sensing services corresponding to the M sensing regions. The resource allocation method on the base station side will be introduced in detail below and will not be elaborated here.

[0270] When M is equal to N, the core network device sends the priorities of all the determined sensing regions to the base station. When M is less than N, the core network device sends the priorities of some of the determined sensing regions to the base station. It can be understood that the N sensing regions may be within the service ranges of multiple base stations, and the first information carried in the first messages sent by the core network device to different base stations may be different. For the first message sent by the core network device to a specific base station, the first information carried in the first message indicates the priority information of M sensing regions among N sensing regions. These M sensing regions may include the sensing regions within the service range of the specific base station, and may also include sensing regions that are not within the service range of the specific base station but are relatively close to the service range of the specific base station, for the purpose of cooperative sensing. For example, sensing regions 1 to 5 among the N sensing regions are within the service range of base station 1, and sensing regions 6 to 9 are not within the service range of base station 1 but are relatively close to the service range of base station 1. Then the first information carried in the first message sent by the core network device to base station 1 is used to indicate the priority information of sensing regions 1 to 9. Sensing regions 6 to 10 among the N sensing regions are within the service range of base station 2. Then the first information carried in the first message sent by the core network device to base station 2 is used to indicate the priority information of sensing regions 6 to 10.

[0271] Exemplarily, the above first message may be a PDU Session Resource Modify Request message sent by a core network device to a base station through the Next-generation Access Protocol (NGAP) protocol, which carries a priority list of M sensing regions. Then, each base station determines the priorities of the M sensing regions based on this priority list, and determines the sensing resource allocation result according to the priorities of the M sensing regions. For example, in combination with the above example, after receiving the first message, Base Station 1 may determine the priorities of Sensing Regions 1 to 9, and then perform sensing resource allocation according to the priorities of Sensing Regions 1 to 9. After receiving the first message, Base Station 2 may determine the priorities of Sensing Regions 6 to 10, and then perform sensing resource allocation according to the priorities of Sensing Regions 6 to 10.

[0272] Exemplarily, the priority list of the sensing regions may be embedded in an extended Information Element (IE) of the PDU Session Resource Modify Request message. In this embodiment, a new custom IE is added, including: region identification ID, priority (first information), and validity period of the priority.

[0273] Exemplarily, the above first information is information on the priority levels of M sensing regions, and the priority levels of the M sensing regions are obtained based on the priority scores of each sensing region in the M sensing regions.

[0274] Among them, a preset scoring interval (such as [0, 1]) may be divided into multiple consecutive sub-scoring intervals, and one sub-scoring interval corresponds to one priority level. According to the sub-scoring interval where the priority score of each sensing region is located, the priority level of the sensing region is determined. For example, the correspondence between the priority levels and multiple consecutive sub-scoring intervals may be as shown in Table 10 below: Table 10

[0275] Through the correspondence shown in Table 10 above, the priority level of each sensing region can be determined according to the priority score corresponding to the sensing region. Among them, the priority levels from high to low are: the first level to the eighth level.

[0276] Exemplarily, the first message further includes: identification information of M sensing regions and / or the validity period of the priority. Among them, the identification information is determined based on the center of the sensing region and the offset of the boundary of the sensing region relative to the center.

[0277] Each sensing area is configured with a unique identification information, namely the area ID. The area ID is jointly determined based on the central position information of the sensing area and the offset information of the boundary relative to the center. Specifically, the center of the sensing area can be represented by a global hash value. For example, a 16-bit geographical hash is used to roughly locate the base station coverage area or the center point of the sensing area. On this basis, grid cells are further divided within the hash area, and a more fine-grained location identification is achieved through additional offset information. Among them, the offset of the boundary relative to the center includes: the horizontal offset along the x direction and the vertical offset along the y direction, which are used to characterize the relative expansion range of the sensing area in the two-dimensional space. For example, 16-bit binary values are respectively used to represent the offsets in the x direction and the y direction, so as to achieve the precise definition of the boundary of the sensing area. The area ID generated in the above way can represent the area range of the sensing area while ensuring uniqueness and identifiability.

[0278] The priority of the sensing area has a certain validity period, which is calculated from the moment of priority update. Specifically, the validity period of the priority depends on the following factors: If the priority update is triggered by a time condition based on a fixed period, the validity period of the priority is equal to the length of this period. For example, in the periodic update mechanism set during the traffic peak period or the night valley period, the validity period of the priority is the duration of a complete cycle. When the priority update is caused by a change in the characteristics of the sensing environment, the validity period of the priority will continue until the characteristics of the sensing environment change significantly again or reach a preset threshold. For example, if the priority is increased because the rainfall exceeds the threshold, the validity period will continue until the rainfall drops back to the normal range or is lower than the set threshold. If the priority adjustment is based on the occurrence probability of a preset event being greater than a preset probability threshold, the validity period depends on the actual occurrence time of this preset event or the time point of probability re-evaluation. For the priority change triggered by a specific user request, the validity period can be specified by the user or automatically determined according to the task completion situation. For example, when the emergency management department requests to temporarily increase the monitoring level of a certain area, the validity period may last until the emergency is lifted or the scheduled task is completed. In addition, the core network device continuously monitors the status of each sensing area and dynamically shortens or extends the validity period of the priority when necessary to adapt to the real-time changing requirements and environmental conditions. In this way, it is possible to ensure the efficient use of resources while flexibly responding to various emergencies.

[0279] Exemplarily, the signaling frame of the first message is shown in Table 11 below, including: area ID, the priority level of the sensing area, and the validity period of the priority.

[0280] Table 11

[0281] Region ID (32 bits): It is represented by a global hash (16 bits) + a local offset (16 bits). Among them, the global hash represents the center of the sensing region, and a low-precision 16-bit hash is used to identify the center of the sensing region. The local offset (16 bits) includes the horizontal offset (8 bits) of the boundary of the sensing region along the x direction compared to the center and the vertical offset (8 bits) along the y direction.

[0282] Priority Level (3 bits): The priority level, which can be represented as levels 1 to 8, corresponding to the eight priority levels in Table 11 above respectively.

[0283] Validity Time (32 bits): The validity period of the priority (UTC timestamp), which is used to avoid long-term invalid updates of the priority. Coordinated Universal Time (UTC) timestamp refers to a time recording format based on the UTC standard and is widely used in computer systems and network communications (such as 5G signaling).

[0284] In another embodiment, the allocation of sensing resources is performed on the core network device side. The core network device obtains a resource allocation result according to the priorities of N sensing regions; the core network device sends a second message to the base station; wherein, the second message carries the resource allocation result, and the resource allocation result includes the resource allocation amounts of the sensing services corresponding to N sensing regions.

[0285] Specifically, the core network device can obtain the resource allocation result according to the priority sorting result of N sensing regions, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to each sensing region. For example, the core network device allocates resources to the sensing services corresponding to each sensing region in order from high to low according to the priorities of the sensing regions, so as to preferentially allocate sensing resources to the sensing regions with high priorities. During the allocation process, the total amount of sensing resources to be allocated and the actual resource requirements of each sensing service are comprehensively considered to achieve efficient utilization of resources and preferentially meet the sensing requirements of the sensing regions with high priorities.

[0286] Exemplarily, the core network device obtains the resource allocation result according to the priorities of N sensing regions, including: the core network device divides the sensing regions belonging to the same priority level among the N sensing regions into one group according to the priority levels of the N sensing regions, and obtains L groups of sensing regions; wherein, the priority levels of the sensing regions in each group are the same, and L is an integer greater than or equal to 1; the core network device obtains the resource allocation amounts of the sensing services corresponding to the N sensing regions according to the sorting of the priority levels of the L groups of sensing regions, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to each sensing region.

[0287] For example, in combination with the correspondence between the priority levels and the scoring sub - intervals shown in Table 10 above, the priority levels of each sensing area can be determined, and thus each sensing area can be grouped according to the priority levels of each sensing area. In Table 12, taking the grouping of sensing areas 1 to 10 as an example, according to the priority levels of the 10 sensing areas, these 10 sensing areas are divided into 5 groups. Among them, the first group includes 3 sensing areas, namely sensing areas 1, 4, and 10; the second group includes 2 sensing areas, namely sensing areas 2 and 7; the third group includes 3 sensing areas, namely sensing areas 3, 6, and 8; the fourth group includes 1 sensing area, i.e., sensing area 5; and the fifth group includes 1 sensing area, i.e., sensing area 9.

[0288] Table 12

[0289] The total amount of resources to be allocated (i.e., the total sensing resources) can be understood as: the total amount of idle resources available for sensing task scheduling or the total amount of resources that can be allocated. This total amount of resources can be determined based on the real - time monitoring results of the overall resource usage status. The total amount of resources to be allocated includes the following types: the total amount of time resources to be allocated (scheduling time slots or symbol resources for periodic sensing tasks), the total amount of frequency resources to be allocated, and the total amount of beam resources to be allocated, etc.

[0290] The resource demand required for the sensing service corresponding to each sensing area can be determined according to the specific requirements of the sensing area for sensing metrics. The sensing metrics include at least one of the following: confidence level, positioning deviation, speed error, resolution, sensing delay, refresh rate, miss detection rate, and false detection rate, etc.

[0291] The confidence level refers to the credibility of the sensing result. The positioning deviation refers to the deviation degree between the target estimated position information and the target actual position information, and this deviation degree includes the deviation in the horizontal direction and the deviation in the vertical direction. The speed error refers to the deviation degree between the target estimated speed information and the target actual speed information. The resolution refers to the closest degree between two adjacent targets that can be distinguished, and can be specifically divided into the resolution in the distance dimension, the resolution in the speed dimension, etc. The sensing delay refers to the time interval from service trigger to the detection result. The refresh rate refers to the reporting interval between two consecutive sensing results; among them, the two reported results may be different. The miss detection rate refers to the ratio or probability that actually occurs but is not detected (i.e., the miss detection ratio and the miss detection probability). The false detection rate refers to the ratio or probability that actually does not occur but is detected as occurring (i.e., the false detection ratio and the false detection probability).

[0292] For example, if a certain sensing area requires a higher confidence level, smaller positioning deviation and speed error, higher resolution, lower sensing latency, higher refresh frequency, and lower missed detection rate and false detection rate, it indicates that the sensing area has higher requirements for sensing quality. Correspondingly, the amount of sensing resources required to execute the sensing task corresponding to the sensing area is also larger. Therefore, in the process of resource allocation, the core network device dynamically adjusts the resource allocation amount obtained by each sensing area according to the priority order of each sensing area, in combination with the matching relationship between its sensing index requirements and the total amount of resources to be allocated, so as to achieve an optimal balance between sensing service quality and resource utilization efficiency. Through the above mechanism, under the condition of limited resources, the sensing performance requirements of high-priority sensing areas can be preferentially guaranteed.

[0293] Exemplarily, the core network device obtains the resource allocation amounts of the sensing services corresponding to N sensing areas according to the sorting of the priority levels of L groups of sensing areas, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to each sensing area, including: in the order from high to low of the priority levels of the L groups of sensing areas, successively determining the resource allocation amounts of the sensing services within each sensing area in each group of sensing areas according to the resource requirements of the sensing services corresponding to each sensing area in each group of sensing areas and the total amount of resources to be allocated. For example, for the five groups of sensing areas obtained by combining Table 12 above, in the order from the first group of sensing areas to the fifth group of sensing areas, successively determine the resource allocation amounts of the sensing services within each sensing area in each group of sensing areas according to the resource requirements of the sensing services corresponding to each sensing area in each group of sensing areas and the total amount of resources to be allocated until the total amount of resources to be allocated is 0.

[0294] In one embodiment, when the resource requirements of the sensing services corresponding to each sensing area in the i-th group of sensing areas are less than or equal to the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to each sensing area in the i-th group of sensing areas are determined based on the resource requirements of the sensing services corresponding to each sensing area; where 1 ≤ i ≤ L. That is, for each sensing area in the i-th group of sensing areas, if the required sensing resources can be fully satisfied, all the required resource amounts are allocated to it. The resource allocation amounts of the sensing services corresponding to each sensing area in the i-th group of sensing areas may be equal to the resource requirements of the corresponding sensing areas.

[0295] For example, in combination with Table 12 above, when the i-th group of sensing regions is the first group of sensing regions, if the sum of the resource requirements of the sensing services corresponding to sensing region 1, sensing region 4, and sensing region 10 in the first group of sensing regions is less than or equal to the total amount of resources to be allocated, then the resource allocation amount of the sensing service corresponding to sensing region 1 is equal to the resource requirement of the sensing service corresponding to sensing region 1, the resource allocation amount of the sensing service corresponding to sensing region 4 is equal to the resource requirement of the sensing service corresponding to sensing region 4, and the resource allocation amount of the sensing service corresponding to sensing region 10 is equal to the resource requirement of the sensing service corresponding to sensing region 10.

[0296] In one embodiment, when the resource requirements of the sensing services corresponding to the sensing regions in the i-th group of sensing regions are greater than the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to the sensing regions in the i-th group of sensing regions are determined based on the total number of sensing regions in the i-th group of sensing regions and the total amount of resources to be allocated. That is, if the resource requirements of the sensing services corresponding to the sensing regions in the i-th group of sensing regions cannot be satisfied, an equal division strategy is adopted to allocate resources to each sensing region in the i-th group of sensing regions, and the total amount of resources to be allocated is evenly distributed to the sensing services corresponding to each sensing region in the group, so as to ensure that multiple sensing tasks have a certain degree of resource guarantee, thereby improving the availability of the overall sensing system and the task completion probability.

[0297] For example, in combination with Table 12 above, when the i-th group of sensing regions is the second group of sensing regions, if the sum of the resource requirements of the sensing services corresponding to sensing region 2 and sensing region 7 in the second group of sensing regions is greater than the total amount of resources to be allocated, then the total amount of resources to be allocated is evenly distributed to the sensing services corresponding to sensing region 2 and sensing region 7.

[0298] Through the above resource allocation mechanism, it is possible to preferentially guarantee the sensing capabilities of high-priority sensing regions under resource constraints and achieve fair resource allocation among low-priority sensing regions, thereby achieving efficient utilization of sensing resources and reasonable control of service quality on a global scale.

[0299] It can be understood that there are a large number of sensing devices and they may be distributed at different locations. Therefore, after determining the resource allocation amounts of the sensing services corresponding to each sensing region, it is also possible to further determine which target sensing devices will use the resource allocation amounts, so that the target sensing devices can use the resource allocation amounts to execute the sensing services corresponding to the sensing regions to ensure the effective execution of sensing tasks. For this purpose, according to the reported data of at least one sensing device, for each sensing service corresponding to a sensing region, the most suitable target sensing device can be selected from among many sensing devices.

[0300] Exemplarily, the resource allocation result further includes: information of the target sensing device; wherein, the target sensing device is determined based on the reporting data of at least one sensing device, and the reporting data includes the location data and device attribute data of at least one sensing device. The information of the target sensing device may be the identification information of the target sensing device.

[0301] The device attribute data includes at least one of: sensing capability, data processing capability, and sensing data type. The data processing capability indicates whether the sensing device has the ability to analyze the sensed data after collecting it, such as video encoding, compression, etc. Different data processing capabilities will affect data transmission. The sensing data type represents the result of sensing measurement, such as voice data type, video data type. Different types of data have different requirements for transmission. For example, video data requires low latency and high bandwidth, etc.

[0302] The core network device receives the reporting data from multiple sensing devices (such as UEs, non-3GPP devices, base stations) to obtain the information of the target sensing device corresponding to N sensing regions based on these reporting data. For example, when determining the target sensing device corresponding to each sensing region, the sensing device that best matches the sensing service corresponding to the sensing region can be selected as the target sensing device according to the location data and device attribute data of multiple sensing devices. That is, among multiple sensing devices, the target sensing device is the most suitable to execute the sensing service corresponding to the sensing region. For the sensing service corresponding to a sensing region, the target sensing device may be one or multiple. That is to say, multiple sensing devices can perform collaborative sensing on the same sensing region.

[0303] When determining the target sensing device corresponding to each sensing region, the geographical range of the sensing region is compared with the location data of multiple sensing devices respectively, and the sensing devices located in or near the sensing region are screened out as candidate devices. For example, for sensing region 1, the sensing devices with geographical locations close to sensing region 1 are preferentially considered as candidate devices. The number of candidate devices may be multiple, and the device attribute data of the candidate devices can be further evaluated. For example, if the sensing capability, data processing capability, and sensing data type of the candidate device can meet the requirements of the sensing service corresponding to sensing region 1, the candidate device that can meet the requirements of the sensing service corresponding to sensing region 1 is selected as the target sensing device corresponding to sensing region 1. In sensing region 1, assume that after evaluation, it is found that sensing device b not only has the closest geographical location but also its sensing capability and data processing capability can meet and even exceed the specific requirements of the sensing service corresponding to sensing region 1, then sensing device b is selected as the target sensing device corresponding to sensing region 1.

[0304] The above implementation method can dynamically select target sensing devices suitable for participating in the sensing services corresponding to each sensing area based on the location data and device attribute data reported by the sensing devices, and realize the intelligent scheduling and optimal configuration of sensing resources by combining the priorities of the sensing areas and the resource requirements, which is beneficial to meeting the diverse sensing requirements of different sensing areas.

[0305] As can be seen from the above, the core network device can determine the resource allocation result according to the priorities of the N sensing areas and the reported data of each sensing device. The resource allocation result includes the resource allocation amounts for the sensing services corresponding to the N sensing areas and the information of the corresponding target sensing devices. The core network device can send a second message to the base station, and the second message carries the resource allocation result. After receiving the second message, the base station parses the second message to obtain the resource allocation result carried by the second message. For any sensing area a among the N sensing areas, the base station can send a sensing control instruction to the target sensing device corresponding to the sensing area a. The sensing control instruction carries key information such as the area ID of the sensing area a, sensing parameters (such as signal waveform, allocated frequency domain resources, time domain resources, etc.), sensing modes (such as self-transmitting and self-receiving mode, base station cooperation sensing mode, etc.), and the reporting method of sensing data (such as single reporting, periodic reporting, etc.). Among them, the sensing parameters can be determined based on the resource allocation amount for the sensing service corresponding to the sensing area a. The target sensing device receives the sensing control instruction and executes the sensing service corresponding to the sensing area a based on the sensing control instruction.

[0306] The above mainly introduced the priority update of the sensing areas and the allocation of sensing resources on the core network device side. The following will introduce the content of allocating sensing resources on the base station side: The embodiment of the present application also provides a resource allocation method applied to a base station. Figure 7 It is a schematic flow of a resource allocation method provided by the embodiment of the present application.

[0307] Exemplarily, as Figure 7 shown, the resource allocation method includes: S701: Receive a first message sent by a core network device; wherein, the first message carries first information, and the first information is used to indicate the information of the priorities of M sensing areas among the N sensing areas. The N sensing areas are obtained by dividing the sensing ranges of at least one sensing device. The priorities of the N sensing areas are obtained by the core network device according to the sensing results corresponding to the N sensing areas. M is an integer less than or equal to N, and N is an integer greater than or equal to 2; S702: Obtain a resource allocation result according to the first information; wherein, the resource allocation result includes the resource allocation amounts for the sensing services corresponding to the M sensing areas.

[0308] InFigure 7 In the illustrated embodiment, the authority of the core network device is decentralized, and the base station dynamically allocates sensing resources, which is beneficial to improving the allocation efficiency of sensing resources. Based on the physical location advantage that the base station is closer to the sensing devices (such as UEs, non-3GPP devices, etc.) than the core network device, in scenarios where the sensing task requires quick response (such as traffic warning, emergency monitoring, etc.), the base station can complete the resource scheduling decision faster, reduce the control delay, and improve the overall timeliness of the system. After receiving the first message sent by the core network device, the base station can directly perform sensing resource allocation locally according to the priorities of the M sensing regions without waiting for the resource allocation result sent by the core network device. If the priorities of the M sensing regions change, the resource allocation result obtained by the base station for sensing resource allocation will also change, which is beneficial to improving the flexibility of sensing resource allocation, realizing preferential allocation of sensing resources to high-priority sensing regions, and adapting to the diverse sensing requirements in the communication-sensing integration scenario.

[0309] Next, Figure 7 the specific implementation methods of each step in the illustrated embodiment will be described: As described above, when the allocation of sensing resources is performed on the base station side, the core network device will send a first message to the base station. Correspondingly, in S701, the base station receives the first message sent by the core network device.

[0310] Exemplarily, the first information is the information of the priority levels of the M sensing regions, and the priority levels of the M sensing regions are obtained based on the priority scores of each sensing region in the M sensing regions.

[0311] Exemplarily, the first message further includes: the identification information of the M sensing regions and / or the validity period of the priority; wherein, the identification information is determined based on the center of the sensing region and the offset of the boundary of the sensing region relative to the center.

[0312] It should be noted that the explanation of the first message can be referred to the relevant content above, and to avoid repetition, it will not be elaborated here.

[0313] Exemplarily, the validity period of the resource allocation result corresponding to each sensing service in each sensing region is the same as the validity period of the priority of the sensing region. That is, when the priority of the sensing region is updated or invalidated, the corresponding resource allocation result is also updated or invalidated synchronously, ensuring the consistency between the resource allocation result and the priority status of the sensing region, avoiding the problem of resource misallocation caused by priority change, and improving the accuracy and timeliness of resource allocation.

[0314] In S702, the base station obtains the resource allocation result according to the first information, which can be understood as: the base station determines the resource allocation result according to the priorities of M sensing regions; the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the M sensing regions.

[0315] In one embodiment, the base station obtains the resource allocation result according to the first information, including: the base station determines L groups of sensing regions according to the priority levels of the M sensing regions; wherein, the priority levels of the sensing regions in each group are the same, and L is an integer greater than or equal to 1; the base station obtains the resource allocation result according to the sorting of the priority levels of the L groups of sensing regions, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to the respective sensing regions.

[0316] Exemplarily, the base station divides the sensing regions with the same priority level among the M sensing regions into one group according to the priority levels of the M sensing regions, and obtains L groups of sensing regions; wherein, the priority levels of the sensing regions in each group are the same, and L is an integer greater than or equal to 1; the base station sequentially determines the resource allocation amounts of the sensing services within the respective sensing regions in each group according to the resource requirements of the sensing services corresponding to the respective sensing regions in each group and the total amount of resources to be allocated in the order from high to low of the priority levels of the L groups of sensing regions. Among them, the explanations of the resource requirements and the total amount of resources to be allocated can be referred to the relevant content above, and will not be repeated here to avoid duplication.

[0317] Exemplarily, when the resource requirements of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are less than or equal to the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are determined based on the resource requirements of the sensing services corresponding to the respective sensing regions; wherein, 1 ≤ i ≤ L.

[0318] Exemplarily, when the resource requirements of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are greater than the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are determined based on the total number of sensing regions in the i-th group of sensing regions and the total amount of resources to be allocated.

[0319] Exemplarily, the resource allocation result further includes: information of the target sensing device; wherein, the target sensing device is determined based on the reported data of at least one sensing device, and the reported data includes the location data and device attribute data of at least one sensing device.

[0320] It should be noted that when the base station side allocates sensing resources, the method for determining the resource allocation amount and the information of the target sensing device is basically the same as that adopted by the core network device side, and reference can be made to the relevant content above, and will not be repeated here to avoid duplication.

[0321] Figure 8 It is a schematic flowchart of resource allocation according to the sorting of priority levels provided by an embodiment of the present application.

[0322] Exemplarily, as Figure 8 shown, it is assumed that the priority levels are from high to low in sequence: the first level to the eighth level. The process of resource allocation according to the sorting of priority levels includes: S801: Determine whether the resources can meet the first priority requirement. If so, execute S802; otherwise, execute S810.

[0323] Among them, the resources in S801 can be understood as the total amount of resources to be allocated, and the first priority requirement can be understood as the sum of the resource requirements of each sensing area at the first level. Therefore, S801 can be understood as: determining whether the total amount of resources to be allocated meets the resource requirements of each sensing area at the first level. If the total amount of resources to be allocated is greater than or equal to the sum of the resource requirements of each sensing area at the first level, it is determined that the first priority requirement is met. If the total amount of resources to be allocated is less than the sum of the resource requirements of each sensing area at the first level, it is determined that the first priority requirement is not met. The resource requirement of a sensing area refers to the resource requirement of the sensing service corresponding to the sensing area.

[0324] S802: Determine whether the resources can meet the second priority requirement. If so, execute S803; otherwise, execute S811.

[0325] Among them, the resources in S802 can be understood as: the first remaining total amount of resources after subtracting the resource requirements of each sensing area at the first level from the total amount of resources to be allocated, and the second priority requirement can be understood as the sum of the resource requirements of each sensing area at the second level. Therefore, S802 can be understood as: determining whether the first remaining total amount of resources meets the resource requirements of each sensing area at the second level. If the first remaining total amount of resources is greater than or equal to the sum of the resource requirements of each sensing area at the second level, it is determined that the second priority requirement is met. If the first remaining total amount of resources is less than the sum of the resource requirements of each sensing area at the second level, it is determined that the second priority requirement is not met.

[0326] S803: Determine whether the resources can meet the third priority requirement. If so, execute S804; otherwise, execute S812.

[0327] Among them, the resources in S803 can be understood as: the total amount of resources to be allocated minus the resource requirements of each sensing area at the first and second levels, which is the second remaining total amount of resources. The third-priority requirement can be understood as the sum of the resource requirements of each sensing area at the third level. If the second remaining total amount of resources is greater than or equal to the sum of the resource requirements of each sensing area at the third level, it is determined that the third-priority requirement is met; otherwise, it is determined that the third-priority requirement is not met.

[0328] S804: Determine whether the resources can meet the fourth-priority requirement. If so, execute S805; otherwise, execute S813.

[0329] Among them, the resources in S804 can be understood as: the total amount of resources to be allocated minus the resource requirements of each sensing area at the first to third levels, which is the third remaining total amount of resources. The fourth-priority requirement can be understood as the sum of the resource requirements of each sensing area at the fourth level. If the third remaining total amount of resources is greater than or equal to the sum of the resource requirements of each sensing area at the fourth level, it is determined that the fourth-priority requirement is met; otherwise, it is determined that the fourth-priority requirement is not met.

[0330] S805: Determine whether the resources can meet the fifth-priority requirement. If so, execute S806; otherwise, execute S814.

[0331] Among them, the resources in S805 can be understood as: the total amount of resources to be allocated minus the resource requirements of each sensing area at the first to fourth levels, which is the fourth remaining total amount of resources. The fifth-priority requirement can be understood as the sum of the resource requirements of each sensing area at the fifth level. If the fourth remaining total amount of resources is greater than or equal to the sum of the resource requirements of each sensing area at the fifth level, it is determined that the fifth-priority requirement is met; otherwise, it is determined that the fifth-priority requirement is not met.

[0332] S806: Determine whether the resources can meet the sixth-priority requirement. If so, execute S807; otherwise, execute S815.

[0333] Among them, the resources in S806 can be understood as: the total amount of resources to be allocated minus the resource requirements of each sensing area at the first to fifth levels, which is the fifth remaining total amount of resources. The sixth-priority requirement can be understood as the sum of the resource requirements of each sensing area at the sixth level. If the fifth remaining total amount of resources is greater than or equal to the sum of the resource requirements of each sensing area at the sixth level, it is determined that the sixth-priority requirement is met; otherwise, it is determined that the sixth-priority requirement is not met.

[0334] S807: Determine whether the resources can meet the seventh-priority requirement. If so, execute S808; otherwise, execute S816.

[0335] Among them, the resources in S807 can be understood as: the total remaining resources of the sixth level after subtracting the resource requirements of each sensing area at the first to sixth levels from the total amount of resources to be allocated. The seventh priority requirement can be understood as the sum of the resource requirements of each sensing area at the seventh level. If the total remaining resources of the sixth level are greater than or equal to the sum of the resource requirements of each sensing area at the seventh level, it is determined that the seventh priority requirement is met; otherwise, it is determined that the seventh priority requirement is not met.

[0336] S808: Determine whether the resources can meet the eighth priority requirement. If so, execute S809; otherwise, execute S817.

[0337] Among them, the resources in S808 can be understood as: the total remaining resources of the seventh level after subtracting the resource requirements of each sensing area at the first to seventh levels from the total amount of resources to be allocated. The eighth priority requirement can be understood as the sum of the resource requirements of each sensing area at the eighth level. If the total remaining resources of the seventh level are greater than or equal to the sum of the resource requirements of each sensing area at the eighth level, it is determined that the eighth priority requirement is met; otherwise, it is determined that the eighth priority requirement is not met.

[0338] S809: Adopt the ninth type of resource allocation method.

[0339] It can be understood that when executing to S809, it means that the total amount of resources to be allocated is greater than or equal to the sum of the resource requirements of all sensing areas at the first to eighth levels. Therefore, all sensing areas at the first to eighth levels can be allocated the resource requirements they need. The ninth type of allocation method can be understood as: allocating all sensing areas to all sensing areas, that is, the resource allocation amount of each sensing area at the first to eighth levels is equal to the resource requirement of each sensing area.

[0340] S810: Adopt the first type of resource allocation method.

[0341] It can be understood that when executing to S810, it means that the total amount of resources to be allocated is less than the sum of the resource requirements of each sensing area at the first level. In this case, the first type of resource allocation method can be understood as: evenly allocating the total amount of resources to be allocated to each sensing area at the first level.

[0342] S811: Adopt the second type of resource allocation method.

[0343] It can be understood that when executing to S811, it means that the first priority requirement is met, but the second priority requirement is not met. Therefore, the second type of resource allocation method can be understood as: allocating the resource requirements required for the corresponding sensing services to each sensing area at the first level, and then evenly allocating the total remaining resources of the first level to the sensing services corresponding to each sensing area at the first level.

[0344] S812: Adopt the third type of resource allocation method.

[0345] It can be understood that when executing to S812, it indicates that the first - priority requirements and the second - priority requirements are met, but the third - priority requirements are not met. Therefore, the third type of resource allocation method can be understood as: allocate the resource requirements needed for the corresponding sensing services to each sensing area at the first level and the second level, and then evenly distribute the total amount of the second remaining resources to the sensing services corresponding to each sensing area at the third level.

[0346] S813: Adopt the fourth type of resource allocation method.

[0347] It can be understood that when executing to S813, it indicates that the first - priority requirements to the third - priority requirements are met, but the fourth - priority requirements are not met. Therefore, the fourth type of resource allocation method can be understood as: allocate the resource requirements needed for the corresponding sensing services to each sensing area at the first level to the third level, and then evenly distribute the total amount of the third remaining resources to the sensing services corresponding to each sensing area at the fourth level.

[0348] S814: Adopt the fifth type of resource allocation method.

[0349] It can be understood that when executing to S814, it indicates that the first - priority requirements to the fourth - priority requirements are met, but the fifth - priority requirements are not met. Therefore, the fifth type of resource allocation method can be understood as: allocate the resource requirements needed for the corresponding sensing services to each sensing area at the first level to the fourth level, and then evenly distribute the total amount of the fourth remaining resources to the sensing services corresponding to each sensing area at the fifth level.

[0350] S815: Adopt the sixth type of resource allocation method.

[0351] It can be understood that when executing to S815, it indicates that the first - priority requirements to the fifth - priority requirements are met, but the sixth - priority requirements are not met. Therefore, the sixth type of resource allocation method can be understood as: allocate the resource requirements needed for the corresponding sensing services to each sensing area at the first level to the fifth level, and then evenly distribute the total amount of the fifth remaining resources to the sensing services corresponding to each sensing area at the sixth level.

[0352] S816: Adopt the seventh type of resource allocation method.

[0353] It can be understood that when it comes to S816, it means that the first to sixth priority requirements are met, but the seventh priority requirement is not met. Therefore, the sixth type of resource allocation method can be understood as follows: allocate the resource demand required for the corresponding sensing services to each sensing area at the first to sixth levels, and then evenly distribute the total amount of the sixth remaining resources to the sensing services corresponding to each sensing area at the seventh level.

[0354] S817: Adopt the eighth type of resource allocation method.

[0355] It can be understood that when it comes to S817, it means that the first to seventh priority requirements are met, but the eighth priority requirement is not met. Therefore, the seventh type of resource allocation method can be understood as follows: allocate the resource demand required for the corresponding sensing services to each sensing area at the first to seventh levels, and then evenly distribute the total amount of the seventh remaining resources to the sensing services corresponding to each sensing area at the eighth level.

[0356] In each of the above resource allocation methods, due to the limited total amount of resources to be allocated, there may be a situation where the sensing areas at certain levels are not allocated sensing resources. For example, in the first type of resource allocation method, the sensing areas at the second to eighth levels will not be allocated sensing resources. In the second type of resource allocation method, the sensing areas at the third to eighth levels will not be allocated sensing resources. In the fifth type of resource allocation method, the sensing areas at the sixth to eighth levels will not be allocated sensing resources.

[0357] The above method of resource allocation according to the sorting of priority levels is conducive to ensuring that the sensing services corresponding to the sensing areas with high priorities are preferentially allocated sensing resources, so as to achieve the preferential sensing and processing of the sensing areas with high priorities.

[0358] Exemplarily, after the base station receives the first message sent by the core network device, the base station sends a data reporting request to at least one sensing device; the base station receives the reported data of at least one sensing device and sends the reported data to the core network device; wherein, the reported data includes the location data and device attribute data of at least one sensing device.

[0359] In one embodiment, the base station may send a priority list of sensing areas (including the area ID, priority level, and validity period of the priority of the sensing area) to the UE and request the UE to report data. Specifically, the base station may send information such as the area ID, priority level, and validity period of the priority of the sensing area to the UE through a Radio Resource Control Reconfiguration (RRC Reconfiguration) message. The base station requests the UE to report location data, sensing capabilities (such as remaining battery power, sensor type, supported sensing modes, sensing range, measurement accuracy, etc.), data processing capabilities, sensing data types, etc. through the above RRC reconfiguration message, so as to dynamically filter out UEs that cannot meet the sensing requirements in subsequent resource allocation and determine the target sensing devices corresponding to each sensing area. That is, the base station receives the reported data for subsequent selection of target sensing devices and resource allocation.

[0360] Exemplarily, after receiving the RRC reconfiguration message, the UE may further send a data reporting request to non-3GPP devices within its neighboring range to obtain the reported data of these non-3GPP devices. In response to the data reporting request, the non-3GPP device communicates directly with the UE through the PC5 interface and reports its location data, sensing capabilities (such as sensor type, supported sensing modes, sensing accuracy, measurement error, etc.), data processing capabilities, sensing data types, etc. using a Sidelink Radio Resource Control (Sidelink RRC) message.

[0361] Exemplarily, after the UE collects the reported data such as the sensing capabilities of itself and non-3GPP devices, it reports these reported data to the base station through a UE Capability Information message. After receiving the reported data of sensing devices such as the UE and non-3GPP devices, the base station determines the resource allocation result according to these reported data and the priorities of the M sensing areas. The resource allocation result includes the resource allocation amount of the sensing services corresponding to the M sensing areas and the information of the corresponding target sensing devices (i.e., the identification of the sensing devices selected to perform specific sensing tasks), and may also include the validity period information of the resource allocation result.

[0362] Exemplarily, according to the resource allocation result, the base station sends a sensing control instruction to the target sensing device. The sensing control instruction carries key information such as the area ID of the sensing area, sensing parameters, sensing mode, and the reporting method of sensing data, so that the target sensing device can perform the sensing service corresponding to the sensing area according to the sensing control instruction.

[0363] Exemplarily, after the base station receives the reported data from sensing devices such as UEs and non-3GPP devices, the base station uploads the reported data to the core network device so that the core network device can make relevant decisions based on the received reported data.

[0364] The following introduces the priority determination method and resource allocation method provided by the embodiments of the present application from the interaction level.

[0365] Figure 9 It is an interaction schematic diagram related to the priority determination method and resource allocation method provided by the embodiments of the present application.

[0366] Exemplarily, as Figure 9 shown, the priority determination method and resource allocation method include: S901: Each sensing device measures sensing data respectively. Among them, S901 includes S9011 to S9014 as follows: S9011: Measure the sensing data of non-3GPP devices.

[0367] S9012: Measure the sensing data of UEs.

[0368] S9013: Measure the sensing data of base station 1.

[0369] S9014: Measure the sensing data of base station N.

[0370] S902: Each sensing device uploads the sensing data. Among them, S902 includes S9021 to S9024 as follows: S9021: The non-3GPP device uploads the sensing data to the core network device.

[0371] Optionally, the non-3GPP device can first transmit the sensing data to the UE, and then the UE transmits the sensing data to the core network device through the base station.

[0372] S9022: The UE uploads the sensing data to base station 1.

[0373] S9023: Base station 1 uploads its own sensing data and the sensing data of the UE to the core network device.

[0374] S9024: Base station N uploads its own sensing data to the core network device.

[0375] S903: The core network device updates the priority.

[0376] Specifically, the core network device collects the sensing data of non-3GPP devices, UEs, and base stations 1 to N, fuses and analyzes the sensing data from multiple sensing devices, and generates comprehensive and accurate sensing results. The core network updates the priority levels of the sensing regions within the service scopes of all sensing devices based on one or more of the sensing results corresponding to each sensing region, sensing service requirements, regional attribute information, and spatio-temporal feature information, and obtains a priority list for each sensing region. The priority update includes the priority level of the updated sensing region and the regional scope of the sensing region.

[0377] S904: The core network device sends a Protocol Data Unit Session Resource Modify Request (PDU Session Resource Modify Request) to base station 1.

[0378] Among them, the core network device embeds the priority list of each sensing region into the extended information element of the PDU Session Resource Modify Request and sends it to base station 1. After receiving the PDU Session Resource Modify Request, base station 1 updates the priority levels of the sensing regions within its sensing scope according to the priority list.

[0379] It should be noted that Figure 9 exemplarily shows that the core network device sends a PDU Session Resource Modify Request to base station 1. In a specific implementation, the core network device also sends a PDU Session Resource Modify Request to other base stations (such as base station N in the figure) to enable other base stations (such as base station N in the figure) to update the priority levels of the sensing regions within their sensing scopes.

[0380] In one embodiment, the priority lists carried in the PDU Session Resource Modify Requests sent by the core network device to base stations 1 to N are all the same, and this priority list includes the priorities of all sensing regions updated by the core network device.

[0381] In another embodiment, there are differences in the priority lists carried in the PDU Session Resource Modify Request sent by the core network device to Base Station 1 to Base Station N. For example, the priority list 1 is carried in the PDU Session Resource Modify Request sent by the core network device to Base Station 1, and the priority list N is carried in the PDU Session Resource Modify Request sent by the core network device to Base Station N. The priority list 1 includes the priorities of the respective sensing areas within the service range of Base Station 1. Optionally, it may also include the priorities of the respective sensing areas close to the service range of Base Station 1. The priority list N includes the respective sensing areas within the service range of Base Station N. Optionally, it may also include the priorities of the respective sensing areas close to the service range of Base Station 1.

[0382] S905: Base Station 1 sends a Protocol Data Unit Session Resource Modify Response (PDU Session Resource Modify Response) to the core network device.

[0383] For example, after completing the priority update of the respective sensing areas within its service range, Base Station 1 may send a PDU Session Resource Modify Response to the core network device.

[0384] It should be noted that Figure 9 exemplarily shows that Base Station 1 sends a PDU Session Resource Modify Response to the core network device. In a specific implementation, after receiving the PDU Session Resource Modify Request sent by the core network device, other base stations (such as Base Station N in the figure) may also send a PDU Session Resource Modify Response to the core network device.

[0385] S906: Base Station 1 sends a priority list, sensing capabilities, location data, data type, and data processing capability reporting request to the UE.

[0386] The priority list sent by Base Station 1 to the UE is the same as the priority list carried in the PDU Session Resource Modify Request sent by the core network device to Base Station 1. The priority list sent by Base Station 1 to the UE is used to assist the UE in performing sensing operations. The reporting request sent by Base Station 1 to the UE is used to instruct the UE to report its sensing capabilities, location data, data type (also referred to as sensing data type above), and data processing capabilities.

[0387] S907: The UE sends a reporting request for sensing capabilities, location data, data types, and data processing capabilities to a non-3GPP device.

[0388] After receiving the reporting request sent by Base Station 1, in addition to reporting its own data to Base Station 1, the UE also sends a reporting request to a non-3GPP device to instruct the non-3GPP device to report its sensing capabilities, location data, data types, and data processing capabilities.

[0389] S908: The non-3GPP device reports its sensing capabilities, location data, data types, and data processing capabilities to the UE.

[0390] The non-3GPP device responds to the reporting request sent by the UE and reports data to the UE.

[0391] S9c9: The UE reports its sensing capabilities, location data, data types, and data processing capabilities to Base Station 1.

[0392] The data reported by the UE to Base Station 1 includes the sensing capabilities, location data, data types, and data processing capabilities of both the UE itself and the non-3GPP device.

[0393] S910: Base Station 1 reports the sensing capabilities, location data, data types, and data processing capabilities of the UE and the non-3GPP device to the core network device.

[0394] The above S907 to S910 mainly involve Base Station 1 requesting other sensing devices (including the UE and non-3GPP devices) within the cell to report information such as sensing capabilities, location data, data types, and data processing capabilities, and reporting the collected data to the core network device. Due to the change in the priority of the sensing area, it is necessary to collect the reporting data of the sensing devices before reallocating the sensing resources.

[0395] S911: Base Station 1 sends a sensing resource allocation plan to the UE.

[0396] S912: The UE sends a sensing resource allocation plan to the non-3GPP device.

[0397] Among them, Base Station 1 can determine the sensing resource allocation plan based on the reporting data it collects and the priority list sent by the core network. This sensing resource allocation plan can be understood as the above-mentioned resource allocation result.

[0398] In the embodiments of the present application, the decision-making power for allocating sensing resources is decentralized from the core network device to the base station side. Since the base station is closer to the sensing device, in scenarios where rapid response is required for sensing tasks, the base station can complete resource scheduling decisions more quickly, reduce control latency, and improve the overall timeliness of the system. The base station can dynamically adjust the resource allocation result according to local sensing environment changes (such as sensing device status updates, local network load fluctuations, etc.), without relying on the centralized scheduling of the core network device every time, thereby improving the adaptability of resource allocation to local dynamic changes. The base station allocates sensing resources to high-priority sensing regions preferentially according to the priority levels of the sensing regions issued by the core network device, ensuring that key tasks receive sufficient sensing support and improving the system service quality and task completion rate. Moreover, sinking the resource allocation function to the base station side helps reduce the control signaling interaction volume of the core network device and avoid bottleneck problems caused by centralized processing.

[0399] To further facilitate the understanding of the priority update and sensing resource allocation methods in the embodiments of the present application, the following will be described in conjunction with a specific scenario: Figure 10 It is a schematic diagram of a scenario for updating the priority levels of sensing regions provided by the embodiments of the present application.

[0400] Exemplarily, as Figure 10 shown, according to the comprehensive calculation of various influencing factors (such as weather, temperature, humidity, location information, time information, etc.) in the core network device, sensing region 1 and sensing region 2 are updated to high-priority sensing regions. Such high-priority sensing regions may be: the area near a dam during the flood season, the area near a railway during the Spring Festival travel rush, the highway area near a hillside during heavy rainfall, etc.

[0401] The core network device updates that sensing region 2 is a high-priority sensing region to base stations 1 and 2. Since sensing region 1 is not within the service scope of base stations 1 and 2, the information within sensing region 1 is not updated to base stations 1 and 2.

[0402] Base stations 1 and 2 control the sensing devices (including but not limited to base stations, UEs, non-3GPP devices) within their service scopes to preferentially sense high-priority sensing region 2 and the sensing targets therein according to the priority levels of the sensing regions within their service scopes. The new targets sensed in sensing region 2 (represented by white circles in the figure) will be updated as high-priority targets in the core network device. In this example, the advantage of updating the priority levels of sensing regions is that it can timely sense the unperceived targets within high-priority sensing regions and the surrounding environment information of high-priority targets.

[0403] In summary, in the embodiments of the present application, the priority list of perception areas can be updated in real time based on environmental information (such as season, location, weather, temperature, etc.). Furthermore, by delegating authority from the core network, perception resources are dynamically allocated by the base station, enabling flexible scheduling of perception tasks. The priority order can be dynamically adjusted based on actual needs to accommodate the diverse perception requirements newly added in integrated communication and perception scenarios. Furthermore, the embodiments of the present application are applicable to a wide range of scenarios, including but not limited to emergency services, commercial centers, medical care, disaster relief, natural disasters, smart transportation, disaster detection, and other scenarios with perception needs.

[0404] Figure 11 It is a structural diagram of a priority determination device provided in an embodiment of the present application.

[0405] For example, Figure 11 As shown, the priority determination device 1100 includes: An acquisition module 1101 is configured to acquire sensing results corresponding to N sensing areas, wherein the N sensing areas are obtained by dividing the sensing range of at least one sensing device, and N is an integer greater than or equal to 2. The determination module 1102 is configured to obtain priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; wherein the priorities are used to allocate resources to the sensing services corresponding to the N sensing areas.

[0406] In a possible implementation, the determination module 1102 is specifically configured to obtain priorities of the N sensing areas according to the sensing results and sensing service requirements corresponding to the N sensing areas.

[0407] In one possible implementation, the determination module 1102 is specifically used to obtain the priorities of the N perception areas based on the perception results, perception business requirements and target information corresponding to the N perception areas; wherein the target information includes the spatiotemporal feature information and / or regional attribute information of the N perception areas; the regional attribute information includes the urgency of the perception area and / or the preset importance level of the perception area.

[0408] In one possible implementation, the determination module includes: a priority score determination module, which is used to determine the priority scores corresponding to N perception areas based on the perception results, perception business requirements and target information corresponding to the N perception areas; a priority determination module, which is used to obtain the priorities of the N perception areas based on the priority scores corresponding to the N perception areas; wherein the priority score is positively correlated with the priority.

[0409] In a possible implementation, the N sensing regions include a first sensing region; the priority score determination module is specifically configured to: determine a first target score corresponding to the first sensing region according to the sensing result corresponding to the first sensing region; determine a second target score corresponding to the first sensing region according to the sensing service requirements of the first sensing region; determine a third target score corresponding to the first sensing region according to the target information of the first sensing region; and determine the priority score corresponding to the first sensing region according to the first target score, the second target score, and the third target score.

[0410] In a possible implementation, the sensing result includes multiple sensing environmental features, and the first target score is obtained by weighting the scores corresponding to each sensing environmental feature among the multiple sensing environmental features with the coefficients corresponding to each sensing environmental feature.

[0411] In a possible implementation, the score corresponding to each sensing environmental feature is positively correlated with the severity of the environment characterized by each sensing environmental feature.

[0412] In a possible implementation, the sensing service requirements include multiple service requirement parameters, and the second target score is obtained by weighting the scores corresponding to each service requirement parameter among the multiple service requirement parameters with the coefficients corresponding to each service requirement parameter.

[0413] In a possible implementation, the multiple service requirement parameters include QoS parameters, and the score corresponding to the QoS parameter is negatively correlated with the level of the QoS parameter.

[0414] In a possible implementation, the third target score is obtained based on a fourth target score corresponding to spatio-temporal feature information and / or a fifth target score corresponding to regional attribute information.

[0415] In a possible implementation, the spatio-temporal feature information includes time information and the location information of the space where the first sensing region is located, and the fourth target score is obtained by weighting the product of the score corresponding to the time information and the coefficient corresponding to the time information, and the product of the score corresponding to the location information and the coefficient corresponding to the location information.

[0416] In a possible implementation, the score corresponding to the time information is determined based on the time period in which the time information is located; when the time period in which the time information is located is the preset peak time period corresponding to the first sensing region, the score corresponding to the time information is the first preset score; when the time period in which the time information is located is the preset flat peak time period corresponding to the first sensing region, the score corresponding to the time information is the second preset score; when the time period in which the time information is located is the preset low valley time period corresponding to the first sensing region, the score corresponding to the time information is the third preset score; wherein, the first preset score is greater than the second preset score, and the second preset score is greater than the third preset score.

[0417] In a possible implementation, the score corresponding to the location information is negatively correlated with the target distance, where the target distance is the distance between the first sensing area and the preset sensing area.

[0418] In a possible implementation, the area attribute information includes the urgency level of the first sensing area and the preset importance level. The fifth target score is obtained by weighting the product of the score corresponding to the urgency level and the coefficient corresponding to the urgency level, and the product of the score corresponding to the preset importance level and the coefficient corresponding to the preset importance level.

[0419] In a possible implementation, the score corresponding to the urgency level is positively correlated with the urgency level, and the score corresponding to the preset importance level is positively correlated with the preset importance level.

[0420] In a possible implementation, the determining module is specifically configured to: if a preset trigger condition is satisfied, obtain the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; where the preset trigger condition includes at least one of the following: the current time is the preset periodic update time, the sensing environment characteristics of the sensing area satisfy the preset conditions, the predicted probability of a preset event occurring in the sensing area is greater than the preset threshold, or an instruction to update the priority is received from the user.

[0421] In a possible implementation, the priority determination device further includes: a first sending module, configured to send a first message to the base station; where the first message carries first information, and the first information is used to indicate the priority information of M sensing areas among the N sensing areas, and M is an integer less than or equal to N.

[0422] In a possible implementation, the priority determination device further includes: an allocation module, configured to obtain a resource allocation result according to the priorities of the N sensing areas; where the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the N sensing areas; a second sending module, configured to send a second message to the base station; where the second message carries the resource allocation result.

[0423] In a possible implementation, the resource allocation result further includes: information about the target sensing device; where the target sensing device is determined based on the reported data of at least one sensing device, and the reported data includes the location data and device attribute data of at least one sensing device.

[0424] In a possible implementation, the first information is the information about the priority levels of the M sensing areas, and the priority levels of the M sensing areas are obtained based on the priority scores of the respective sensing areas in the M sensing areas.

[0425] In a possible implementation, the first message further includes: identification information of M sensing regions and / or the validity period of the priority; wherein, the identification information is determined based on the center of the sensing region and the offset of the boundary of the sensing region relative to the center.

[0426] In a possible implementation, the sensing result includes the sensed environmental characteristics.

[0427] In a possible implementation, the sensing device includes at least one of the following: a non-3GPP device, a UE, and a base station.

[0428] Figure 12 It is a schematic structural diagram of a resource allocation device provided by an embodiment of the present application.

[0429] Exemplarily, as Figure 12 shown, the resource allocation device 1200 includes: A receiving module 1201, configured to receive a first message sent by a core network device; wherein, the first message carries first information, and the first information is used to indicate the priority information of M sensing regions among N sensing regions. The N sensing regions are obtained by dividing the sensing ranges of at least one sensing device. The priorities of the N sensing regions are obtained by the core network device according to the sensing results corresponding to the N sensing regions. M is an integer less than or equal to N, and N is an integer greater than or equal to 2; An allocation module 1202, configured to obtain a resource allocation result according to the first information; wherein, the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the M sensing regions.

[0430] In a possible implementation, the first information is the information of the priority levels of the M sensing regions, and the priority levels of the M sensing regions are based on the priority scores of the respective sensing regions in the M sensing regions.

[0431] In a possible implementation, the allocation module 1202 is specifically configured to: determine L groups of sensing regions according to the priority levels of the M sensing regions; wherein, the priority levels of the respective sensing regions in each group of sensing regions are the same, and L is an integer greater than or equal to 1; and obtain a resource allocation result according to the sorting of the priority levels of the L groups of sensing regions, the total amount of resources to be allocated, and the resource requirements of the sensing services corresponding to the respective sensing regions.

[0432] In a possible implementation, when the resource requirements of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are less than or equal to the total amount of resources to be allocated, the resource allocation amounts of the sensing services corresponding to the respective sensing regions in the i-th group of sensing regions are determined based on the resource requirements of the sensing services corresponding to the respective sensing regions; wherein, 1 ≤ i ≤ L.

[0433] In a possible implementation, when the resource requirement of the sensing service corresponding to each sensing area in the i-th group of sensing areas is greater than the total amount of resources to be allocated, the resource allocation amount of the sensing service corresponding to each sensing area in the i-th group of sensing areas is determined based on the total number of sensing areas in the i-th group of sensing areas and the total amount of resources to be allocated.

[0434] In a possible implementation, the resource allocation result further includes: information of the target sensing device; wherein, the target sensing device is determined based on the reported data of at least one sensing device, and the reported data includes the location data and device attribute data of at least one sensing device.

[0435] In a possible implementation, the above resource allocation device further includes: a third sending module, configured to send a data reporting request to at least one sensing device after receiving a first message sent by a core network device; a receiving and uploading module, configured to receive the reported data of at least one sensing device and send the reported data to the core network device; wherein, the reported data includes the location data and device attribute data of at least one sensing device.

[0436] Figure 13 It is a schematic structural diagram of a core network device provided by an embodiment of the present application.

[0437] Exemplarily, as Figure 13 shown, the core network device 1300 includes: A memory 1301, configured to store executable program codes; A processor 1302, configured to call and run the executable program codes from the memory 1301, so that the core network device executes the above priority determination method.

[0438] Figure 14 It is a schematic structural diagram of a base station provided by an embodiment of the present application.

[0439] Exemplarily, as Figure 14 shown, the base station 1400 includes: A memory 1401, configured to store executable program codes; A processor 1402, configured to call and run the executable program codes from the memory 1401, so that the base station executes the above resource allocation method.

[0440] It should be noted that in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B. The "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0441] It should be understood that the division of the methods, situations, categories, and embodiments in the embodiments of the present application is only for the convenience of description and should not constitute a special limitation. The features in various methods, categories, situations, and embodiments can be combined with each other without conflict.

[0442] It should also be understood that in the description of this embodiment, unless otherwise specified, the meaning of "a plurality of" is two or more than two. In various embodiments of the present application, the size of the serial numbers of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0443] It should also be noted that in the embodiments of the present application, "preset", "fixed value", etc. can be implemented by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in an electronic device. The present application does not limit its specific implementation method.

[0444] It should be noted that all relevant contents of each step involved in the above method embodiment can be cited in the function description of the corresponding functional module and will not be repeated here.

[0445] This embodiment can divide the functional modules of the core network device or the base station according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative and is only a logical function division. There can be other division methods in actual implementation.

[0446] The core network device provided in this embodiment is used to execute the above priority determination method, so the same effect as the above implementation method can be achieved. The base station provided in this embodiment is used to execute the above resource allocation method, so the same effect as the above implementation method can be achieved.

[0447] In the case of adopting integrated units, the core network device or the base station may include a processing module, a storage module, and a communication module. Among them, the processing module may be used to control and manage the actions of the core network device or the base station, the storage module may be used to support the core network device or the base station to execute stored program codes and data, etc., and the communication module may be used to support the communication between the core network device or the base station and other devices.

[0448] Among them, the processing module may be a processor or a controller. It may implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination including one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, and so on. The storage module may be a memory. The communication module may specifically be a device that interacts with other electronic devices, such as a radio frequency circuit, a Bluetooth chip, a Wi-Fi chip, etc.

[0449] This embodiment also provides a computer-readable storage medium. Computer instructions are stored in the computer-readable storage medium. When the computer instructions run on the core network device, the core network device is caused to execute the above-mentioned related method steps to implement the priority determination method in the above embodiment. When the computer instructions run on the base station, the base station is caused to execute the above-mentioned related method steps to implement the resource allocation method in the above embodiment.

[0450] This embodiment also provides a computer program product. When the computer program product runs on a computer, the computer is caused to execute the above-mentioned related steps to implement the priority determination method or the resource allocation method in the above embodiment.

[0451] In addition, an embodiment of the present application also provides a device, which may specifically be a chip, a component, or a module. The device may include a processor and a memory connected to each other. Among them, the memory is used to store computer execution instructions. When the device runs, the processor may execute the computer execution instructions stored in the memory so that the chip executes the priority determination method or the resource allocation method in the above-mentioned method embodiments.

[0452] Among them, the core network device, the base station, the computer-readable storage medium, the computer program product, or the chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved by them may refer to the beneficial effects in the corresponding method provided above, and will not be elaborated here.

[0453] Through the description of the above embodiments, those skilled in the art can understand that for the convenience and brevity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0454] In several embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0455] The units described as separate components may or may not be physically separated. The components displayed as units may be one physical unit or multiple physical units, that is, they can be located in one place or distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0456] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0457] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods in various embodiments of the present application. The foregoing storage medium includes: USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other various media that can store program codes.

[0458] The above content is only the specific implementation manner of this application, but the specific implementation manner of this application is not limited thereto. The protection scope of this application shall be subject to the protection scope of the claims. Any person skilled in the art within the technical scope disclosed by the claims of this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A method for determining priority, characterized in that, Applied to core network devices, including: Obtain the sensing results corresponding to N sensing regions; wherein, the N sensing regions are obtained by dividing the sensing ranges of at least one sensing device, and N is an integer greater than or equal to 2; Obtain the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions; wherein, the priorities are used for resource allocation for the sensing services corresponding to the N sensing regions.

2. The method according to claim 1, wherein The obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions includes: Obtain the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements.

3. The method according to claim 2, wherein The obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions and the sensing service requirements includes: Obtain the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions, the sensing service requirements and the target information; wherein, the target information includes the spatio-temporal feature information and / or the regional attribute information of the N sensing regions; the regional attribute information includes the urgency degree of the sensing region and / or the preset importance level of the sensing region.

4. The method according to claim 3, wherein The obtaining the priorities of the N sensing regions according to the sensing results corresponding to the N sensing regions, the sensing service requirements and the target information includes: Determine the priority scores corresponding to the N sensing regions according to the sensing results corresponding to the N sensing regions, the sensing service requirements and the target information; Obtain the priorities of the N sensing regions according to the priority scores corresponding to the N sensing regions; wherein, the priority scores are positively correlated with the priorities.

5. The method according to claim 4, wherein The N sensing regions include a first sensing region; The determining the priority scores corresponding to the N sensing regions according to the sensing results corresponding to the N sensing regions, the sensing service requirements and the target information includes: Determine the first target score corresponding to the first sensing region according to the sensing result corresponding to the first sensing region; Determine the second target score corresponding to the first sensing region according to the sensing service requirements of the first sensing region; Determine the third target score corresponding to the first sensing region according to the target information of the first sensing region; ​ 6. The method according to claim 5, wherein ​ 7. The method according to claim 6, wherein ​ 8. The method according to claim 5, wherein ​ 9. The method according to claim 8, wherein ​ 10. The method according to claim 5, wherein The third target score is obtained based on the fourth target score corresponding to the spatio-temporal feature information and / or the fifth target score corresponding to the regional attribute information.

11. The method according to claim 10, wherein The spatio-temporal feature information includes time information and location information of the space where the first sensing area is located. The fourth target score is obtained by weighting the product of the score corresponding to the time information and the coefficient corresponding to the time information, and the product of the score corresponding to the location information and the coefficient corresponding to the location information.

12. The method according to claim 11, wherein The score corresponding to the time information is determined based on the time period in which the time information is located. When the time period in which the time information is located is the preset peak period corresponding to the first sensing area, the score corresponding to the time information is the first preset score. When the time period in which the time information is located is the preset flat peak period corresponding to the first sensing area, the score corresponding to the time information is the second preset score. When the time period in which the time information is located is the preset trough period corresponding to the first sensing area, the score corresponding to the time information is the third preset score. Among them, the first preset score is greater than the second preset score, and the second preset score is greater than the third preset score.

13. The method according to claim 11, characterized in that, The score corresponding to the location information is negatively correlated with the target distance, and the target distance is the distance between the first sensing area and the preset sensing area.

14. The method according to claim 10, wherein The regional attribute information includes the urgency level and the preset importance level of the first sensing area. The fifth target score is obtained by weighting the product of the score corresponding to the urgency level and the coefficient corresponding to the urgency level, and the product of the score corresponding to the preset importance level and the coefficient corresponding to the preset importance level.

15. The method according to claim 14, wherein The score corresponding to the urgency level is positively correlated with the urgency level, and the score corresponding to the preset importance level is positively correlated with the preset importance level.

16. The method according to claim 1, characterized in that, Obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas includes: If a preset trigger condition is satisfied, obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas; Among them, the preset trigger condition includes at least one of the following: The current time is the preset periodic update time, the sensing environment characteristics of the sensing area meet the preset conditions, The predicted probability of a preset event occurring in the sensing area is greater than a preset threshold, or an instruction to update the priority is received from the user.

17. The method according to claim 1, wherein After obtaining the priorities of the N sensing areas according to the sensing results corresponding to the N sensing areas, the method further includes: Sending a first message to the base station; Among them, the first message carries first information, and the first information is used to indicate the information of the priorities of M sensing areas among the N sensing areas, where M is an integer less than or equal to N.

18. The method according to claim 1, characterized in that, The method further includes: Obtaining a resource allocation result according to the priorities of the N sensing areas; where the resource allocation result includes the resource allocation amounts of the sensing services corresponding to the N sensing areas; Sending a second message to the base station; where the second message carries the resource allocation result.

19. The method according to claim 18, characterized in that, The resource allocation result further includes: Information of the target perception device; Among them, the target perception device is determined based on the reported data of the at least one perception device, and the reported data includes the location data and device attribute data of the at least one perception device.

20. The method according to claim 17, wherein The first information is the information of the priority levels of the M perception regions, and the priority levels of the M perception regions are obtained based on the priority scores of the respective perception regions in the M perception regions.

21. The method according to claim 17, characterized in that The first message further includes: The identification information of the M perception regions and / or the validity period of the priority; Among them, the identification information is determined based on the center of the perception region and the offset of the boundary of the perception region relative to the center.

22. The method according to any one of claims 1 to 21, characterized in that, The perception result includes perception environment characteristics.

23. The method according to any one of claims 1 to 21, characterized in that, The perception device includes at least one of the following: Non-3GPP device, UE, base station.

24. A resource allocation method, characterized in that, Applied to a base station, including: Receiving a first message sent by a core network device; among them, the first message carries first information, and the first information is used to indicate the information of the priorities of M perception regions among N perception regions. The N perception regions are obtained by dividing the perception ranges of at least one perception device. The priorities of the N perception regions are obtained by the core network device according to the perception results corresponding to the N perception regions. M is an integer less than or equal to N, and N is an integer greater than or equal to 2; Obtaining a resource allocation result according to the first information; among them, the resource allocation result includes the resource allocation amounts of the perception services corresponding to the M perception regions.

25. The method according to claim 24, wherein The first information is the information of the priority levels of the M perception regions, and the priority levels of the M perception regions are obtained based on the priority scores of the respective perception regions in the M perception regions.

26. The method according to claim 25, wherein The obtaining the resource allocation result according to the first information includes: Determining L groups of perception regions according to the priority levels of the M perception regions; among them, the priority levels of the respective perception regions in each group of perception regions are the same, and L is an integer greater than or equal to 1; Obtaining the resource allocation result according to the sorting of the priority levels of the L groups of perception regions, the total amount of resources to be allocated, and the resource requirements of the perception services corresponding to the respective perception regions.

27. The method according to claim 26, wherein When the resource requirements of the perception services corresponding to the respective perception regions in the i-th group of perception regions are less than or equal to the total amount of resources to be allocated, the resource allocation amounts of the perception services corresponding to the respective perception regions in the i-th group of perception regions are determined based on the resource requirements of the perception services corresponding to the respective perception regions; where 1 ≤ i ≤ L.

28. The method according to claim 26, wherein When the resource requirements of the perception services corresponding to the respective perception regions in the i-th group of perception regions are greater than the total amount of resources to be allocated, the resource allocation amounts of the perception services corresponding to the respective perception regions in the i-th group of perception regions are determined based on the total number of perception regions in the i-th group of perception regions and the total amount of resources to be allocated.

29. The method according to claim 24, characterized in that, The resource allocation result further includes: Information of the target perception device; Among them, the target perception device is determined based on the reported data of the at least one perception device, and the reported data includes the location data and device attribute data of the at least one perception device.

30. The method according to claim 24, wherein After receiving the first message sent by the core network device, the method further includes: Sending a data reporting request to the at least one perception device; Receiving the reported data of the at least one perception device and sending the reported data to the core network device; Among them, the reported data includes the location data and device attribute data of the at least one perception device.

31. A core network device, characterized in that, It includes: A memory for storing executable program code; A processor for calling and running the executable program code from the memory, so that the core network device executes the method according to any one of claims 1 to 23.

32. A base station, characterized in that, It includes: A memory for storing executable program code; A processor for calling and running the executable program code from the memory, so that the base station executes the method according to any one of claims 24 to 30.

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