Perception communication method, perception communication system, electronic equipment and storage medium

Abstract

The invention provides a sensing communication method, a sensing communication system, electronic equipment and a storage medium, relates to the technical field of communication, and is used for improving the sensing data processing accuracy of the sensing communication system and the data transmission convenience between modules. The method is applied to a core network, and the core network comprises a sensing functional entity and a sensing server. The method comprises the following steps: receiving first sensing data sent by a sensing node; and processing the first perception data through the perception function entity based on the first artificial intelligence model trained by the perception server to obtain a first perception result.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular, to a sensing communication method, a sensing communication system, an electronic device, and a storage medium. Background Art

[0002] With the wide application of the fifth-generation (5G) communication system, while meeting the needs of individual users, 5G is gradually penetrating into various industries and fields of society, thus realizing the upgrade from consumption to industrial applications. Among them, the sensing communication system based on artificial intelligence utilizes the existing network architecture and communication waveforms, and uses the existing communication devices to implement sensing applications, which promotes the development of innovative application services and is an important research direction of 5G.

[0003] Due to the more complex and diverse network scale, service types, and terminal devices of the future sixth-generation (6G) communication system, the communication range is expanded, the connections are complex, the information interaction increases, and the sensing requirements are high. Therefore, the intelligent management of communication and sensing is an inevitable trend. The existing sensing communication systems are basically systems with separated communication and sensing, highly dependent on expert communication experience and sensing optimization algorithms. With the increase of parameters, network maintenance and optimization are difficult. Summary of the Invention

[0004] Embodiments of the present disclosure provide a sensing communication method, a sensing communication system, an electronic device, and a storage medium, which are used to improve the accuracy of the sensing communication system in processing sensing data and the convenience of data transmission between modules.

[0005] In a first aspect, a sensing communication method is provided, which is applied to a core network. The core network includes a sensing function entity and a sensing server. The method includes:

[0006] Receiving first sensing data sent by a sensing node;

[0007] Based on a first artificial intelligence model trained by the sensing server, processing the first sensing data through the sensing function entity to obtain a first sensing result.

[0008] Based on the sensing communication method provided by the embodiments of the present disclosure, by adding a sensing function entity and a sensing server to the existing core network architecture, the core network itself also has the ability of model training and intelligent computing. At the same time, processing the sensing data based on the first artificial intelligence model trained by the sensing server to obtain the first sensing result makes the acquisition method of the first sensing result more accurate and less prone to errors.

[0009] In a second aspect, a sensing communication method is provided, which is applied to a sensing node. The method includes:

[0010] Receive a first sensing control instruction sent by a sensing function entity in the core network, where the first sensing control instruction is used to instruct a sensing node to perform sensing detection;

[0011] In response to the first sensing control instruction, send a first sensing signal and obtain first sensing data;

[0012] Send the first sensing data to the sensing function entity in the core network.

[0013] Based on the sensing communication method provided in the embodiments of the present disclosure, through the communication interaction between the sensing node and the core network, the sensing node performs sensing detection according to the first sensing control instruction sent by the core network, making the sensing detection more timely. At the same time, the sensing node sends a first sensing signal based on the first sensing control instruction, thereby obtaining first sensing data, and the acquisition method of the first sensing data is also more accurate. Finally, the obtained first sensing data is sent to the sensing function entity in the core network, realizing data communication and sharing among various modules.

[0014] In a third aspect, a sensing communication method is provided, which is applied to an intelligent agent in an edge network. The method includes:

[0015] Obtain second sensing data from a sensing node;

[0016] Process the second sensing data based on a second artificial intelligence model to obtain a second sensing result.

[0017] Based on the sensing communication method provided in the embodiments of the present disclosure, through the communication interaction between the sensing node and the intelligent agent, the intelligent agent obtains second sensing data from the sensing node. At the same time, the second sensing data is processed based on the second artificial intelligence model trained by the sensing server in the intelligent agent, diversifying the processing method of the sensing data, improving the accuracy of the sensing result, and making the credibility of the second sensing result higher.

[0018] In a fourth aspect, a sensing communication system is provided. The sensing communication system includes: a sensing node and a core network, and the core network includes a sensing function entity and a sensing server;

[0019] The sensing node is used to provide first sensing data to the core network;

[0020] The core network is used to process the first sensing data through the sensing function entity based on the first artificial intelligence model trained by the sensing server to obtain a first sensing result.

[0021] In a fifth aspect, a communication device is provided, including: a memory and a processor; the memory and the processor are coupled; the memory is used to store a computer program; when the processor executes the computer program, the sensing communication method of any of the above embodiments is implemented.

[0022] In a sixth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the perception communication method of any of the above embodiments is implemented.

[0023] In a seventh aspect, a computer program product is provided, which includes computer program instructions. When the computer program instructions are executed by a processor, the perception communication method of any of the above embodiments is implemented.

[0024] For the specific descriptions of the fourth to seventh aspects and their various implementation manners in the present disclosure, reference may be made to the detailed descriptions in the first, second, and third aspects and their various implementation manners; and for the beneficial effects of the fourth to seventh aspects and their various implementation manners, reference may be made to the analysis of the beneficial effects in the first, second, and third aspects and their various implementation manners, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the present disclosure, the drawings required to be used in some embodiments of the present disclosure will be briefly introduced below. Obviously, the drawings in the following description are only the drawings of some embodiments of the present disclosure, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0026] Figure 1 FIG. is a schematic diagram of a perception scenario provided for some embodiments of the present disclosure;

[0027] Figure 2 FIG. is a schematic diagram of the architecture of a perception communication system provided for some embodiments of the present disclosure;

[0028] Figure 3 FIG. is a schematic flowchart of a model training method provided for some embodiments of the present disclosure;

[0029] Figure 4 FIG. is a schematic flowchart of a perception communication method provided for some embodiments of the present disclosure;

[0030] Figure 5 FIG. is a schematic diagram of a range-Doppler map provided for some embodiments of the present disclosure;

[0031] Figure 6 FIG. is a schematic diagram of the correspondence of feature data provided for some embodiments of the present disclosure;

[0032] Figure 7 FIG. is a schematic diagram of the architecture of a convolutional neural network provided for some embodiments of the present disclosure;

[0033] Figure 8A schematic structural diagram of a long short-term memory neural network model provided by some embodiments of the present disclosure;

[0034] Figure 9 Another schematic structural diagram of a long short-term memory neural network model provided by some embodiments of the present disclosure;

[0035] Figure 10 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0036] Figure 11 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0037] Figure 12 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0038] Figure 13 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0039] Figure 14 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0040] Figure 15 A schematic flowchart of another perception communication method provided by some embodiments of the present disclosure;

[0041] Figure 16 A schematic structural diagram of a perception communication device provided by some embodiments of the present disclosure;

[0042] Figure 17 Another schematic structural diagram of a perception communication device provided by some embodiments of the present disclosure;

[0043] Figure 18 Another schematic structural diagram of a perception communication device provided by some embodiments of the present disclosure;

[0044] Figure 19 A schematic structural diagram of a communication device provided by some embodiments of the present disclosure. Detailed implementation manners

[0045] Next, the technical solutions in the present disclosure will be clearly and completely described in conjunction with the accompanying drawings in the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0046] It should be noted that in this disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0047] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0048] In the description of this disclosure, unless otherwise specified, " / " means "or". For example, A / B may represent A or B. The "and / or" herein is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, "at least one" means one or more, and "a plurality" means two or more.

[0049] The method provided by the embodiments of this disclosure can be applied to various communication systems. For example, the communication system can be a 5G communication system, a Wi-Fi system, a 3GPP-related communication system, a future evolved communication system (such as 6G, etc.), or a system integrating multiple systems, etc. The embodiments of this disclosure are not limited thereto.

[0050] With the wide application of 5G, while meeting the needs of individual users, 5G is gradually penetrating into various industries and fields of society, thus realizing the upgrade from consumption to industrial applications. Among them, the integration of communication and sensing is based on the existing network architecture and communication waveforms, and uses existing communication devices to implement sensing applications, promoting the development of innovative application services and is an important research direction of 5G. As Figure 1 shown, it is a schematic diagram of a sensing scenario provided by the embodiments of this disclosure. The sensing system in the sensing base station 1 can perform base station sensing on the drone 2, the human body 3, the vehicle 4, and the building 5. The sensing system in the satellite 6 can perform satellite sensing on the aircraft 7 (illustrated as an airplane). The sensing system in the drone 2 can perform drone sensing on the human body. The sensing terminal in the vehicle 4 can perform terminal sensing on the human body 3.

[0051] In some embodiments, the terminal for performing terminal perception may also be a device with full-duplex transmission capability. The terminal may be a mobile phone, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and so on. The embodiments of the present disclosure do not limit the application scenarios. The terminal may sometimes also be referred to as a user, user equipment (UE), access terminal, UE unit, UE station, mobile station, mobile unit, remote station, remote terminal, mobile device, UE terminal, wireless communication device, UE agent, or UE device, etc., and the embodiments of the present disclosure do not limit this.

[0052] In the currently applied perception functions, due to the more complex and diverse network scale, service types, and terminal devices in future mobile communication systems, such as the 6th generation (6G) communication system, the communication range expands, the connections are complex, the information interaction increases, and the perception requirements are high. Therefore, intelligent management of communication and sensing is an inevitable trend. However, the existing perception communication systems are basically systems where communication and sensing are separated, highly dependent on expert communication experience and perception optimization algorithms. As the parameters increase, network maintenance and optimization become difficult.

[0053] To address the above technical problems, the embodiments of the present disclosure provide a perception communication method. The idea is as follows: Add a perception function entity and a perception server to the existing core network architecture, enabling the core network itself to have the ability of intelligent computing. Through the interaction between the core network, the intelligent agents located in the edge network, and the perception nodes, the sharing of perception data and the fusion of artificial intelligence models are realized, reducing the complexity of artificial model training. At the same time, based on the trained artificial intelligence model, the perception data is processed to obtain perception results, making the acquisition method of perception results more accurate, less prone to errors, and facilitating the opening of data to other modules or third parties in the system to achieve real-time communication among all modules in the perception communication system and complete the perception tasks.

[0054] See Figure 2 , which is a schematic diagram of the architecture of a perception communication system provided by the embodiments of the present disclosure. As Figure 2As shown in the figure, the system includes: a sensing node and a core network.

[0055] In some embodiments, the sensing node is configured to provide first sensing data to the core network.

[0056] In some embodiments, the core network includes a sensing function entity (SF) and a sensing server (SS), and is configured to process the first sensing data through the sensing function entity based on the first artificial intelligence model trained by the sensing server to obtain a first sensing result.

[0057] In some embodiments, the sensing communication system further includes an agent located in the edge network.

[0058] In some embodiments, the sensing node is configured to provide second sensing data to the agent.

[0059] In some embodiments, the agent includes a sensing function entity and a sensing server, and is configured to process the second sensing data based on the second artificial intelligence model to obtain a second sensing result.

[0060] Specifically, the sensing server in the agent is configured to train the second artificial intelligence model, and the sensing function entity in the agent is configured to process the second sensing data based on the second artificial intelligence model to obtain a second sensing result.

[0061] In some embodiments, the agent is further configured to send at least one of the following to the core network: a sensing service request message, a sensing service response message, second sensing data, a second sensing result, and the second artificial intelligence model.

[0062] In some embodiments, the core network is further configured to send at least one of the following to the agent: a sensing service request message, a sensing service response message, first sensing data, a first sensing result, and the first artificial intelligence model.

[0063] In some embodiments, the core network is further configured to send a first sensing control instruction to the sensing node in response to the sensing service request message.

[0064] The sensing service request message is sent by the agent located in the edge network to the core network; or, the sensing service request message is generated by an application function network element in the core network, and the first sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0065] Specifically, the core network is further configured to send the first sensing control instruction to the sensing node through the sensing function entity in the core network in response to the sensing service request message.

[0066] In some embodiments, the core network is further configured to receive at least one of the following sent by an agent located in the edge network: a sensing service request message, a sensing service response message, second sensing data, a second sensing result, and a second artificial intelligence model.

[0067] The second sensing result is obtained by a sensing functional entity in the agent based on the second artificial intelligence model trained by a sensing server in the agent to process the second sensing data.

[0068] In some embodiments, the sensing functional entity is configured with at least one of the following functions: a sensing control function, a sensing management function, an artificial intelligence function, a sensing processing function, and a data preprocessing function.

[0069] In some embodiments, the sensing control function includes at least one of the following: selection of sensing nodes, management of sensing resources, management of sensing time, management of artificial intelligence applications, and management of information transmission.

[0070] In some embodiments, the sensing management function includes at least one of the following: obtaining sensing service requirements, authentication of sensing service requirements, determining sensing tasks based on sensing service requirements, artificial intelligence model fusion, sensing data fusion, and sensing result fusion.

[0071] Specifically, when the sensing management function obtains the first artificial intelligence model sent by the sensing server of the core network and the second artificial intelligence model sent by the agent located in the edge network, it fuses the first artificial intelligence model and the second artificial intelligence model to obtain a more complete and accurate artificial intelligence model.

[0072] Specifically, when the sensing management function obtains the first sensing data sent by the sensing node and the second sensing data sent by the agent located in the edge network, it fuses the first sensing data and the second sensing data to obtain more complete and accurate sensing data.

[0073] Specifically, when the sensing management function obtains the first sensing result and the second sensing result sent by the agent located in the edge network, it fuses the first sensing result and the second sensing result to obtain a more complete and accurate sensing result.

[0074] In some embodiments, the artificial intelligence function includes at least one of the following: training an artificial intelligence model and using an artificial intelligence model.

[0075] In some embodiments, the sensing processing function is used for object detection, false alarm suppression, object association, trajectory tracking, object recognition, and trajectory correction of sensing data.

[0076] In some embodiments, a data preprocessing function is used to extract or normalize feature data from the sensed data.

[0077] In some embodiments, the sensing functional entity is further configured to obtain a trained artificial intelligence model from the sensing server.

[0078] In some embodiments, the sensing functional entity is further configured to process the sensed data based on the artificial intelligence model to obtain a sensing result.

[0079] The sensing result is used for at least one of the following: target detection, trajectory tracking, trajectory correction, target association, and target recognition.

[0080] Optionally, the sensing functional entity may further send a sensing control instruction to the sensing node, and the sensing control instruction is used to select the sensing node to perform the sensing task.

[0081] Optionally, the sensing functional entity may further send at least one of the following to the sensing server: first indication information for indicating whether the sensing server performs model training, and second indication information for indicating the training period.

[0082] Specifically, the sensing control function in the sensing functional entity sends at least one of the following to the sensing server: first indication information for indicating whether the sensing server performs model training, and second indication information for indicating the training period.

[0083] In some embodiments, the core network is further configured to fuse the obtained artificial intelligence model to reduce the training complexity of the large model.

[0084] In some embodiments, the core network further includes at least one of the following network elements: Network Slice Selection Function (NSSF), Network Exposure Function (NEF), Network Repository Function (NRF), Policy Control Function (PCF), Unified Data Management (UDM), Application Function (AF), Authentication Server Function (AUSF), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), or other possible Network Functions (NFs).

[0085] In some embodiments, the NSSF virtualizes multiple end-to-end networks on a common hardware basis through slicing technology. Each network has different NFs and adapts to different types of service requirements.

[0086] In some embodiments, the NEF is located between the 5G core network and external third-party application functions and is responsible for managing the externally exposed network data. All external applications that want to access the internal data of the 5G core network must go through the NEF.

[0087] In some embodiments, the NRF is used for the registration, management, and status detection of NFs, realizing the automated management of all NFs. When each NF starts, it must register with the NRF to provide services. The registration information includes the type, address, and service list of the NF, etc.

[0088] In some embodiments, the PCF is used to manage network behavior using a unified policy framework and cooperate with the user information in the Unified Data Repository (UDR) to execute relevant policies.

[0089] In some embodiments, the UDM is used to manage user identities, subscription data, and authentication data, and is also used to manage the registration of the user's serving network elements.

[0090] In some embodiments, AF refers to various services at the application layer, which can be applications within the operator or third-party AFs (such as video servers, game servers). If it is an AF within the operator, it is in a trusted domain with other NFs and can directly interact and access other NFs. However, third-party AFs are not in the trusted domain and need to access other NFs through the NEF.

[0091] In some embodiments, the AUSF is used to receive a request from the AMF to authenticate a user equipment (UE), request a key from the UDM, and then forward the key sent by the UDM to the AMF for authentication processing.

[0092] In some embodiments, the AMF is used to provide a session management message transmission channel for the UE and the SMF, and provide authentication, authorization functions, and a core network control plane access point for the terminal and the radio when the user accesses.

[0093] In some embodiments, the SMF is used to support customized mobility management solutions together with the AMF, such as mobile initiated connection only (MICO) or radio access network (RAN) enhancements.

[0094] In some embodiments, the UPF is mainly used to send UE service data to the data network (DN), identify data and services, and execute actions and policies, etc.

[0095] In some embodiments, a perception server is used to train an artificial intelligence model, and the artificial intelligence model is used to process perception data to obtain perception results.

[0096] Optionally, the training of the artificial intelligence model can be carried out in an offline training and online prediction manner, or in an online training and online prediction manner.

[0097] See Figure 3 , which is a schematic flowchart of a model training method provided by an embodiment of the present disclosure. As Figure 3 shown, taking the artificial intelligence model using the offline training and online prediction manner as an example, the training of the artificial intelligence model includes four stages: an initial stage, a training stage, a prediction stage, and a collection stage.

[0098] Among them, in the initial stage of artificial intelligence model training, an initial data set using simulation data or measured data according to the scenario can be stored in the perception server. The initial training model is obtained by performing initial training using the initial data set. In the training stage of artificial intelligence model training, when the training data accumulates to a certain amount, periodic artificial intelligence model training is performed. In the prediction stage of artificial intelligence model training, the perception functional entity performs real-time online prediction and transmits the preprocessed data with high confidence and the corresponding perception results to the perception server for training sample collection. In the collection stage of artificial intelligence model training, the perception server continuously collects data for periodic training of the artificial intelligence model.

[0099] In some embodiments, the online training and online prediction methods of the artificial intelligence model specifically refer to the artificial intelligence function in the perception functional entity, which can perform some lightweight online training and online prediction. And transmit the preprocessed data with high confidence and the corresponding perception results to the perception server for training sample collection.

[0100] In some embodiments, the perception server is further configured to send the trained artificial intelligence model to the perception functional entity.

[0101] In some embodiments, the perception server is further configured with at least one of the following functions: service control function, data preprocessing function, data storage function, data opening function.

[0102] Among them, the service control function includes at least one of the following: management of artificial intelligence applications, management of model training time, management of data opening function, management of information transmission; the data preprocessing function is used to extract or normalize the feature data of the perception data; the data storage function is used to store the perception data with relatively high confidence as the data set for subsequent periodic training of the artificial intelligence model; the data opening function is used to externally provide at least one of the perception data, perception results, and artificial intelligence models.

[0103] In some embodiments, the management of artificial intelligence applications is used to determine whether the perception server performs training of the artificial intelligence model; the management of model training time is used to determine the training period of the artificial intelligence model; the management of the data opening function is used to determine whether to open and share the perception data and perception results; the management of information transmission is used to determine whether to transmit the open and shared perception data and perception results.

[0104] Optionally, the perception server is further configured to send at least one of the following to the core network: perception data, artificial intelligence model.

[0105] In some embodiments, the perception node is further configured to receive a first perception control instruction sent by the perception functional entity in the core network.

[0106] Among them, the first sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0107] In some embodiments, the sensing node is further configured to send a first sensing signal and obtain first sensing data in response to the first sensing control instruction.

[0108] Specifically, after receiving the first sensing control instruction sent by the sensing function entity, the sensing node sends a first sensing signal according to the sensing method indicated by the first sensing control instruction, performs the first sensing task, and obtains first sensing data.

[0109] It should be noted that the first sensing data can be measurement data at the physical layer or sensing result data.

[0110] In some embodiments, the sensing node is further configured to send the first sensing data to the sensing function entity in the core network.

[0111] Specifically, after obtaining the sensing data, the sensing node sends the sensing data to the sensing function entity.

[0112] In some embodiments, the sensing node is further configured to receive a second sensing control instruction sent by an agent located in the edge network.

[0113] Among them, the second sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0114] In some embodiments, the sensing node is further configured to send a second sensing signal and obtain second sensing data in response to the second sensing control instruction.

[0115] Specifically, after receiving the second sensing control instruction sent by the agent located in the edge network, the sensing node sends a second sensing signal according to the sensing method indicated by the second sensing control instruction, performs the second sensing task, and obtains second sensing data.

[0116] In some embodiments, the sensing node is further configured to send the second sensing data to the agent.

[0117] Optionally, referring to Figure 2 , there may be multiple sensing nodes, and the sensing nodes communicate with each other through the Uu interface (U is the user to network interface, and u is Universal), and each sensing node can be a (generation node B, gNB) base station or a UE.

[0118] In some embodiments, the sensed data or sensing results processed by the UE are transmitted to the sensing functional entity or agent through the Uu interface and the gNB base station.

[0119] In some embodiments, the sensing node is further configured to perform signal processing, communication signal processing, and artificial training and prediction of the artificial intelligence model.

[0120] It should be noted that the system architecture and application scenarios described in the embodiments of the present disclosure are for more clearly explaining the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those of ordinary skill in the art can know that with the evolution of the system architecture and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.

[0121] Next, a sensing communication method provided by an embodiment of the present disclosure will be described with reference to the accompanying drawings of the specification.

[0122] See Figure 4 , which is a schematic flowchart of a sensing communication method provided by an embodiment of the present disclosure, applied to the core network. The core network includes a sensing functional entity and a sensing server. As Figure 4 shown, the method includes the following steps:

[0123] S101. The core network receives the first sensed data sent by the sensing node.

[0124] In some embodiments, when the core network receives a sensing service request message generated by an application function network element in the core network, in response to the sensing service request message, the sensing functional entity in the core network sends a first sensing control instruction to the sensing node.

[0125] Among them, the first sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0126] Further, when the sensing node receives the first sensing control instruction sent by the sensing functional entity in the core network, in response to the first sensing control instruction, it sends a first sensing signal and obtains the first sensed data.

[0127] In some embodiments, when the sensing node obtains the first sensed data, the sensing node sends the first sensed data to the core network, and the core network receives the first sensed data sent by the sensing node.

[0128] S102. The core network processes the first sensed data through the sensing functional entity based on the first artificial intelligence model trained by the sensing server to obtain a first sensing result.

[0129] In some embodiments, after the core network obtains the first sensing data sent by the sensing node, the sensing function entity in the core network may obtain the trained first artificial intelligence model from the sensing server, and process the first sensing data through the first artificial intelligence model to obtain the first sensing result.

[0130] Specifically, after the core network sends a sensing service request message to the sensing function entity, the sensing function entity receives the sensing service request message sent by the core network and obtains the trained artificial intelligence model from the sensing server.

[0131] Optionally, the sensing service request message may carry the sensing service type, sensing area, sensing report content, and sensing report type.

[0132] Among them, the sensing service type may be drone sensing, vehicle sensing, or sensing of other targets. The sensing area may be within a range of 1000 meters centered on the sensing node or other ranges. The sensing report content may include the position and speed of the target. The sensing report type may be sensing result reporting.

[0133] It should be noted that the target may also be other people, vehicles, or other objects with moving trajectories or state changes. The present disclosure does not limit the type of the target.

[0134] In some embodiments, in response to the sensing service request message, the sensing function entity selects a sensing node for sensing detection and obtains the first sensing data obtained by the sensing node during the sensing detection process.

[0135] In some embodiments, the above-mentioned sensing function entity, in response to the sensing service request message, selects a sensing node for sensing detection and obtains the first sensing data obtained by the sensing node during the sensing detection process, which may be specifically implemented as the following steps:

[0136] A1. The sensing function entity determines a sensing task based on the sensing service request message.

[0137] Specifically, in response to the sensing service request message, the sensing function entity authenticates the sensing service request message through the sensing management function in the sensing function entity. After the authentication of the sensing service request message passes, the sensing management function determines a sensing task based on the sensing service request message (such as sensing drones in the sensing area, mainly sensing the position and speed of the drones).

[0138] A2. The sensing function entity selects a sensing node to execute the sensing task and sends a first sensing control instruction to the sensing node.

[0139] Among them, the first sensing control instruction is used to instruct the sensing node to perform a sensing task, and the first sensing control instruction includes sensing configuration information, which is used to configure at least one of sensing resources, sensing time, and sensing signals.

[0140] Specifically, after the sensing task is determined, the sensing management function in the sensing function entity sends a sensing service request message to the sensing control function. The sensing control function in the sensing function entity selects the sensing node and sensing method for performing the sensing task according to the sensing service request message, and sends the first sensing control instruction to the sensing node.

[0141] Optionally, the sensing node can be a base station, and the sensing method can be A transmit and A receive or A transmit and B receive.

[0142] It should be noted that A transmit and A receive means sending a sensing service request message through sensing node A (or base station A) and receiving sensing data through sensing node A (or base station A). A transmit and B receive means sending a sensing service request message through sensing node A (or base station A) and receiving sensing data through sensing node B (or base station B).

[0143] Furthermore, the sensing node starts to perform the sensing task, conducts sensing detection on the sensing area, obtains the first sensing data obtained during the sensing detection process, and sends the first sensing data to the core network.

[0144] In some embodiments, the sensing node receives the sensing signal reflected by the target according to the integrated communication and sensing time-frequency resources indicated by the first sensing control function, performs baseband processing on the sensing signal, and obtains the first sensing data of the target.

[0145] Optionally, the first sensing data can be range-doppler (RD) map data of the target.

[0146] A3. The core network receives the first sensing data sent by the sensing node.

[0147] In some embodiments, after the sensing node obtains the first sensing data obtained during the sensing detection process, it sends the first sensing data (such as RD map data) to the core network, and the core network receives the first sensing data.

[0148] Furthermore, after the core network receives the first sensing data sent by the sensing node, the sensing function entity in the core network processes the first sensing data based on the first artificial intelligence model to obtain the first sensing result.

[0149] It can be understood that, based on the perception communication method provided in the embodiments of the present disclosure, by adding a perception function entity and a perception server to the existing core network architecture, the core network itself also has the ability of model training and intelligent computing. At the same time, the perception data is processed by the first artificial intelligence model trained by the perception server to obtain the first perception result, making the acquisition method of the first perception result more accurate and less prone to errors.

[0150] In some embodiments, the first artificial intelligence model can be trained by the perception server. After the perception function entity obtains the first perception data sent by the perception node, the perception processing function in the perception function entity preprocesses the first perception data and then sends the first perception data to the perception server. The perception server trains the first artificial intelligence model based on the preprocessed first perception data.

[0151] In some embodiments, the perception server trains the first artificial intelligence model in response to the first indication information sent by the perception control function in the perception function entity for indicating the perception server to perform model training.

[0152] Refer to Figure 5 , Figure 5 A schematic diagram of a range-Doppler map provided by the embodiments of the present disclosure, as Figure 5 shown, P (m，n) represents the power of the grid where the m-th row and the n-th column are located in the RD map.

[0153] In some embodiments, at the initial stage of training the first artificial intelligence model, the perception server uses the initial test data to train the first artificial intelligence model. When the RD map of the perception signal is determined, according to the reference unit in the RD map, the unit to be detected and the protection unit of the RD data are extracted from the RD map, which is illustrated as the power data of 9 grids, forming the feature data of [P0, P1, P2, P3, P4, P5, P6, P7, P8, P9]. This set of feature data is used as a sample data, and its corresponding label is 0 or 1, where 0 represents no target and 1 represents having a target. The corresponding relationship is as Figure 6 shown, Figure 6 is a schematic diagram of the corresponding relationship of the feature data provided by the embodiments of the present disclosure.

[0154] In some embodiments, the present disclosure uses a convolutional neural (CNN) network to train the model. Figure 7 The present disclosure provides an architecture schematic diagram of a convolutional neural network, as Figure 7 shown, Conv L j (P1, P1) where L j is the number of convolutional kernels, and (P1, P1) represents the size of the convolutional kernel.

[0155] It should be noted that the network training is not limited to the CNN network, and other networks can also be used, such as the residual network (ResNet), etc.

[0156] In some embodiments, in addition to the input layer and the fully connected layer, the CNN network structure may also have J layers of networks in the middle, and each layer of network includes convolution, activation, and pooling operations.

[0157] It should be noted that the CNN network model is a type of feedforward neural network that includes convolutional calculations and has a deep structure. The CNN network model has the ability of feature learning and can perform translation-invariant classification on the input information according to its hierarchical structure.

[0158] Optionally, the activation function of the CNN network uses the linear rectification function (ReLU), as shown in formula (1):

[0159] f x =max (0，x) Formula (1)

[0160] It should be noted that the ReLU function is also called the rectified linear unit, which is a commonly used activation function in artificial neural networks and usually refers to non-linear functions represented by the ramp function and its variants.

[0161] Optionally, the activation function of the fully connected layer uses the Sigmoid function, as shown in formula (2):

[0162]

[0163] It should be noted that the Sigmoid function is a common S-shaped function in biology, also known as the S-shaped growth curve. In information science, due to its properties such as monotonic increase and monotonic increase of the inverse function, the Sigmoid function is often used as the activation function of neural networks to map variables to the interval (0, 1).

[0164] Optionally, the loss function used by the fully connected layer is the cross-entropy function, as shown in formula (3):

[0165]

[0166] Among them, O s is the size of the output, y i is the true value of the sample, is the predicted value output by the model.

[0167] It should be noted that the cross entropy function is mainly used to measure the difference information between two probability distributions. The meaning of cross entropy is the difficulty of text recognition using the model, or from the perspective of compression, how many bits are used to encode each word on average.

[0168] In some embodiments, the initial test data can be divided into 80% of the data as a training set and 20% of the data as a test set. The training set is used to train the first artificial intelligence model, and the test set is used to test the first artificial intelligence model.

[0169] In some embodiments, the perception server sends the trained first artificial intelligence model to the perception function entity, and the perception function entity processes the first perception data based on the first artificial intelligence model, extracts the data of the RD graph, and performs the above Figure 5 As shown, feature data [P0, P1, P2, P3, P4, P5, P6, P7, P8, P9] are extracted, and this feature data is used as the input of the first artificial intelligence model, and the online prediction output is no target (0) or with target (1).

[0170] In some embodiments, for characteristic data of a target, other sensing detection is performed on the target, such as direction of arrival (DOA) estimation, signal-to-noise ratio (SNR) estimation, speed estimation, power calculation, etc.

[0171] It should be noted that DOA estimation is a positioning technology that obtains the distance and direction information of the target by processing the received echo signal. SNR refers to the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference).

[0172] Furthermore, based on the first perception data of the above-mentioned perception detection, target detection, trajectory tracking, trajectory correction, target association, and target recognition are performed on the target.

[0173] It should be noted that if it is multi-sensory node perception, data fusion is performed among the multi-sensory nodes to obtain a more complete and accurate first perception result of target detection, trajectory tracking, trajectory correction, target association, and target recognition.

[0174] Optionally, the fusion of the first perception results among multiple perception nodes can be achieved by merging, optimizing, etc., or by using video.

[0175] In some embodiments, the perception function entity transmits a first perception result with a higher confidence level and a corresponding label to a perception server for data collection, and the perception server performs periodic model training on the first artificial intelligence model according to the second indication information sent by the perception control function.

[0176] In some embodiments, the sensing server sends at least one of first sensing data, a first sensing result, or a first artificial intelligence model to the core network.

[0177] In some embodiments, the sensing processing function in the sensing functional entity sends a sensing service response message to the sensing management function, and the sensing management function sends the sensing service response message to the core network and reports a first sensing result according to the sensing service request message.

[0178] In some embodiments, the sensing functional entity performs target detection, trajectory tracking, trajectory correction, target association, and target recognition based on information such as DOA estimation, SNR estimation, speed estimation, and power calculation reported by the sensing nodes, and obtains the moving trajectory of the target.

[0179] In some embodiments, in wireless communication, especially in high-frequency band wireless communication, wireless signals are extremely easy to be blocked, resulting in signal blind spots, thus causing the loss of the target's trajectory. At this time, the missing part of the trajectory can be predicted according to the known historical trajectory information of the target.

[0180] In some embodiments, at the initial stage of training the first artificial intelligence model, the sensing server uses initial test data to train the first artificial intelligence model, and the initial test data can also be the actual test trajectory data of the target.

[0181] Exemplarily, the trajectory point sequence of the target can be expressed as [(Lo1, La1, H1, V1),

[0182] (Lo2, La2, H2, V2),..., (Lo n , La n , H n , V n )].

[0183] Among them, Lo t represents the longitude of the sensed target trajectory point at the t-th moment, La t represents the latitude of the sensed target trajectory point at the t-th moment, H t represents the height of the sensed target trajectory point at the t-th moment, V t represents the speed of the sensed target at the trajectory point (Lo t , La t , H t ) at the t-th moment, and t = 1, 2,..., n.

[0184] In some embodiments, after obtaining the actual test trajectory data of the target, it is necessary to preprocess this actual test trajectory data and normalize the actual test trajectory data between the minimum value and the maximum value within a certain range to achieve the standardization of the training data.

[0185] In some embodiments, the trajectory data at adjacent times in the trajectory point sequence is differentiated, that is, x t = Lo t+1 - Lo t , y t = La t+1 - La t , z t = H t+1 - H t , v t = V t+1 - V t .

[0186] Furthermore, (n - 1) trajectory sequences are obtained, namely [(x1, y1, z1, v1), (x2, y2, z2, v2),..., (x n-1 , y n-1 , z n-1 , v n-1 )].

[0187] In some embodiments, using the sklearn tool in the machine learning library, the differentiated trajectory sequence is normalized with the MinMaxScaler() function, scaled to the range [-1, 1], and the output parameter scaler of the function is saved. A supervised sequence is constructed for the normalized data. Table 1 is a schematic diagram of a method for constructing a supervised sequence provided in some embodiments of the present disclosure. Taking n = 13 as an example, as shown in Table 1:

[0188] Table 1

[0189]

[0190] Exemplarily, if the constructed supervised sequence is [(x1, y1, z1, v1), (x2, y2, z2, v2),..., (x 12 , y 12 , z 12 , v 12 )], then the label of this supervised sequence is (x 13 , y 13 , z 13 , v 13 ); if the constructed supervised sequence is [(x2, y2, z2, v2), (x3, y3, z3, v3),..., (x 13 , y 13 , z13 , v 13 ), then the label of this supervision sequence is (x 14 , y 14 , z 14 , v 14 ); if the constructed supervision sequence is [(x3, y3, z3, v3), (x4, y4, z4, v4),..., (x 14 , y 14 , z 14 , v 14 ), then the label of this supervision sequence is (x 15 , y 15 , z 15 , v 15 ); if the constructed supervision sequence is [(x4, y4, z4, v4), (x5, y5, z5, v5),..., (x 15 , y 15 , z 15 , v 15 ), then the label of this supervision sequence is (x 16 , y 16 , z 16 , v 16 ); if the constructed supervision sequence is [(x t-12 , y t-12 , z t-12 , v t-12 ), (x t-11 , y t-11 , z t-11 , v t-11 ),..., (x t-1 , y t-1 , z t-1 , v t-1 ), then the label of this supervision sequence is (x t , y t , z t , v t ).

[0191] In some embodiments, the processed data set is split into a training data set and a test data set at a ratio of 80% and 20%. The training data set is used to train the first artificial intelligence model, and the test data set is used to evaluate the first artificial intelligence model.

[0192] In some embodiments, a long short-term memory neural network model (long short term memory, LSTM) can be constructed, and the network model parameters can be configured to train the first artificial intelligence model.

[0193] In some embodiments, the LSTM network model includes three gate information, namely, a forget gate, an input gate, and an output gate.Figure 8 This disclosure provides a schematic structural diagram of a long short-term memory neural network model, as Figure 8 shown. The forgetting gate mainly controls the proportion of data features of the previous layer of the network to be forgotten, that is, the input data features are weighted and summed with the previous sequence state and input to the forgetting gate to obtain the data feature output of the forgetting gate. Its calculation method is shown in formula (4):

[0194] f t =σ(W xf ·x t +W hf ·h t-1 +b f ) Formula (4)

[0195] where f t is the data feature output of the forgetting gate, W xf is the weighting coefficient of the forgetting gate for the current moment data, x t is the input data feature, W hf represents the weighting coefficient of the forgetting gate for the previous moment state, h t-1 is the previous sequence state, b f is the data bias constant of the forgetting gate, and σ(x) represents the sigmod activation function.

[0196] Furthermore, the main memory data at the current moment is obtained by combining the memory system data of the previous moment. The calculation method is shown in formula (5):

[0197] c t1 =c t-1 ⊙f t Formula (5)

[0198] where c t1 is the main memory data at the current moment, and c t-1 is the memory system data of the previous moment.

[0199] In some embodiments, the input gate mainly screens the effective features of the current input data and compensates and updates the control data features of the forgetting gate in combination with the current data. The data feature extraction method of the input gate is shown in formula (6):

[0200] i t =σ(W xi ·x t +W hi ·h t-1 +b i ) Formula (6)

[0201] where i t is the data feature output of the input gate, W xiis the weighting coefficient of the input gate for the data at the current moment, W hi is the weighting coefficient of the input gate for the state at the previous moment, b i is the data bias constant of the input gate.

[0202] Furthermore, the input data features will compensate the memory data features to a certain extent. It is necessary to screen the feature parameters of the input gate. The calculation method of the screening control coefficient is shown in formula (7):

[0203] c' t = tanh(W xc ·x t + W hc ·h t-1 + b c ) Formula (7)

[0204] Among them, c' t is the feature screening control coefficient of the input gate, W xc is the screening control weight coefficient of the input data at the current moment, W hc is the screening weight coefficient of the data state input at the previous moment, b c is the screening control data bias constant, and tanh(x) is the activation function.

[0205] Furthermore, the input gate data is screened and controlled to obtain the memory system data at the next moment. The calculation method is shown in formula (8):

[0206] c t2 = c' t ⊙ i t Formula (8)

[0207] Among them, c t2 is the memory system data at the next moment.

[0208] In some embodiments, the output value of the memory parameter at the current moment can be determined according to the main memory data at the current moment and the memory system data at the next moment. The calculation method is shown in formula (9):

[0209] c t = c t1 + c t2 Formula (9)

[0210] Among them, c t is the output value of the memory parameter at the current moment.

[0211] In some embodiments, the output gate mainly learns and trains the model according to the forgetting data features and the current data features, and its training method is shown in formula (10):

[0212] ht = σ(W xo · x t + W ho · h t-1 + b o ⊙ tanh(c t )) Equation (10)

[0213] where h t is the data output by the output gate, W xo is the weight coefficient of the data at the current moment in the output gate, W ho is the weight coefficient of the state data at the previous moment in the output gate, b o is the data bias constant in the output gate.

[0214] Optionally, the present disclosure may adopt an architecture of two-layer LSTM plus a fully connected layer. Figure 9 FIG. 29 is a schematic diagram of the architecture of a long short-term memory neural network model provided by an embodiment of the present disclosure. Table 2 shows a parameter configuration method for an architecture of two-layer LSTM plus a fully connected layer, as shown in Table 2:

[0215] Table 2

[0216] Parameter item Parameter value Number of LSTM layers 2 Number of hidden layers 64 Activation function ReLU Loss function MSE

[0217] Exemplarily, in combination with Figure 9 and Table 2, the number of hidden layers of the LSTM can be 64 layers, the activation function can be ReLU, and the loss function can be the mean square error function (MSE).

[0218] In some embodiments, after the first artificial intelligence model is trained, the perception server sends the trained first artificial intelligence model to the core network.

[0219] In some embodiments, after the perception functional entity receives the trained first artificial intelligence model, the perception functional entity preprocesses the actual test trajectory data (the preprocessing method is the same as the preprocessing method during training of the model), inputs the preprocessed data set into the first artificial intelligence model, and obtains predicted values (x t , y t , z t , v t ). According to the scaler saved during data preprocessing, inverse normalization is performed, and then (Lo n , La n , H n , V n ) is added to obtain the final predicted values (Lo n+1 , La n+1 , H n+1 , V n+1 ).

[0220] In some embodiments, the sensing functional entity transmits the actual test trajectory data with a relatively high confidence level and the corresponding labels to the sensing server for data collection.

[0221] In some embodiments, the sensing functional entity transmits the sensing results with a relatively high confidence level and the corresponding labels to the sensing server for data collection, and the sensing server performs periodic model training on the first artificial intelligence model according to the second indication information sent by the sensing control function for indicating the training period.

[0222] In some embodiments, the sensing processing function in the sensing functional entity sends the response information of the sensing service request message to the sensing management function, and the sensing management function sends the response information of the sensing service request message to the core network and reports the sensing results according to the sensing service request message, so as to display a complete running trajectory of the target.

[0223] See Figure 10 , Figure 10 which is a schematic flowchart of another sensing communication method provided by an embodiment of the present disclosure, applied to a sensing node, such as Figure 10 shown, and the method includes the following steps:

[0224] S201. The sensing node receives a first sensing control instruction sent by a sensing functional entity in the core network.

[0225] In some embodiments, when the core network receives a sensing service request message sent by an agent located in the edge network or a sensing service request message generated by an application function network element in the core network, in response to the sensing service request message, the sensing functional entity in the core network sends a first sensing control instruction to the sensing node.

[0226] The first sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0227] S202. The sensing node responds to the first sensing control instruction, sends a first sensing signal, and obtains first sensing data.

[0228] In some embodiments, when the sensing node receives the first sensing control instruction sent by the sensing functional entity in the core network, in response to the first sensing control instruction, it sends a first sensing signal and obtains first sensing data.

[0229] S203. The sensing node sends the first sensing data to the sensing functional entity in the core network.

[0230] In some embodiments, when the sensing node obtains the first sensing data, it sends the first sensing data to the sensing functional entity in the core network.

[0231] See Figure 11 , Figure 11 which is a schematic flowchart of another sensing communication method provided by an embodiment of the present disclosure, applied to a sensing node, as Figure 11 shown. The method includes the following steps:

[0232] S301. The sensing node receives a second sensing control instruction sent by an agent located in the edge network.

[0233] Among them, the second sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0234] In some embodiments, when the core network receives a sensing service request message sent by an agent located in the edge network, in response to the sensing service request message, a sensing function entity in the core network sends a second sensing control instruction to the sensing node.

[0235] S302. The sensing node responds to the second sensing control instruction, sends a second sensing signal, and obtains second sensing data.

[0236] In some embodiments, when the sensing node receives the second sensing control instruction sent by the sensing function entity in the core network, in response to the second sensing control instruction, it sends a second sensing signal and obtains second sensing data.

[0237] S303. The sensing node sends the second sensing data to the agent.

[0238] In some embodiments, when the sensing node obtains the second sensing data, it sends the second sensing data to the agent located in the edge network.

[0239] It should be noted that based on the sensing communication method provided by the embodiment of the present disclosure, through the communication interaction between the sensing node and the core network, the sensing node performs sensing detection according to the first sensing control instruction sent by the core network, making the sensing detection more timely. At the same time, the sensing node sends a first sensing signal based on the first sensing control instruction, thereby obtaining first sensing data, and the acquisition method of the first sensing data is also more accurate. Finally, the obtained first sensing data is sent to the sensing function entity in the core network, realizing data communication and sharing among various modules.

[0240] See Figure 12 , Figure 12 which is a schematic flowchart of another sensing communication method provided by an embodiment of the present disclosure, applied to an agent in the edge network, as Figure 12 shown. The method includes the following steps:

[0241] S401. The agent obtains second sensing data from the sensing node.

[0242] In some embodiments, when the agent needs to obtain second sensing data, the agent sends a second sensing control instruction to the sensing node.

[0243] The second sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0244] In some embodiments, when the sensing node receives the second sensing control instruction sent by the sensing function entity in the core network, in response to the second sensing control instruction, it sends a second sensing signal, obtains second sensing data, and sends the second sensing data to the agent, and the agent receives the second sensing data.

[0245] S402. The agent processes the second sensing data based on the second artificial intelligence model to obtain a second sensing result.

[0246] In some embodiments, when the agent receives the second sensing data sent by the sensing node, the agent can process the second sensing data based on the second artificial intelligence model trained by the sensing server in the agent to obtain a second sensing result.

[0247] It should be noted that the training method of the second artificial intelligence model is the same as that of the first artificial intelligence model in step S102 above, and will not be elaborated here.

[0248] In some embodiments, the agent can also send at least one of the following to the core network: a sensing service response message, second sensing data, a second sensing result, and a second artificial intelligence model.

[0249] In some embodiments, the agent can also receive at least one of the following sent by the core network: a sensing service request message, first sensing data, a first sensing result, and a first artificial intelligence model.

[0250] The first sensing result is obtained by processing the first sensing data based on the first artificial intelligence model.

[0251] It should be noted that based on the sensing communication method provided in the embodiments of the present disclosure, through the communication interaction between the sensing node and the agent, the agent obtains the second sensing data from the sensing node. At the same time, the second artificial intelligence model trained by the sensing server in the agent is used to process the second sensing data, which diversifies the processing method of the sensing data, improves the accuracy of the sensing result, and makes the credibility of the second sensing result higher.

[0252] In some embodiments, the above steps S101 - S102 can also be implemented through the process as Figure 13 shown, see Figure 13 , which is a schematic flowchart of another sensing communication method provided by the embodiments of the present disclosure. As Figure 13As shown in the figure, taking a drone as the target, the method includes the following steps:

[0253] S501. The core network sends a first sensing control instruction of the drone to the sensing management function in the sensing function entity.

[0254] Among them, the first sensing control instruction is used to indicate the sensing service requirements, and the sensing service type, sensing area, sensing report content, and sensing report type can be carried in the first sensing control instruction.

[0255] Among them, the sensing service type is drone sensing, the sensing area can be within a range of 1000 meters centered on the sensing node or other ranges, the sensing report content can include the position and speed of the drone, and the sensing report type can be sensing result reporting.

[0256] S502. After the sensing management function obtains the first sensing control instruction, it authenticates the first sensing control instruction.

[0257] S503. The sensing management function sends the first sensing control instruction passed through authentication to the sensing control function in the sensing function entity.

[0258] S504. After the sensing control function obtains the first sensing control instruction, it selects the sensing method, sensing node, and processing method of the sensing data for the drone according to the first sensing control instruction.

[0259] S505. The sensing control function sends the first sensing control instruction to the sensing node.

[0260] Among them, the first sensing control instruction is used to instruct the sensing node to perform a sensing task, and the sensing control instruction includes sensing configuration information, and the sensing configuration information is used to configure at least one of sensing resources, sensing time, and sensing signals.

[0261] S506. The sensing control function sends a first indication message for model training to the sensing server.

[0262] S507. When the sensing node receives the first sensing control instruction, according to the first sensing control instruction, it sends a first sensing signal for the drone, executes the first sensing task, and obtains the first sensing data of the drone.

[0263] S508. The sensing node sends the first sensing data of the drone to the sensing processing function of the sensing function entity.

[0264] S509. The sensing processing function performs preprocessing of the sensing data on the first sensing data.

[0265] Among them, the preprocessing of the sensing data includes extracting or normalizing the feature data of the first sensing data.

[0266] After the sensing server receives the first indication information for model training sent by the sensing control function, it trains the first artificial intelligence model and sends the trained first artificial intelligence model to the sensing processing function.

[0267] It should be noted that for the training of the first artificial intelligence model, please refer to the above step S102, which will not be elaborated here.

[0268] S511. The sensing processing function performs artificial intelligence prediction on the first artificial intelligence model to obtain the predicted first sensing result.

[0269] S512. The sensing processing function sends the first sensing result to the sensing server for storage.

[0270] S513. The sensing server performs periodic training on the first artificial intelligence model based on the second indication information for indicating the training period sent by the sensing control function and the sensing result.

[0271] S514. After obtaining the first sensing result, the sensing processing function in the sensing functional entity sends a sensing service response message to the sensing management function.

[0272] S515. The sensing management function sends the sensing service response message to the core network, and the core network obtains the first sensing result of the unmanned aerial vehicle.

[0273] Among them, the first sensing result may include the position and speed of the unmanned aerial vehicle, showing a complete movement trajectory of the unmanned aerial vehicle.

[0274] In some embodiments, the above steps S201 - S202 can also be implemented through the process as Figure 14 shown in, see Figure 14 , which is a schematic flowchart of another sensing communication method provided by the embodiments of the present disclosure. As Figure 14 shown, taking the target as an unmanned aerial vehicle as an example, this method includes the following steps:

[0275] S601. The application function of the core network sends the first sensing control instruction of the unmanned aerial vehicle to the sensing management function in the sensing functional entity.

[0276] Among them, the first sensing control instruction is used to indicate the sensing service requirements, and the first sensing control instruction may carry the sensing service type, sensing area, sensing report content, and sensing report type.

[0277] Among them, the perception service type is drone perception. The perception area can be within a range of 1000 meters centered on the perception node or other ranges. The perception report content can include the position and speed of the drone, and the perception report type can be the perception result report.

[0278] S602. After receiving the first perception control instruction, the perception management function authenticates the first perception control instruction.

[0279] S603. The perception management function sends the first perception control instruction that passes the authentication to the perception control function in the perception function entity.

[0280] S604. The perception control function selects the perception method, perception node, and processing method of perception data according to the first perception control instruction.

[0281] S605. The perception control function sends the first perception control instruction to the perception node.

[0282] S606. The perception node executes the perception task according to the first perception control instruction.

[0283] Specifically, when the perception node receives the first perception control instruction, according to the first perception control instruction, it sends the first perception signal for the drone, executes the first perception task, and obtains the first perception data of the drone.

[0284] S607. The perception node sends the first perception data to the perception management function.

[0285] S608. The perception node trains and predicts the first artificial intelligence model based on the first perception data to obtain the first perception result.

[0286] S609. The perception node sends the first artificial intelligence model and the first perception result to the perception management function.

[0287] S610. The perception management function performs the fusion of artificial intelligence models, the fusion of perception results, and the fusion of perception data.

[0288] S611. The perception management function sends a perception service response message to the application function of the core network.

[0289] In some embodiments, the above steps S301 - step S302 and steps S401 - step S402 can also be implemented through a process as Figure 15 shown, see Figure 15 , which is a schematic flowchart of another perception communication method provided by the embodiments of the present disclosure. As Figure 15 shown, taking the target as a drone as an example, the method includes the following steps:

[0290] S701. The application function of the core network sends a second sensing control instruction to the intelligent agent.

[0291] Among them, the second sensing control instruction is used to indicate the sensing service requirements, and the sensing service type, sensing area, sensing reporting content, and sensing reporting type can be carried in the second sensing control instruction.

[0292] Among them, the sensing service type is drone sensing, the sensing area can be within a range of 1000 meters centered on the sensing node or other ranges, the sensing reporting content can include the position and speed of the drone, and the sensing reporting type can be sensing result reporting.

[0293] S702. The intelligent agent receives the second sensing control instruction and authenticates the second sensing control instruction.

[0294] Specifically, the sensing function entity in the intelligent agent authenticates the second sensing control instruction.

[0295] S703. The intelligent agent sends the second sensing control instruction passed through authentication to the sensing node.

[0296] S704. The sensing node executes the sensing task according to the second sensing control instruction, obtains the second sensing data, and sends the second sensing data to the intelligent agent.

[0297] Specifically, when the sensing node receives the second sensing control instruction, it sends a second sensing signal to the drone according to the second sensing control instruction, executes the second sensing task, and obtains the second sensing data of the drone.

[0298] S705. The intelligent agent trains and predicts the second artificial intelligence model based on the second sensing data.

[0299] Specifically, the intelligent agent trains the second artificial intelligence model through the sensing server in the intelligent agent, and predicts the trained second artificial intelligence model through the sensing function entity in the intelligent agent to obtain the second sensing result.

[0300] S706. The intelligent agent sends the sensing service response message, the second sensing result, and the second artificial intelligence model to the core network.

[0301] In some embodiments, when the core network receives the second sensing result and the second artificial intelligence model, the sensing management function in the core network performs the fusion of the artificial intelligence model, the fusion of the sensing results, and the fusion of the sensing data.

[0302] The above mainly introduced the solution of the embodiments of the present disclosure from the perspective of methods. It can be understood that in order to implement the above functions, the sensing communication device includes at least one of the corresponding hardware structures and software modules for performing each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the examples described in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments of the present disclosure.

[0303] It can be understood that in order to implement the above functions, the sensing communication device includes the corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of the examples described in the embodiments of the present disclosure, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0304] The embodiments of the present disclosure can divide the functional modules of the sensing communication device according to the above method embodiments. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one functional module. The above integrated module can be implemented in the form of hardware or software. It should be noted that the division of modules in the embodiments of the present disclosure is illustrative, only a logical function division, and there can be other division methods in actual implementation. The following takes the example of dividing each functional module corresponding to each function for illustration.

[0305] Figure 16 It is a schematic structural diagram of a sensing communication device provided by the embodiments of the present disclosure. The sensing communication device is applied to the core network and can execute the sensing communication method provided by the above method embodiments. As Figure 16 shown, the sensing communication device 200 includes: a receiving module 201, a processing module 202, and a sending module 203.

[0306] In some embodiments, the receiving module 201 is configured to receive the first sensing data sent by the sensing node.

[0307] In some embodiments, the processing module 202 is configured to process the first sensing data through the sensing functional entity based on the first artificial intelligence model trained by the sensing server to obtain a first sensing result.

[0308] In some embodiments, the sending module 203 is configured to send a first sensing control instruction to the sensing node in response to a sensing service request message, where the first sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0309] In some embodiments, the sensing service request message is sent by an agent located in the edge network to the core network; alternatively, the sensing service request message is generated by an application function network element in the core network.

[0310] In some embodiments, the sending module 203 is further configured to send at least one of the following to an agent located in the edge network: a sensing service request message, a sensing service response message, the first sensing data, the first sensing result, and the first artificial intelligence model.

[0311] In some embodiments, the receiving module 201 is further configured to receive at least one of the following sent by an agent located in the edge network: a sensing service request message, a sensing service response message, the second sensing data, the second sensing result, and the second artificial intelligence model.

[0312] In some embodiments, the second sensing result is obtained by the agent processing the second sensing data based on the second artificial intelligence model.

[0313] In some embodiments, the sensing server is configured with at least one of the following functions: a service control function, a data preprocessing function, a data storage function, and a data opening function.

[0314] In some embodiments, the service control function includes at least one of the following: management of artificial intelligence applications, management of model training time, management of the data opening function, and management of information transmission.

[0315] In some embodiments, the data opening function is used to externally provide at least one of sensing data, sensing results, and artificial intelligence models.

[0316] Figure 17 It is a schematic structural diagram of a sensing communication device provided by an embodiment of the present disclosure. The sensing communication device is applied to a sensing node and can execute the sensing communication method provided by the above method embodiment. As Figure 17 shown, the sensing communication device 300 includes: a receiving module 301 and a sending module 302.

[0317] In some embodiments, a receiving module 301 is configured to receive a first sensing control instruction sent by a sensing function entity in a core network, where the first sensing control instruction is used to instruct a sensing node to perform sensing detection.

[0318] In some embodiments, a sending module 302 is configured to send a first sensing signal and obtain first sensing data in response to the first sensing control instruction.

[0319] In some embodiments, the sending module 302 is further configured to send the first sensing data to a sensing function entity in the core network.

[0320] In some embodiments, the receiving module 301 is further configured to receive a second sensing control instruction sent by an agent located in an edge network, where the second sensing control instruction is used to instruct a sensing node to perform sensing detection.

[0321] In some embodiments, the sending module 302 is further configured to send a second sensing signal and obtain second sensing data in response to the second sensing control instruction.

[0322] In some embodiments, the sending module 302 is further configured to send the second sensing data to the agent.

[0323] Figure 18 It is a schematic structural diagram of another sensing communication device provided by an embodiment of the present disclosure. The sensing communication device is applied to an agent in an edge network and can execute the sensing communication method provided by the above method embodiment. As Figure 18 shown, the sensing communication device 400 includes: an acquisition module 401, a processing module 402, and a sending module 403.

[0324] In some embodiments, the acquisition module 401 is configured to obtain second sensing data from a sensing node.

[0325] In some embodiments, the processing module 402 is configured to process the second sensing data based on a second artificial intelligence model to obtain a second sensing result.

[0326] In some embodiments, the sending module 403 is configured to send a second sensing control instruction to a sensing node, where the second sensing control instruction is used to instruct the sensing node to perform sensing detection.

[0327] In some embodiments, the sending module 403 is further configured to send at least one of the following to the core network: a sensing service response message, second sensing data, a second sensing result, and a second artificial intelligence model.

[0328] In some embodiments, the acquisition module 401 is further configured to receive at least one of the following sent by the core network: a sensing service request message, first sensing data, a first sensing result, and a first artificial intelligence model.

[0329] In some embodiments, the first perception result is obtained by processing the first perception data based on the first artificial intelligence model.

[0330] In the case where the functions of the above integrated modules are implemented in the form of hardware, the embodiments of the present disclosure provide a possible structure of the communication device involved in the above embodiments. As Figure 19 shown, the communication device 500 includes: a processor 502, and a bus 504. Optionally, the communication device 500 may further include a memory 501; optionally, the communication device 500 may further include a communication interface 503.

[0331] In some embodiments, the processor 502 may be a device that implements or executes various exemplary logical blocks, modules, and circuits described in connection with the embodiments of the present disclosure. The processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute various exemplary logical blocks, modules, and circuits described in connection with the embodiments of the present disclosure. The processor 502 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0332] In some embodiments, the communication interface 503 is used to connect to other devices through a communication network. The communication network may be an Ethernet, a radio access network, a wireless local area network (WLAN), etc.

[0333] In some embodiments, the memory 501 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium, or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0334] As a possible implementation, the memory 501 can exist independently of the processor 502. The memory 501 can be connected to the processor 502 via a bus 504 and is used to store instructions or program codes. When the processor 502 calls and executes the instructions or program codes stored in the memory 501, the perception communication method provided by the embodiments of the present disclosure can be implemented.

[0335] In another possible implementation, the memory 501 can also be integrated with the processor 502.

[0336] In some embodiments, the bus 504 can be an extended industry standard architecture (EISA) bus or the like. The bus 504 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 19 only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0337] Some embodiments of the present disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium). Computer program instructions are stored in the computer-readable storage medium. When the computer program instructions run on a computer, the computer is caused to execute the perception communication method in any one of the above embodiments.

[0338] Exemplarily, the above computer-readable storage medium can include, but is not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes, etc.), optical discs (e.g., Compact Disks (CDs), Digital Versatile Disks (DVDs), etc.), smart cards, and flash memory devices (e.g., Erasable Programmable Read-Only Memories (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in the present disclosure can represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" can include, but is not limited to, wireless channels and various other media that can store, contain, and / or carry instructions and / or data.

[0339] Embodiments of the present disclosure provide a computer program product containing instructions. When the computer program product runs on a computer, the computer is caused to execute the perception communication method in any one of the above embodiments.

[0340] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed by the present disclosure should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A perception communication method, characterized in that, Applied to the core network, the core network includes a sensing function entity and a sensing server; the method includes: Receiving first sensing data sent by a sensing node; Based on the first artificial intelligence model completed by training of the sensing server, processing the first sensing data through the sensing function entity to obtain a first sensing result.

2. The method according to claim 1, wherein Before receiving the first sensing data sent by the sensing node, the method further includes: Responding to a sensing service request message, sending a first sensing control instruction to the sensing node, where the first sensing control instruction is used to instruct the sensing node to perform sensing detection.

3. The method according to claim 2, wherein The sensing service request message is sent by an intelligent agent located in the edge network to the core network; or, the sensing service request message is generated by an application function network element in the core network.

4. The method according to claim 1, characterized in that, The method further includes: Sending at least one of the following to an intelligent agent located in the edge network: a sensing service request message, a sensing service response message, the first sensing data, the first sensing result, the first artificial intelligence model.

5. The method according to claim 1, wherein The method further includes: Receiving at least one of the following sent by an intelligent agent located in the edge network: a sensing service request message, a sensing service response message, second sensing data, a second sensing result, a second artificial intelligence model.

6. The method according to claim 5, wherein The second sensing result is obtained by the intelligent agent processing the second sensing data based on the second artificial intelligence model.

7. The method according to claim 1, characterized in that, The sensing function entity is configured with at least one of the following functions: a sensing control function, a sensing management function, an artificial intelligence function, a sensing processing function, a data preprocessing function.

8. The method according to claim 7, characterized in that, The sensing control function includes at least one of the following: selection of sensing nodes, management of sensing resources, management of sensing time, management of artificial intelligence applications, management of information transmission.

9. The method according to claim 7, wherein The sensing management function includes at least one of the following: obtaining sensing service requirements, authentication of the sensing service requirements, determining sensing tasks based on the sensing service requirements, artificial intelligence model fusion, sensing data fusion, sensing result fusion.

10. The method according to claim 7, characterized in that, The artificial intelligence function includes at least one of the following: training an artificial intelligence model, using the artificial intelligence model.

11. The method according to claim 1, characterized in that, The sensing server is configured with at least one of the following functions: a service control function, a data preprocessing function, a data storage function, a data opening function.

12. The method according to claim 11, wherein The service control function includes at least one of the following: management of artificial intelligence applications, management of model training time, management of the data opening function, management of information transmission.

13. The method according to claim 11, wherein The data opening function is used to externally provide at least one of sensing data, sensing results, and artificial intelligence models.

14. A perception communication method, characterized in that, Applied to a sensing node, the method includes: Receiving a first sensing control instruction sent by a sensing function entity in the core network, where the first sensing control instruction is used to instruct the sensing node to perform sensing detection; Responding to the first sensing control instruction, sending a first sensing signal, and obtaining first sensing data; Sending the first sensing data to the sensing function entity in the core network.

15. The method according to claim 14, wherein The method further includes: Receive a second sensing control instruction sent by an agent in the edge network, where the second sensing control instruction is used to instruct the sensing node to perform sensing detection; In response to the second sensing control instruction, send a second sensing signal and obtain second sensing data; Send the second sensing data to the agent.

16. A perception communication method, characterized in that, Applied to an agent in the edge network, the method includes: Obtain second sensing data from the sensing node; Process the second sensing data based on a second artificial intelligence model to obtain a second sensing result.

17. The method according to claim 16, wherein The method further includes: Send a second sensing control instruction to the sensing node, where the second sensing control instruction is used to instruct the sensing node to perform sensing detection.

18. The method according to claim 16, wherein The method further includes: Send at least one of the following to the core network: a sensing service response message, second sensing data, a second sensing result, a second artificial intelligence model.

19. The method according to claim 16, characterized in that, The method further includes: Receive at least one of the following sent by the core network: a sensing service request message, first sensing data, a first sensing result, a first artificial intelligence model.

20. The method according to claim 19, wherein The first sensing result is obtained by processing the first sensing data based on the first artificial intelligence model.

21. A perception communication system, characterized in that, The sensing communication system includes: a sensing node and a core network, where the core network includes a sensing function entity and a sensing server; The sensing node is used to provide first sensing data to the core network; The core network is used to process the first sensing data through the sensing function entity based on the first artificial intelligence model trained by the sensing server to obtain a first sensing result.

22. The system according to claim 21, wherein The sensing communication system further includes an agent in the edge network; The sensing node is further used to provide second sensing data to the agent; The agent is used to process the second sensing data based on a second artificial intelligence model to obtain a second sensing result.

23. The system according to claim 22, wherein, The agent is further used to send at least one of the following to the core network: a sensing service request message, a sensing service response message, the second sensing data, the second sensing result, the second artificial intelligence model.

24. The system according to claim 22, wherein The core network is further used to send at least one of the following to the agent: a sensing service request message, a sensing service response message, the first sensing data, the first sensing result, the first artificial intelligence model.

25. A communication device, characterized in that, Includes: A memory and a processor; The memory and the processor are coupled; The memory is used to store instructions executable by the processor; When the processor executes the instructions, it executes the method according to any one of claims 1-13, or the method according to claim 14 or 15, or the method according to any one of claims 16-20.

26. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and when the computer instructions run on the computer, the computer is caused to execute the method according to any one of claims 1-13, or the method according to claim 14 or 15, or the method according to any one of claims 16-20.

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