Perception data transmission method and device

Abstract

The invention relates to the technical field of communication, in particular to a sensing data transmission method and device, and aims to improve the flexibility of compression processing of sensing data, obtain higher compression benefits and reduce the data volume of the sensing data. The method comprises the steps that a first communication device generates compressed data and first indication information, the first indication information indicates categories of multiple pieces of sensing data, and the compressed data is obtained by compressing the multiple pieces of sensing data according to compression modes corresponding to the categories of the multiple pieces of sensing data; wherein the perception data of the same category in the multiple perception data corresponds to the same compression mode in the corresponding relation between the categories of different perception data and different compression modes; and transmitting the compressed data and the first indication information to the second communication device.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technologies, and in particular, to a method and apparatus for transmitting sensing data. Background Art

[0002] With the increasing richness of wireless communication application scenarios, wireless communication can be applied to more new scenarios, such as sensing, point cloud, imaging, environmental reconstruction, etc. For example, in environmental reconstruction, multiple terminal devices can scan the environment and send the obtained sensing data to a base station, and the base station can construct a complete environmental map based on the received sensing data.

[0003] Since a large amount of sensing data transmission is involved in scenarios such as environmental reconstruction, how to compress the sensing data and reduce the data volume of the sensing data is a problem worthy of attention. Summary of the Invention

[0004] The present application provides a method and apparatus for transmitting sensing data, aiming to improve the flexibility of compressing sensing data, obtain higher compression efficiency, and reduce the data volume of sensing data.

[0005] In a first aspect, an embodiment of the present application provides a method for transmitting sensing data. The method can be executed by a first communication device, and the method includes: generating compressed data and first indication information, where the first indication information indicates the categories of multiple sensing data, and the compressed data is obtained by compressing the multiple sensing data according to the compression method corresponding to the categories of the multiple sensing data, and among the multiple sensing data, the sensing data of the same category corresponds to the same compression method in the correspondence between different categories of sensing data and different compression methods; sending the compressed data and the first indication information to a second communication device.

[0006] Exemplarily: The categories of multiple sensing data can be determined according to the categories of the target objects corresponding to the multiple sensing data.

[0007] In the above method for transmitting sensing data, the first communication device and the second communication device are different communication devices. The first communication device (or the second communication device) can be a terminal device, an internal component of the terminal device (such as a processor, a chip, or a chip system, etc.), or a device used in combination with the terminal device, and can also be a network device, an internal component of the network device (such as a processor, a chip, or a chip system, etc.), or a device used in combination with the network device.

[0008] Through the above method, considering that the importance and characteristics of different categories of sensing data may be different, different compression methods can be set for different categories of sensing data, which is beneficial to improving the flexibility of compressing sensing data, obtaining higher compression efficiency, and reducing the data volume of sensing data.

[0009] In a possible design, the correspondence between different categories of perception data and different compression methods includes: a compression method of quantizing and compressing perception data without sampling, a compression method of quantizing and compressing perception data with sampling, a compression method based on the geometric structure corresponding to the perception data, a compression method based on the border corresponding to the perception data, a compression method of the perception data based on geometric projection, etc., one or more of them. Through this design, different compression methods can be set for different categories of perception data, which is beneficial to improving the flexibility of compressing and processing perception data and obtaining higher compression benefits.

[0010] Exemplarily: a compression method of quantizing and compressing perception data without sampling, where non-sampling quantization compression can mean quantizing and compressing all data.

[0011] A compression method of quantizing and compressing perception data with sampling, where sampling can refer to extracting partial data, such as extracting partial data in the form of data downsampling / extracting feature data, etc., and sampling quantization compression quantizes and compresses the extracted partial data.

[0012] A compression method based on the geometric structure corresponding to the perception data, where the geometric structure can be structures such as a plane, a curved surface, a polygon, a polyhedron, a cube, a cylinder, a cone, etc. The specific compression can be carried out in the following ways: (1) replacing the original data with geometric structure information, and the compressed data includes geometric structure information. For example, replacing with a cylinder, the compressed data can include its center point, radius and height; replacing with a polygon, the compressed data can include the vertices of the polygon, etc.; or (2) compressing data based on geometric structure information, and the compressed data can include geometric structure information and / or the perception data information contained in the geometric structure. For example, all / partial sampled perception data within the scanning area of the geometric structure. Additionally, it can be understood that entropy coding or quantization and entropy coding can be further performed on the above compressed data to further reduce the data volume.

[0013] A compression method based on the bounding box corresponding to the perception data, where the bounding box can be the bounding box of an object (such as a vehicle, a person, an object, etc.), and the form of the bounding box can be various. For example, the minimum bounding box, the bounding box based on the xyz three axes, and so on. The specific compression can be carried out in the following ways: (1) Replace the original data with the bounding box information. The compressed data includes the bounding box information, which can be the vertices of the bounding box, or the center position, length, and width of the bounding box; or (2) Compress the data based on the bounding box information. The compressed data includes the bounding box information and / or the data contained in the bounding box. For example, all the target data / partial sampled target data (such as target feature points, contour points, etc.) within the bounding box. Additionally, it can be understood that entropy coding or quantization can be performed on the above compressed data to further reduce the data volume. The boundary of quantization can be the range of the data or the range of the bounding box. Exemplarily, the boundary of quantization can be the boundary of the perception data based on the xyz three-axis coordinate system, or the perception data can be transformed into a coordinate system with the length, width, and height of the bounding box as the axes. Based on the boundary in this coordinate system, if based on the transformed coordinate system, the coordinate transformation relationship needs to be sent to the second communication device (such as a base station), such as a translation vector and / or a rotation parameter, where the rotation parameter can be in the form of a rotation matrix or three-axis rotation angles, etc.

[0014] A compression method for perception data based on geometric projection can project the perception data based on spherical coordinates / polar coordinates / Cartesian coordinates, and perform dimensionality reduction compression on the obtained data. For example, two of the obtained dimensions are used as position information, and the data of the remaining dimensions is compressed. This method can be used for all data / partial data, and can also be used for geometric structure information and / or the data contained in the geometric structure, and can also be used for target bounding box information and / or the data contained in the bounding box.

[0015] In a possible design, any perception data includes multi-dimensional data, and the first indication information also indicates the position information of multiple perception data, where the position information of any perception data is determined according to two dimensions of the multi-dimensional data of the perception data. Through this design, the two-dimensional data of the perception data can be indicated by the first indication information, and only the data of the perception data other than the two dimensions corresponding to the position information needs to be compressed, further reducing the data volume of the obtained compressed data.

[0016] In a possible design, multiple perception data are of the first category, and generating compressed data includes: performing quantization compression on the third-dimensional data of multiple perception data to obtain quantization compressed data, where the third-dimensional data of any perception data is the data of the multi-dimensional data of the perception data other than the two dimensions corresponding to the position information. Through this design, compression processing can be carried out in the way of quantization compression, further reducing the data volume of the generated compressed data.

[0017] In a possible design, the multi-dimensional data of any piece of sensed data includes at least three of θ, R, x, y, z, and r, where θ represents the vertical angle in the spherical coordinate system, represents the horizontal angle in the spherical coordinate system, R represents the distance to the origin in the spherical coordinate system, x represents the abscissa in the Cartesian coordinate system, y represents the ordinate in the Cartesian coordinate system, z represents the vertical coordinate in the Cartesian coordinate system, r represents the projection distance on the horizontal plane in the Cartesian coordinate system, and the third-dimensional data of the multiple sensed data other than the two-dimensional data corresponding to the position information is selected according to the information entropy or value range of each dimension of data other than the two-dimensional data in the multi-dimensional data. Through this design, the data dimensions for compression can be selected according to the information entropy or value range, which is beneficial to obtaining higher compression benefits.

[0018] In a possible design, the categories of the multiple sensed data are the second category, and generating compressed data includes: determining, according to the multiple sensed data, the geometric structure parameters or geometric structure identification points of at least one geometric structure (such as a plane, a curved surface, a polygon, a polyhedron, a cube, a cylinder, a cone, etc.) where the multiple sensed data are located, where the first indication information also indicates the geometric structure where the multiple sensed data are located, and the geometric structure parameters or geometric structure identification points of any geometric structure are used to determine the geometric structure, and the geometric structure identification points can be the boundary points of the geometric structure, the points located in the concave region and / or convex region of the geometric structure, etc. By way of example, when the sensed geometric structure is a plane, the geometric structure identification points can be three points not on the same straight line located on the plane, etc. Through this design, only the geometric structure parameters or geometric structure identification points need to be sent, and a greater compression gain can be obtained.

[0019] In a possible design, the method further includes: quantizing and compressing the residuals corresponding to the multiple sensed data to obtain quantized compressed data, where the residuals corresponding to the multiple sensed data are determined according to the multiple sensed data and the multiple restored data corresponding to the multiple sensed data, and the restored data corresponding to any piece of sensed data is determined according to the position information of the sensed data and the geometric structure where the sensed data is located. Through this design, further transmitting the residual data can meet the performance requirements of high reconstruction accuracy of the sensed data.

[0020] In a possible design, the categories of multiple perception data are the third category, and generating compressed data includes: determining at least one border corresponding to the multiple perception data, where the first indication information also indicates the border where the multiple perception data is located; quantizing and compressing the border endpoints of the multiple perception data and / or the perception data located at the border endpoints to obtain quantized compressed data; or, quantizing and compressing the third-dimensional data of the multiple perception data located at any border to obtain quantized compressed data, where the third-dimensional data of any perception data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

[0021] Exemplarily: quantizing and compressing the third-dimensional data of the multiple perception data located at any border to obtain quantized compressed data includes: quantizing and compressing the border endpoints of the multiple perception data (such as the three-dimensional data of the border endpoints or the third-dimensional data of the border endpoints) and / or the third-dimensional data of the perception data located at the border endpoints to obtain quantized compressed data; or, quantizing and compressing the third-dimensional data of multiple sampled perception data among the multiple perception data to obtain quantized compressed data, where the number of the multiple sampled perception data is less than the number of the multiple perception data; or, quantizing and compressing the third-dimensional data of all the perception data located at the border to obtain quantized compressed data. Through this design, sampled perception data (feature points) can be selected for compression to obtain compressed data, and the remaining perception data can be interpolated and restored, which can further improve the compression efficiency.

[0022] In a possible design, the method further includes: receiving a compression configuration from a second communication device, where the compression configuration indicates one or more categories of perception data that need to be transmitted, and the categories of the multiple perception data belong to the categories of perception data that need to be transmitted indicated by the compression configuration. Through this design, only the compressed data of the categories required by the second communication device can be transmitted, further reducing the data transmission volume.

[0023] In a possible design, the compression configuration further indicates the compression parameters and / or compression performance respectively corresponding to one or more categories of perception data that need to be transmitted, where the compressed data meets the requirements of the compression parameters and / or compression performance. Through this design, it is beneficial for the second communication device to adjust the compression parameters and / or compression performance on the first communication device side according to the requirements for the performance of the perception data.

[0024] In a possible design, the compression configuration further indicates the transmission priorities respectively corresponding to one or more categories of perception data that need to be transmitted, and the compressed data and the first indication information are sent to the second communication device according to the transmission priorities corresponding to the categories of the multiple perception data. Through this design, in scenarios such as resource constraints, the first communication device can transmit data hierarchically or in grades according to the transmission priorities respectively corresponding to the categories of perception data that need to be transmitted, and preferentially send the data required by the second communication device.

[0025] In a possible design, the compressed data further includes the reconstruction performance of multiple sensing data, and the method further includes: receiving second indication information from a second communication device, where the second indication information indicates sending residuals corresponding to the multiple sensing data, and the second indication information is sent by the second communication device when the reconstruction performance does not meet the reconstruction performance threshold; sending the residuals corresponding to the multiple sensing data to the second communication device, where the residuals corresponding to the multiple sensing data are determined according to the multiple sensing data and the multiple restored data corresponding to the multiple sensing data, and for any restored data corresponding to a sensing data, it is determined according to the position information corresponding to the sensing data and the geometric structure where the sensing data is located. Through this design, residual data can be sent only when the second communication device issues an indication. Under the condition of obtaining a certain compression gain, the performance requirement of high reconstruction accuracy of sensing data can be met.

[0026] In a second aspect, an embodiment of the present application provides a method for transmitting sensing data, which can be executed by a second communication device. The method includes: receiving compressed data and first indication information from a first communication device, where the first indication information indicates the categories of multiple sensing data, and the compressed data is obtained by compressing the multiple sensing data according to the compression method corresponding to the categories of the multiple sensing data; generating multiple sensing data, where the multiple sensing data is obtained by decompressing the compressed data based on the decompression method corresponding to the categories of the multiple sensing data, and the sensing data of the same category in the multiple sensing data corresponds to the same decompression method in the corresponding relationship between different categories of sensing data and different decompression methods.

[0027] Exemplarily: the categories of the multiple sensing data are determined according to the categories of the target objects corresponding to the multiple sensing data.

[0028] In a possible design, the corresponding relationship between different categories of sensing data and different compression methods includes: a compression method of quantizing and compressing the sensing data without sampling, a compression method of sampling and quantizing the sensing data, a compression method based on the geometric structure corresponding to the sensing data, a compression method based on the border corresponding to the sensing data, a compression method of the sensing data based on geometric projection, etc., one or more of them.

[0029] In a possible design, any sensing data includes multi-dimensional data, and the first indication information further indicates the position information of the multiple sensing data, where the position information of any sensing data is determined according to two-dimensional data in the multi-dimensional data of the sensing data; the multiple sensing data is obtained by decompressing the compressed data based on the decompression method corresponding to the categories of the multiple sensing data and the position information of the multiple sensing data.

[0030] In a possible design, the categories of multiple perception data are the first category, and the compressed data includes quantization-compressed data obtained by quantizing and compressing the third-dimensional data of the multiple perception data, where the third-dimensional data of any one of the perception data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

[0031] In a possible design, the categories of multiple perception data are the second category, the compressed data includes the geometric structure parameters or geometric structure identification points of at least one geometric structure where the multiple perception data is located, and the first indication information further indicates the geometric structure where the multiple perception data is located, and the geometric structure parameters or geometric structure identification points of any geometric structure are used to determine the geometric structure.

[0032] In a possible design, the compressed data further includes quantization-compressed data obtained by quantizing and compressing the residuals corresponding to the multiple perception data, where the residuals corresponding to the multiple perception data are determined according to the multiple perception data and the multiple restoration data corresponding to the multiple perception data, and the restoration data corresponding to any one of the perception data is determined according to the position information corresponding to the perception data and the geometric structure where the perception data is located.

[0033] In a possible design, the categories of multiple perception data are the third category, the first indication information further indicates the border where the multiple perception data is located, and the compressed data includes quantization-compressed data obtained by quantizing and compressing the border endpoints of the multiple perception data and / or the perception data located at the border endpoints, or quantization-compressed data obtained by quantizing and compressing the third-dimensional data of the multiple perception data located at any border indicated by the first indication information, where the third-dimensional data of any one of the perception data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

[0034] In a possible design, the quantization-compressed data obtained by quantizing and compressing the third-dimensional data of the multiple perception data located at any border includes: quantization-compressed data obtained by quantizing and compressing the third-dimensional data of all the perception data located at the border; or, quantization-compressed data obtained by quantizing and compressing the third-dimensional data of the border endpoints of the multiple perception data and / or the perception data located at the border endpoints; or, quantization-compressed data obtained by quantizing and compressing the third-dimensional data of multiple sampled perception data among the multiple perception data, where the number of the multiple sampled perception data is less than the number of the multiple perception data.

[0035] In a possible design, the method further includes: sending a compression configuration to the first communication device, where the compression configuration indicates one or more categories of perception data that need to be transmitted, and the categories of the multiple perception data belong to the categories of perception data that need to be transmitted indicated by the compression configuration.

[0036] In a possible design, the compression configuration further indicates compression parameters and / or compression performance respectively corresponding to one or more categories of sensed data to be transmitted, where the compressed data meets the requirements of the compression parameters and / or compression performance.

[0037] In a possible design, the compression configuration further indicates the transmission priorities respectively corresponding to one or more categories of sensed data to be transmitted.

[0038] In a possible design, the compressed data further includes the reconstruction performance of multiple sensed data, and the method further includes: when the reconstruction performance does not meet the reconstruction performance threshold, sending second indication information to a first communication device, where the second indication information indicates to send the residuals corresponding to the multiple sensed data; receiving the residuals corresponding to the multiple sensed data from the first communication device, where the residuals corresponding to the multiple sensed data are determined according to the multiple sensed data and the multiple restored data corresponding to the multiple sensed data, and for any restored data corresponding to a sensed data, it is determined according to the position information corresponding to the sensed data and the geometric structure where the sensed data is located.

[0039] In a third aspect, an embodiment of the present application provides a communication device, which has the functions of implementing the method in the first aspect or the second aspect above. The functions can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, such as an interface unit and a processing unit.

[0040] In a possible design, the device can be a device, a chip, or an integrated circuit.

[0041] In a possible design, the device includes a memory and a processor. The memory is used to store instructions executed by the processor. When the instructions are executed by the processor, the device can execute the method in the first aspect or the second aspect.

[0042] In a fourth aspect, an embodiment of the present application provides a communication device, which includes an interface circuit and a processor, and the processor and the interface circuit are coupled to each other. The processor is used to implement the method in the first aspect or the second aspect above through logic circuits or by executing instructions. The interface circuit is used to receive signals from other communication devices outside the communication device and transmit them to the processor, or send signals from the processor to other communication devices outside the communication device. It can be understood that the interface circuit can be a transceiver, a transceiver unit, a transceiver device, or an input / output interface.

[0043] Optionally, the communication device may further include a memory for storing instructions executed by the processor, or for storing input data required for the processor to execute the instructions, or for storing data generated after the processor executes the instructions. The memory may be a physically independent unit, or may be coupled to the processor, or the processor includes the memory (i.e., the processor and the memory are integrated together).

[0044] In one possible implementation, the communication device may be a device or a chip.

[0045] In a fifth aspect, an embodiment of the present application provides a communication system, which includes a first communication device and a second communication device. The first communication device is configured to implement the method of the first aspect above; the second communication device is configured to implement the method of the second aspect above.

[0046] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program or instructions are stored. When the computer program or instructions are executed by a processor, the method of the first aspect or the second aspect above can be implemented.

[0047] In a seventh aspect, an embodiment of the present application further provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed by a processor, the method of the first aspect or the second aspect above can be implemented.

[0048] In an eighth aspect, an embodiment of the present application further provides a chip system, which includes a processor. The processor is configured to be coupled to a memory, and the memory is used to store programs or instructions. When the programs or instructions are executed by the processor, the method of the first aspect or the second aspect above can be implemented.

[0049] For the technical effects that can be achieved by the second aspect to the eighth aspect above, please refer to the technical effects that can be achieved by the first aspect above, and will not be repeated here. Description of the Drawings

[0050] Figure 1 It is a schematic diagram of the architecture of the communication system provided by an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0052] Figure 3 It is one of the schematic diagrams of the perception data transmission method provided by an embodiment of the present application;

[0053] Figure 4 It is a schematic diagram of three-dimensional coordinates provided by an embodiment of the present application;

[0054] Figure 5 It is a 2D indication diagram provided by an embodiment of the present application;

[0055] Figure 6 One of the schematic diagrams of the 2D indication diagram provided by the embodiment of the present application for indicating the category of sensing data;

[0056] Figure 7A Schematic diagram of the distribution probability of the x value of the sensing data provided by the embodiment of the present application;

[0057] Figure 7B Schematic diagram of the distribution probability of the y value of the sensing data provided by the embodiment of the present application;

[0058] Figure 8 Schematic diagram of residual determination provided by the embodiment of the present application;

[0059] Figure 9 Another schematic diagram of the 2D indication diagram provided by the embodiment of the present application for indicating the category of sensing data;

[0060] Figure 10 Schematic diagram of the sensing data distribution provided by the embodiment of the present application;

[0061] Figure 11 Schematic diagram of sensing data sampling and interpolation restoration provided by the embodiment of the present application;

[0062] Figure 12 Another schematic diagram of the 2D indication diagram provided by the embodiment of the present application for indicating the category of sensing data;

[0063] Figure 13 Another schematic diagram of the sensing data transmission method provided by the embodiment of the present application;

[0064] Figure 14 Schematic diagram of layering provided by the embodiment of the present application;

[0065] Figure 15 Schematic diagram of incremental layer transmission provided by the embodiment of the present application;

[0066] Figure 16 Another schematic diagram of the sensing data transmission method provided by the embodiment of the present application;

[0067] Figure 17 One of the schematic diagrams of the sensing data transmission simulation results provided by the embodiment of the present application;

[0068] Figure 18 Another schematic diagram of the sensing data transmission simulation results provided by the embodiment of the present application;

[0069] Figure 19 One of the schematic diagrams of the structure of the communication device provided by the embodiment of the present application;

[0070] Figure 20 Another schematic diagram of the structure of the communication device provided by the embodiment of the present application. Detailed implementation manners

[0071] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: Long Term Evolution (LTE) systems, evolved LTE (LTE-Advanced, LTE-A) systems, Universal Mobile Telecommunications System (UMTS), and fifth-generation (5G) mobile communication systems, beyond 5G (B5G) mobile communication systems, or communication systems evolved after 5G (such as 6G mobile communication systems), etc. The communication system can also be a device-to-device (D2D) network, a WiFi network, a machine-to-machine (M2M) network, an Internet of Things (IoT) network, or other networks.

[0072] The architecture of the communication system to which the embodiments of the present application are applied can be as Figure 1 shown. The communication system 1000 includes a Radio Access Network (RAN) 100 and a Core Network (CN) 200. Optionally, the communication system 1000 may further include the Internet 300. The RAN 100 includes at least one network device (such as Figure 1 110a and 110b in Figure 1 , collectively referred to as 110) and at least one terminal device (such as Figure 1 120a - 120j in

[0073] , collectively referred to as 120). The RAN 100 may also include other RAN nodes, for example, wireless relay devices and / or wireless backhaul devices ( Figure 1 not shown in

[0073] ) and the like. The terminal device 120 is connected to the network device 110 wirelessly. The network device 110 is connected to the core network 200 wirelessly or by wire. The core network devices in the core network 200 and the network devices 110 in the RAN 100 may be different physical devices respectively, or may be the same physical device integrating the core network logical function and the radio access network logical function.The RAN 100 may be a cellular system related to the 3rd generation partnership project (3GPP), for example, a 4G, 5G, or an evolved system after 5G (such as a 6G mobile communication system). The RAN 100 may also be an open radio access network (O-RAN), a cloud radio access network (CRAN), or a WiFi system. The RAN 100 may also be a communication system that integrates two or more of the above systems.

[0074] The device provided by the embodiments of the present application can be applied to the network device 110 or the terminal device 120. It can be understood that Figure 1 Only one possible communication system architecture to which the embodiments of the present application can be applied is shown. In other possible scenarios, other devices may also be included in the communication system architecture.

[0075] The network device 110 is a node in a radio access network (RAN), also referred to as an access network device, and can also be referred to as a RAN node (or device). The network device 110 is used to assist the terminal device in achieving wireless access. The multiple network devices 110 in the communication system 1000 can be of the same type of node or different types of nodes. In some scenarios, the roles of the network device 110 and the terminal device 120 are relative. For example, Figure 1 The network element 120i in the Chinese network can be a helicopter or a drone, which can be configured as a mobile base station. For the terminal devices 120j that access the RAN 100 through the network element 120i, the network element 120i is a base station; but for the base station 110a, the network element 120i is a terminal device. The network device 110 and the terminal device 120 are sometimes both referred to as communication devices. For example Figure 1 The network elements 110a and 110b in the Chinese network can be understood as communication devices with base station functions, and the network elements 120a - 120j can be understood as communication devices with terminal device functions.

[0076] In a possible scenario, the network device can be a base station, an evolved NodeB (eNodeB), a transmitting and receiving point (TRP), a transmitting point (TP), a next generation NodeB (gNB), a base station in a future mobile communication system, a satellite, or an access point (AP) in a WiFi system, an integrated access and backhaul (IAB) node, a network device in a mobile switching center non-terrestrial network (NTN) communication system, that is, it can be deployed on a high-altitude platform or a satellite, etc. The network device can be a macro base station (such as Figure 1 110a in Figure 1 ), a micro base station or an indoor station (such as

[0077] 110b in

[0077] ), a relay node or a donor node, or a radio controller in a CRAN scenario. The network device can also be a device that serves as a base station function in device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, drone communication, or machine communication. Optionally, the network device can also be a server, a wearable device, a vehicle, or an in-vehicle device, etc. For example, the access network device in vehicle-to-everything (V2X) technology can be a road side unit (RSU).In another possible scenario, multiple network devices cooperate to assist a terminal device in achieving wireless access, and different network devices respectively implement some functions of a base station. For example, the network device can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be set separately, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as included in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH). It can be understood that the network device can be a CU node, or a DU node, or a device including a CU node and a DU node. In addition, the CU can be classified as a network device in the radio access network (RAN), or the CU can be classified as a network device in the core network (CN), which is not limited here.

[0078] In different systems, the CU (or CU-CP and CU-UP), DU, or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, the CU can also be called an O-CU (open CU), the DU can also be called an O-DU, the CU-CP can also be called an O-CU-CP, the CU-UP can also be called an O-CU-UP, and the RU can also be called an O-RU. For the convenience of description, the CU, CU-CP, CU-UP, DU, and RU are used as examples in this application. Any one of the CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0079] In the embodiments of this application, the form of the network device is not limited. The device for implementing the functions of the network device can be the network device; it can also be a device capable of supporting the network device to implement this function, such as a chip system. This device can be installed in the network device or used in matching with the network device.

[0080] The terminal device 120, which can also be referred to as a terminal, user equipment (UE), mobile station (MS), mobile terminal (MT), etc., can be a device for providing voice or data connectivity to users, an Internet of Things device, or a station (STA) in a WiFi system. For example, the terminal device includes handheld devices, vehicle-mounted devices, etc. with wireless connection functions. Currently, the terminal device can be: a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a mobile internet device (MID), a wearable device (such as a smart watch, a smart bracelet, a pedometer, smart glasses, etc.), a vehicle-mounted device (such as a car, a bicycle, an electric vehicle, an airplane, a ship, a train, a high-speed train, etc.), a satellite terminal, a virtual reality (VR) device, an augmented reality (AR) device, a smart point of sale (POS) machine, a customer-premises equipment (CPE), a wireless terminal in industrial control, a smart home device (such as a refrigerator, a TV, an air conditioner, an electric meter, etc.), a smart robot, a robotic arm, a workshop device, a wireless terminal in unmanned driving, a wireless terminal in remote medical treatment, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, or a wireless terminal in a smart home, a flying device (such as a smart robot, a hot air balloon, a drone, an airplane), etc. The terminal device can also be other devices with terminal functions. For example, the terminal device can also be a device that serves as a terminal function in D2D communication.

[0081] The embodiments of the present application do not limit the device form of the terminal device. The device for implementing the functions of the terminal device can be the terminal device; it can also be a device capable of supporting the terminal device to implement the functions, such as a chip system. This device can be installed in the terminal device or used in matching with the terminal device. In the embodiments of the present application, the chip system can be composed of chips or can also include chips and other discrete devices.

[0082] Based on Figure 1 the shown communication system architecture, Figure 2An exemplary application scenario applicable to the embodiments of the present application is shown, including a terminal device and a network device. The terminal device (such as a mobile phone, computer, car, airplane, etc.) can scan the surrounding environment through sensors set on the terminal device to obtain perception data (which can also be called point cloud data, imaging data, etc.), and send the compressed perception data to the network device. The network device can perform information fusion and environmental map construction based on the perception data reported by the terminal device.

[0083] In addition, it should be understood that the perception data involved in the embodiments of the present application may refer to data obtained by a communication device (such as a terminal device, a vehicle-mounted device, etc.) scanning the surrounding environment through sensors (such as one or more of a vision sensor, an electromagnetic wave sensor (or antenna), a millimeter wave sensor, etc.). Exemplarily: Each perception data can correspond to a point in space and can include at least one-dimensional data obtained by scanning the point. For example, the three-dimensional coordinates of the point (where each coordinate dimension can correspond to one-dimensional data), the echo signal strength, the round-trip time of the perception signal (such as an electromagnetic wave signal), etc., and each piece of data can be the one-dimensional data included in the perception data. The restored data can refer to the perception data restored based on certain information (or data), such as the perception data obtained by decompressing the compressed data obtained by compressing the perception data. The residual corresponding to the perception data can refer to the difference between the perception data and its restored data.

[0084] The ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the size, content, order, time sequence, priority, or importance, etc. of the multiple objects. For example, the first communication device and the second communication device do not indicate differences in the priority or importance, etc. corresponding to these two messages.

[0085] In the embodiments of the present application, unless otherwise specified, for the number of nouns, it means "singular noun or plural noun", that is, "one or more".

[0086] "At least one" means one or more, and "multiple" means two or more. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. For example, A / B means: A or B. "At least one (item) of the following or similar expressions refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c means: a, b, c, a and b, a and c, b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0087] The present application provides a method and apparatus for transmitting sensing data. Considering that the importance and characteristics of different types of sensing data in sensing data may vary, different compression methods can be used to compress different types of sensing data, in order to improve the flexibility of compressing sensing data and obtain higher compression benefits. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0088] The sensing data transmission method provided by the embodiments of the present application can be executed by a first communication device and a second communication device, where the first communication device and the second communication device are different communication devices. The first communication device (or the second communication device) may refer to a terminal device, a component of the terminal device (such as a processor, a chip, or a chip system, etc.) or a device used in combination with the terminal device, and may also refer to a network device, a component of the network device (such as a processor, a chip, or a chip system, etc.) or a device used in combination with the network device. The first communication device can be used as the sending end of the sensing data to compress the sensing data, and the second communication device can be used as the receiving end to decompress and recover the sensing data.

[0089] Figure 3 FIG. 8 is one of the schematic diagrams of the sensing data transmission method provided by the embodiments of the present application. The method includes:

[0090] S301: The first communication device generates compressed data and first indication information. The first indication information indicates the types of multiple sensing data, and the compressed data is obtained by compressing the multiple sensing data according to the compression methods respectively corresponding to the types of the multiple sensing data.

[0091] Among them, the sensing data belonging to the same type among the multiple sensing data corresponds to the same compression method in the correspondence between different types of sensing data and different compression methods.

[0092] S302: The first communication device sends the compressed data and the first indication information to the second communication device. Correspondingly, the second communication device receives the compressed data and the first indication information.

[0093] In the embodiments of the present application, the first communication device can be used as a sensing device or a scanning device to scan a certain area to obtain sensing data; or the first communication device can also obtain sensing data from other communication devices, and the present application does not limit this. Exemplarily: Each sensing data can correspond to a point in space, and the sensing data can include at least one-dimensional data obtained by scanning the point. For example, one or more of the three-dimensional coordinates of the point (where each coordinate dimension can correspond to one-dimensional data), the echo signal intensity, the round-trip time of the sensing signal (such as an electromagnetic wave signal), etc.

[0094] Refer to Figure 4The three-dimensional coordinate schematic diagram shown, the three-dimensional coordinates included (or recorded) in the perception data can be spherical coordinates represented by the vertical angle θ (which can also be called the pitch angle), the horizontal angle (which can also be called the yaw angle) and the distance R from the point to the origin It can also be Cartesian coordinates (x, y, z) represented by the x value (abscissa), y value (ordinate) and z value (vertical coordinate) in the Cartesian coordinate system. Of course, it can also be three-dimensional coordinates in other three-dimensional coordinate systems (such as the cylindrical coordinate system, etc.). The present application does not limit the specific form of the three-dimensional coordinates. It can be understood that the three-dimensional coordinates in different coordinate systems can be converted to each other.

[0095] Exemplarily, the mapping algorithm from the Cartesian coordinate system to the spherical coordinate system can satisfy the following formula:

[0096]

[0097] In the embodiments of the present application, considering that the characteristics of different perception data included (or recorded) are different, multiple obtained perception data can be classified based on the characteristics of one-dimensional or multi-dimensional data of the perception data (such as the value range, etc.), and different compression methods can be used to compress different categories of perception data.

[0098] Taking the perception data including the round-trip time of the perception signal as an example, when the first communication device scans a certain area, the greater the distance between the target object in the area and the first communication device, the greater the round-trip time of the perception signal in the perception data obtained by scanning the object. Therefore, in one possible implementation, the perception data can be divided into different categories according to the round-trip time of the perception signal. Example: The perception data with the round-trip time within the round-trip time range 1 can be divided into category 1, the perception data with the round-trip time within the round-trip time range 2 can be divided into category 2, and the perception data with the round-trip time within the round-trip time range 3 can be divided into category 3.

[0099] Taking the perception data including three-dimensional coordinates as an example, when the first communication device scans a certain area, the three-dimensional coordinates corresponding to the multiple perception data obtained by scanning a certain object in the area match the three-dimensional coordinates of multiple points on the surface of the object. Therefore, in one possible implementation, the perception data can also be divided into different categories according to the category of the target object corresponding to the perception data.

[0100] As an example: An artificial intelligence (AI) model for identifying the categories of target objects corresponding to perception data (such as ground, buildings, vehicles, vegetation, utility poles, etc.) can be trained. The first communication device can input the acquired multiple perception data into the AI model for processing to obtain the categories of the target objects corresponding to the respective perception data, which are used as the categories of the corresponding perception data.

[0101] In the embodiments of the present application, different categories of perception data correspond to different compression methods. For example, category 1 corresponds to compression method 1, category 2 corresponds to compression method 2, and so on. Among them, different compression methods can refer to different compression schemes or different compression parameters (or compression degrees). By way of example: The KDtree compression scheme and the compression scheme for sampling and quantizing compression of perception data (the unsampled perception data is restored based on interpolation) can be different compression methods; the compression scheme for sampling and quantizing compression of perception data with a sampling rate of 1 / 2 and the compression scheme for sampling and quantizing compression of perception data with a sampling rate of 1 / 4 can also be different compression methods.

[0102] The correspondence between different categories of perception data and different compression methods can be pre-configured in the first communication device or can also be indicated to the first communication device by other communication devices (such as the second communication device). The present application does not limit the manner in which the first communication device obtains (or determines) the correspondence between different categories of perception data and different compression methods.

[0103] It can be understood that if the data characteristics of different categories of perception data are the same or similar, for example, the three-dimensional coordinates included in the perception data all conform to the distribution of the three-dimensional coordinates of points on a plane, different categories of perception data can also correspond to the same compression method.

[0104] After the first communication device acquires multiple perception data (a set of perception data), for each category of perception data, the first communication device can use the compression method corresponding to that category to compress the acquired perception data of that category to generate compressed data. After generating the compressed data, the first communication device can send the compressed data and the first indication information that can indicate the categories of the acquired multiple perception data to the second communication device.

[0105] Exemplary: The first communication device obtains 35 sensing data by scanning a certain area. Among them, 20 sensing data belong to category 1 and 15 sensing data belong to category 2. The first communication device can use compression method 1 corresponding to category 1 (such as a compression scheme for compressing all sensing data) to compress the 20 sensing data of category 1 and generate compressed data corresponding to the 20 sensing data of category 1; use compression method 2 corresponding to category 2 (such as a compression scheme for sampling half of the sensing data for compression and restoring the other unsampled sensing data based on interpolation) to compress 10 sensing data of category 2 and generate compressed data corresponding to the 10 sensing data of category 2. After generating the compressed data, the first communication device can send the compressed data corresponding to the 20 sensing data of category 1, the compressed data corresponding to the 10 sensing data of category 2, and the first indication information for indicating the categories of the obtained 35 sensing data to the second communication device.

[0106] In a possible implementation, the first indication information can indicate the categories of multiple sensing data by means of a sequence, a bitmap, etc. As an example: The sequence is 1, 1, 2, 3, …, 4, where 1, 2, 3, and 4 are the indexes of category 1, category 2, category 3, and category 4 respectively. It can be indicated by this sequence that the category of the first sensing data is category 1, the category of the 2nd sensing data is category 1, the category of the 3rd sensing data is category 2, the category of the 4th sensing data is category 3, …, and the category of the last sensing data is category 4.

[0107] Of course, the first indication information can also indicate the categories of sensing data by means of a two-dimensional (2D) indication map, etc. Taking the sensing data including three-dimensional data (such as three-dimensional coordinates) as an example, for the obtained multiple sensing data, the first communication device can determine the position information (such as the 2D structure position) according to the two-dimensional data (such as two-dimensional coordinates) of any sensing data, and use the index (or identifier) of the category corresponding to the sensing data as the filling value at this position information, so as to design the 2D indication map.

[0108] Exemplary, the first communication device can the three-dimensional coordinates of any sensing data Figure 5 after quantization processing, determine the 2D structure position of the sensing data, and fill the index (or identifier) of the category corresponding to the sensing data at the 2D structure position of the sensing data, to obtain the 2D indication map as The quantization value of the horizontal axis represents the quantization value of the horizontal angle φ, and the quantization value of the vertical axis represents the quantization value of the vertical angle θ. The value filled in each square represents the index (or identifier) of the category corresponding to the sensed data represented by the square. Among them, a filled value of 1 indicates that the sensed data represented by the square corresponds to the category with index 1 (such as category 1), a filled value of 2 indicates that the sensed data represented by the square corresponds to the category with index 2 (such as category 2), a filled value of 3 indicates that the sensed data represented by the square corresponds to the category with index 3 (such as category 3), a filled value of 4 indicates that the sensed data represented by the square corresponds to the category with index 4 (such as category 4), and a filled value of 0 can indicate that there is no sensed data in the square (i.e., the 2D structure position), or the 2D structure position corresponding to the square (i.e., the 2D structure position) has no corresponding information and is a hole point.

[0109] In some implementations, the position information (such as the 2D structure position) of multiple sensed data can also be indicated by the first indication information, and the position information can be used to indicate (or determine) the two-dimensional data of the multiple sensed data. For example: The first indication information can indicate the categories of multiple sensed data in the manner of the 2D indication diagram shown above, and indicate the 2D structure position of each sensed data, and the 2D structure position can be used to indicate (or determine) the three-dimensional coordinates of the sensed data Figure 5 shown above, and indicate the 2D structure position of each sensed data, and the 2D structure position can be used to indicate (or determine) the three-dimensional coordinates of the sensed data in

[0110] In one implementation, for the case where the sensed data includes three-dimensional data (such as three-dimensional coordinates), a compression method based on 2D indication + filled value can be considered.

[0111] Taking the three-dimensional coordinates of multiple sensed data as the three-dimensional coordinates in the spherical coordinate system as an example, the 2D structure position of each sensed data can be determined according to the θ value and value of each sensed data, and the 2D structure position of each sensed data can be indicated by the first indication information (such as a 2D indication diagram). For the R value sequence composed of the R values of multiple sensed data, quantization compression can be performed to obtain quantization compression data. The quantization compression data can refer to the data obtained by compressing the quantized R value sequence.

[0112] The quantization range corresponding to the quantization can be pre-configured or determined (such as negotiated and determined by the first communication device and the second communication device before data compression, or predefined through a protocol, etc. and stored in the first communication device and the second communication device, etc.), or can be determined by the first communication device. For example, the range of R values in the R value sequence that needs to be quantized is [R min , R max , and the first communication device can determine the quantization range as [R min , R maxand so on. It can be understood that if the quantization range during the compression of the sensed data is determined by the first communication device, the compressed data sent by the first communication device to the second communication device may include not only the quantization compression of the sensed data to obtain the quantized compressed data, but also the quantization range. When performing quantization, the smaller the quantization range, the more quantization bits, and the higher the quantization accuracy.

[0113] To further improve the compression efficiency, for the case where the sensed data includes three-dimensional data (such as three-dimensional coordinates), a compression method based on 2D indication + fill value selection can be considered.

[0114] As an example: The spherical coordinates can be deformed into where a is one or more of {x, y, z, r, R...}, θ represents the vertical angle in the spherical coordinate system, θ represents the horizontal angle in the spherical coordinate system, x represents the abscissa in the Cartesian coordinate system, y represents the ordinate in the Cartesian coordinate system, z represents the vertical coordinate in the Cartesian coordinate system, r represents the projection distance on the horizontal plane (xOy plane) in the Cartesian coordinate system, R represents the distance to the origin in the spherical coordinate system, where For x, y, z, r, R, etc., they can be determined according to the three-dimensional coordinates included (or recorded) in the sensed data, and will not be elaborated here. It can be understood that a can also take the projection distance on the xOz plane in the Cartesian coordinate system, the projection distance on the yOz plane in the Cartesian coordinate system, etc.

[0115] For the value of a, it can be selected according to the information entropy or value range of one or more of the x-value sequence, y-value sequence, z-value sequence, r-value sequence, R-value sequence, etc. corresponding to multiple sensed data.

[0116] As an example: After quantizing the values in the x-value sequence, y-value sequence, z-value sequence, r-value sequence, R-value sequence corresponding to multiple sensed data with the same quantization performance (such as the same number of quantization levels), the information entropy H(a) corresponding to each sequence can be calculated, and the value (such as x, y, z, r, R...) corresponding to the sequence with the smallest H(a) is selected as a. Among them, the following formula can be used to calculate H(x):

[0117] H(a) = -∑P(ai)log(2, P(ai)), (i = 1, 2,..n), where a can take one or more of x, y, z, r, R, etc. respectively, n is the number of sensed data, i is the index of the value in the sequence, and ai is a certain symbol in the quantization space (such as the quantization value of the i-th a in the sequence).

[0118] As an example: It is also possible to select the value (such as x, y, z, r, R...) corresponding to the sequence with the smallest range g among the values of the x-value sequence, y-value sequence, z-value sequence, r-value sequence, and R-value sequence corresponding to multiple sensing data as a. The range g of the values in any one of the sequences can be determined based on the difference between the maximum value max and the minimum value min in that sequence.

[0119] In some implementations, when compressing multiple sensing data of the ground (such as the first category: category 1) using a compression scheme based on the selection of padding values, since the fluctuation of the z-values of the multiple sensing data of the ground category is the smallest and the value range of the z-value sequence is the smallest, the three-dimensional data (such as three-dimensional coordinates) of each sensing data can be deformed into Quantize and compress the z-value sequence of multiple sensing data to obtain a quantization compression range and quantization compression data S 1 。

[0120] For multiple sensing data of the ground, the compressed data corresponding to the ground sent by the first communication device to the second communication device may include a quantization range (optional, for example, when the first communication device determines the quantization range based on the value range of the sequence to be quantized, etc., it may be included), a quantization step (such as the number of quantization bits, optional, for example, when it is determined solely by the first communication device and the second communication device has not learned about it, it may be included) quantization compression data, and may also include an index for the selection of the a value (such as the index of z). Of course, the selection of the a value can also be directly associated with the category. For example, when the category is the ground, the a value is selected as z.

[0121] Refer to Figure 6 As shown, after receiving the first indication information (2D indication diagram) and the compressed data from the first communication device, the second communication device can determine multiple sensing data of the ground category based on the padding value 1, and obtain the According to the quantization range and quantization step, perform inverse quantization on the quantization compression data to obtain the z of each sensing data and restore the three-dimensional coordinates of each sensing point.

[0122] The outer surfaces of buildings, etc., usually exist in the form of a certain geometric structure (such as a plane, a curved surface, etc.). When scanning buildings, etc., the three-dimensional coordinate distribution of the multiple sensing data obtained usually also conforms to the three-dimensional coordinate distribution of multiple points on a certain geometric structure. Therefore, if the sensing data includes three-dimensional coordinates, for multiple sensing data of a building (such as the second category: category 2), a compression method based on 2D indication + geometric structure (such as a plane, a curved surface, a polygon, a polyhedron, a cube, a cylinder, a cone, etc.) fitting can be considered.

[0123] Taking the geometric structure as a plane as an example, the first communication device can also extract and fit the sensed data according to the distribution probability of one or more of the x-values (or y-values or z-values) of multiple sensed data of a building, and based on a relatively large continuous segment of x-values (or y-values or z-values) that appear according to the probability, fit out at least one plane, and obtain the plane parameters or plane identification points of at least one plane. Among them, any plane F can be represented by the formula F = Ax + By + Cz + D = 0, and the parameters of the plane F can be (A, B, C, D). The plane identification point is a point that can determine the plane. For example, the plane identification point can be three points on the plane that are not on the same straight line, and the three-dimensional coordinates of the three points can uniquely determine the plane.

[0124] Referring to Figure 7A the schematic diagram of the distribution probability of the x-values of the multiple sensed data shown, where Figure 7A the horizontal axis in it represents the x-values of the multiple sensed data (i.e., the abscissa), and the vertical axis represents the distribution probability (or ratio) of the sensed data. It can be seen from Figure 7A that the x-values for which the distribution probability of the sensed data is greater than the distribution probability threshold (taking the distribution probability threshold as 0.05 as an example) are -19 and 12 respectively. It can be determined that the x-value distribution intervals [-19.25, -18.75] and [11.75, 12.25] corresponding to -19 and 12 respectively can be used for the fitting of the plane. The first communication device can fit out the sensed plane F based on the multiple sensed data with x-values in the range [-19.25, -18.75] by the least squares method 1 ; and fit out the sensed plane F based on the multiple sensed data with x-values in the range [11.75, 12.25] by the least squares method 2 .

[0125] Referring to Figure 7B the schematic diagram of the distribution probability of the y-values of the multiple sensed data shown, where Figure 7B the horizontal axis in it represents the y-values of the multiple sensed data (i.e., the ordinate), and the vertical axis represents the distribution probability (or ratio) of the sensed data. It can be seen from Figure 7B that the y-values for which the distribution probability of the sensed data is greater than the distribution probability threshold (taking the distribution probability threshold as 0.05 as an example) are -77, -75, and 47 respectively. It can be determined that the y-value distribution intervals [-77.25, -76.75], [-75.25, -74.75], and [46.75, 47.25] corresponding to -77, -75, and 47 respectively can be used for the fitting of the plane. The first communication device can fit out the sensed plane F based on the multiple sensed data with y-values in the range [-77.25, -76.75] by the least squares method 3 ; and fit out the sensed plane F based on the multiple sensed data with y-values in the range [-75.25, -74.75] by the least squares method 4, based on multiple sensing data where the y value is within [46.75, 47.25], the sensing plane F is fitted by the least squares method 5 .

[0126] Taking the horizontal axis of the 2D indication diagram to represent the quantization value of the horizontal angle and the vertical axis to represent the quantization value of the vertical angle θ as an example, referring to Figure 8 as shown, for each sensing data, there will be a corresponding 2D structural position in the 2D indication diagram. According to this 2D structural position, the vertical angle θ and the horizontal angle of this sensing data can be obtained to thereby determine a straight line I starting from the origin: x / m = y / n = z / k = t, where m, n, and k are the components of the spatial vector determined according to

[0127] the angle θ and on the x-axis, y-axis, and z-axis, and t is a variable. Combining the plane parameters (A, B, C, D) of the plane where this sensing data is located, the intersection point P of the straight line I and the plane: Ax + By + Cz + D = 0 can be determined 2 ′ is the restored data (which can also be called the restored point) of this sensing data.

[0128] Further calculate the restored data P 2 ′ and the projection distances r′ 2 of this sensing data (i.e., the real point) P 2 and r 2 on the horizontal plane (or vertical plane or x-axis, or y-axis or z-axis), etc., and the difference can obtain the residual Δr corresponding to the sensing data.

[0129] In a possible implementation, as Figure 9 shown, for multiple sensing data of a building, the first communication device can send the first indication information (such as a 2D indication diagram) and the compressed data including the plane parameters of at least one plane where the multiple sensing data are located to the second communication device. The multiple 2D structural positions with a filled value of 2 in the 2D indication diagram can indicate that the categories of the corresponding multiple sensing data are buildings. After receiving the first indication information and the plane parameters of at least one plane, the second communication device can determine the straight line I in the 3D space: x / m = y / n = z / k = t according to the angle θ and corresponding to each 2D structural position in the 2D indication diagram; combining the plane where this sensing data is located: Ax + By + Cz + D = 0, the intersection point P of the straight line I and this plane can be calculated 2 ′: (x, y, z) is the restored data (i.e., the restored point) of this sensing data.

[0130] It can be understood that if multiple perception data of a building correspond to multiple planes, and if the perception data corresponding to the multiple planes are not continuous on the 2D indication diagram, each part of the continuous perception data can correspond to a plane. Of course, if there are situations where the perception data corresponding to multiple planes are continuous on the 2D indication diagram, etc., it is also possible to indicate the plane corresponding to the perception data on the 2D indication diagram through different filling values (such as the index of the filled plane), for example, indicating different planes through filling values 2 and 5; it is also possible to indicate the plane corresponding to the perception data through the value corresponding to the filling category + the index of the plane, for example, indicating that the category corresponding to the perception data is category 2 through 2A and 2B, and the planes corresponding to the perception data are plane A and plane B respectively, etc. The present application does not limit the specific manner in which the first indication information (such as the 2D indication diagram) indicates the plane where the multiple perception data are located.

[0131] Furthermore, the first communication device can also perform quantization compression on the residuals Δr corresponding to the above-mentioned multiple perception data, that is, the residual Δr sequence composed of the residuals Δr corresponding to the multiple perception data, to obtain quantization compression data S. 2 , and send the quantization compression range of the residual Δr sequence (optional, for example, it can be included when the first communication device determines the quantization range according to the value range of the sequence to be quantized, etc.), the quantization step (such as the number of quantization bits, optional, for example, it can be included when it is determined solely by the first communication device and the second communication device has not learned it) and the quantization compression data S to the second communication device. 2 . Optionally, the compressed data can also include the index of the selected residual Δr (such as indicating the residual determined based on the projection distance on the horizontal plane or vertical plane or the x-axis, or y-axis or z-axis). Of course, the selection of the residual Δr can be associated with the category. For example, when the category is the ground, the residual Δr is fixedly selected as the residual determined based on the projection distance on the horizontal plane, etc.

[0132] After the second communication device restores the restoration point P 2 ′ of the perception data, it calculates P 2 ′ projection distance r 2 ′, +Δr to restore the true value, and combines θ and value to solve the true (x, y, z) of the perception data.

[0133] It can be understood that the above r′ 2 +Δr can also be expressed in forms such as x+Δr, y+Δr, z+Δr, R+Δr, etc. For example, when the residual Δr is determined according to the difference in the projection distance on the x-axis between the restoration point P 2 ′ and the true position (i.e., the true point) P 2 of this perception data, it can be expressed as x+Δr; according to the restoration point P 2 ′ and the true position (i.e., the true point) P 2When the difference in the projected distance on the y-axis is determined, it can be expressed as y + Δr; according to the restored point P 2 ′ and the true position of the sensed data (i.e., the true point) P 2 When the difference in the projected distance on the z-axis is determined, it can be expressed as z + Δr, etc.; according to the restored point P 2 ′ and the true position of the sensed data (i.e., the true point) P 2 When the difference in the distance to the origin is determined, it can be expressed as R + Δr, etc. Among them, x, y, z, and R can be the coordinates of the restored point P 2 ′ on the x-axis (or the projected distance on the x-axis), the restored point P 2 ′ on the y-axis (or the projected distance on the y-axis), the restored point P 2 ′ on the z-axis (or the projected distance on the z-axis) and the restored point P 2 ′ to the distance of the origin.

[0134] In a possible implementation, for multiple sensed data corresponding to objects existing in the form of independent individuals, a compression scheme can be designed based on the object's border. For example, vehicles all exist in their respective border forms. For vehicles, a compression method based on 2D indication + sampling of sensed data (feature points) within the border can be considered.

[0135] Refer to Figure 10 As shown in the schematic diagram of the sensed data, by clustering the sensed data of the category of vehicles, the sensed data corresponding to each vehicle can be obtained, that is, the sensed data corresponding to the border of each vehicle, where Figure 10 each circle in it can represent a clustering of the sensed data of a vehicle.

[0136] Exemplarily, based on the way of filling value selection, multiple sensed data corresponding to the border can be compressed. For example, the three-dimensional coordinates of multiple sensed data corresponding to the border are deformed into The 2D structural position of each sensed data can be determined according to the θ value and value of each sensed data, and the 2D structural position of each sensed data is indicated by the first indication information (such as a 2D indication diagram). For the a value sequence composed of the a values of multiple sensed data, quantization compression can be performed to obtain quantization compression data. Where a ∈ {x, y, z, r, R...} is one or more items, and the description of the value of a can refer to the introduction in the above compression method based on 2D indication + filling value selection, and will not be elaborated here.

[0137] In the above implementation, the third-dimensional data of all sensed data located at the border can be quantized and compressed to obtain quantization compression data.

[0138] In some implementations, in order to further reduce the amount of data, when quantizing and compressing the third-dimensional data (such as a) of multiple perception data of a border to obtain quantized compressed data, only the border endpoints of the multiple perception data (such as the three-dimensional data of the border endpoints or the third-dimensional data of the border endpoints) and / or the third-dimensional data (such as a) of the perception data located at the border endpoints can be quantized and compressed to obtain a quantization range and quantized compressed data.

[0139] Alternatively, the third-dimensional data (such as a) of multiple sampled perception data (i.e., feature points) among the multiple perception data is quantized and compressed to obtain quantized compressed data, where the number of the multiple sampled perception data is less than the number of the multiple perception data.

[0140] For multiple perception data of a vehicle, the compressed data corresponding to the vehicle sent by the first communication device to the second communication device may include a quantization range (optional, for example, it can include when the first communication device determines the quantization range according to the value range of the sequence to be quantized, etc.), a quantization step (such as the number of quantization bits, optional, for example, it can include when it is determined solely by the first communication device and the second communication device has not learned about it), quantized compressed data, and may also include an index for the selection of the a value (such as the indices of z, x, etc.). Of course, the selection of the a value can be associated with the category. For example, when the category is a vehicle, the a value selects z.

[0141] As Figure 11 shown, when the target shape is relatively fixed and consists of many faces / lines, partial feature data (which can also be called feature points) can be extracted, and the whole picture can be restored based on the feature data. For example: for the perception data at the first continuous position, the a sequence at odd positions is sent, and the a at even positions is restored by interpolation. For example, a i =(a i -1 + a i +1) / 2, where i is the position or index of the perception data to be restored.

[0142] Referring to Figure 12 shown, after receiving the first indication information (2D indication map) and the compressed data from the first communication device, the second communication device can determine multiple perception data of the category of vehicle according to the filling value 3, and obtain the According to the quantization range and quantization step, inverse quantization is performed on the quantized compressed data, and interpolation restoration is performed to obtain the z of each perception data, and the three-dimensional data (such as three-dimensional coordinates) of each perception data is restored.

[0143] It can be understood that if multiple perception data of a vehicle correspond to multiple borders, on a 2D indication diagram, the borders corresponding to the perception data can be indicated by different filling values (such as the indexes of the filled borders). For example, different borders can be indicated by filling values 3 and 6; or the borders corresponding to the perception data can be indicated by the index corresponding to the category + the index of the border. For example, 3A and 3B are used to indicate that the category of the perception data is category 3, and the corresponding faces of the perception data are border A and border B respectively. The present application does not limit the specific manner in which the first indication information (such as the 2D indication diagram) indicates the borders where multiple perception data are located.

[0144] Of course, the compression scheme based on the border design may further include quantizing and compressing the border endpoints of multiple perception data and / or the perception data located at the border endpoints to obtain quantized compressed data. The receiving end can restore multiple perception data based on the border endpoints and / or the perception data located at the border endpoints through interpolation and other methods.

[0145] S303: The second communication device generates multiple perception data, and the multiple perception data are obtained by decompressing the compressed data based on the decompression method corresponding to the category of the multiple perception data.

[0146] Among them, the perception data of the same category in the multiple perception data correspond to the same decompression method in the corresponding relationship between the categories of different perception data and different decompression methods.

[0147] In the embodiments of the present application, different categories of perception data correspond to different decompression methods. The decompression method corresponding to the category of any perception data can be used to decompress the compressed data of the perception data of this category. After receiving the compressed data and the first indication information from the first communication device, the first communication device can, according to the category of the perception data indicated by the first indication information, adopt the corresponding decompression method to decompress the compressed data to obtain multiple perception data.

[0148] Exemplarily: The compressed data includes the compressed data of 20 perception data of category 1 obtained by compressing 20 perception data of category 1 according to the compression method 1 corresponding to category 1, and the compressed data of 15 perception data of category 2 obtained by compressing 15 perception data of category 2 according to the compression method 2 corresponding to category 2. The second communication device can decompress the compressed data of 20 perception data of category 1 according to the decompression method 1 corresponding to category 1 to obtain 20 perception data of category 1; and decompress the compressed data of 15 perception data of category 2 according to the decompression method 2 corresponding to category 2 to obtain 15 perception data of category 2.

[0149] It can be understood that the present application does not limit the correspondence between the categories of different perception data and different compression methods. The correspondence between the categories of different perception data and different compression methods can be flexibly configured or adjusted according to the transmission requirements of the perception data. The compression methods that can be adopted can include, but are not limited to, the above-mentioned compression methods based on 2D indication + fill value, compression methods based on 2D indication + geometric structure fitting, compression methods based on 2D indication + perception data (or feature points) compression within the border, as well as compression methods for quantizing and compressing perception data without sampling, compression methods for quantizing and compressing perception data with sampling, compression methods based on the geometric structure corresponding to the perception data, compression methods based on the border corresponding to the perception data, compression methods for the perception data based on geometric projection, etc., one or more of them.

[0150] Exemplary: A compression method for quantizing and compressing perception data without sampling, where non-sampling quantization compression can mean quantizing and compressing all data.

[0151] A compression method for quantizing and compressing perception data with sampling, where sampling can refer to extracting part of the data, such as extracting part of the data in the form of data downsampling / extracting feature data, etc., and sampling quantization compression quantizes and compresses the extracted part of the data.

[0152] A compression method based on the geometric structure corresponding to the perception data, where the geometric structure can be structures such as planes, curved surfaces, polygons, polyhedrons, cubes, cylinders, cones, etc. The specific compression can be carried out in the following ways: (1) replacing the original data with geometric structure information, and the compressed data includes geometric structure information. For example, replacing with a cylinder, the compressed data can include its center point, radius, and height; replacing with a polygon, the compressed data can include the vertices of the polygon, etc.; or (2) compressing the data based on the geometric structure information, and the compressed data can include geometric structure information and / or the perception data information contained in the geometric structure. For example, all / part of the sampled perception data within the scanning area of the geometric structure. It can also be understood that entropy coding or quantization and entropy coding can be further performed on the above compressed data to further reduce the data volume.

[0153] A compression method based on the bounding box corresponding to the perception data, where the bounding box can be the bounding box of an object (such as a vehicle, a person, an object, etc.). The specific compression can be carried out in the following ways: (1) replacing the original data with the bounding box information, and the compressed data includes the bounding box information, which can be the vertices of the bounding box, or the center position, length, and width of the bounding box; or (2) performing data compression based on the bounding box information, and the compressed data includes the bounding box information and / or the data contained in the bounding box, such as all the target data / partial sampled target data (such as target feature points, contour points, etc.) within the bounding box. Additionally, it can be understood that entropy coding or quantization can be performed on the above compressed data to further reduce the data volume, where the quantization boundary can be the range of the data or the range of the bounding box.

[0154] A compression method for perception data based on geometric projection can project the perception data based on spherical coordinates / polar coordinates / Cartesian coordinates, and perform dimensionality reduction compression on the obtained data. For example, two of the obtained dimensions are used as position information, and the data of the remaining dimensions is compressed. This method can be used for all data / partial data, and can also be used for geometric structure information and / or the data contained in the geometric structure, and can also be used for target bounding box information and / or the data contained in the bounding box.

[0155] In some embodiments, the second communication device can also send a compression configuration to the first communication device, and the first communication device can also generate and send compressed data according to the compression configuration.

[0156] Figure 13 This is the second method for transmitting perception data provided by this application, and this method includes:

[0157] S1301: The second communication device sends a compression configuration to the first communication device. Correspondingly, the first communication device receives the compression configuration.

[0158] Among them, the compression configuration can indicate one or more categories of perception data that need to be transmitted, and can also indicate the transmission priorities corresponding to one or more categories of perception data that need to be transmitted, respectively.

[0159] As an example: The compression configuration can indicate one or more categories of perception data that need to be transmitted through the index (or identifier) of the category. For example: The index of category 1 is 1, the index of category 2 is 2, and the index of category 3 is 3. The second communication device needs the perception data of category 2 and category 3, and can carry the index 2 of category 2 and the index 3 of category 3 in the first indication information to indicate the first communication device to perform compression reporting on the perception data of category 2 and category 3, respectively.

[0160] In addition, the second communication device can also sort the indexes of the categories to be transmitted according to the corresponding transmission priorities (or importance levels), for example, sort them in descending order of transmission priorities (or importance levels), and indicate the transmission priorities corresponding to each category to be transmitted.

[0161] It can be understood that the compression configuration can also indicate the categories to be transmitted in ways such as a bitmap. For example, there are 10 categories of sensing data, and the 10 categories correspond one-to-one with 10 bit positions of the bit bitmap. A bit value of 1 in the bit bitmap indicates that the category corresponding to this bit needs to be transmitted, and a bit value of 0 indicates that the category corresponding to this bit does not need to be transmitted, and so on.

[0162] In some embodiments, the compression configuration can also indicate information such as the compression parameters and / or compression performance respectively corresponding to one or more categories of sensing data to be transmitted.

[0163] Taking the compression configuration also indicating the compression performance (or performance accuracy) respectively corresponding to one or more categories of sensing data to be transmitted as an example, the indexes of different compression performances (or performance accuracies) can be as shown in Table 1. One or more performance indexes carried by the compression performance field of the compression configuration can be used to determine the compression performance respectively corresponding to one or more categories of sensing data to be transmitted. Example: The categories of sensing data to be transmitted are Category 2 and Category 3, the compression performance field of the compression configuration includes Index 1 and 2, and the first communication device receiving the compression configuration information can determine that the compression performance corresponding to Category 2 is e-04 and the compression performance corresponding to Category 2 is e-05.

[0164] Table 1

[0165]

[0166] In the embodiments of the present application, the compression accuracy (or performance accuracy) can refer to information such as the mean-square error (MSE) between the data restored from the compressed data and the original data.

[0167] The compression parameters can include information such as the number of quantization bits. The number of quantization bits refers to the number of binary digits required to distinguish all quantization levels. For example, if there are 8 quantization levels, then 3-bit binary numbers can be used to distinguish them. The quantization accuracy can be expressed as the ratio of the range of values to be quantized (i.e., the quantization range) to the number of quantization levels. If the quantization compression method is adopted, the more the number of quantization levels, the higher the accuracy of the data restored after decompression. Therefore, the larger the number of quantization bits, the higher the compression accuracy (or performance accuracy). In some implementations, there may be a mapping relationship between the compression accuracy and the compression parameters (such as the number of quantization bits), and the number of quantization bits applied can be determined according to the compression accuracy.

[0168] It should be understood that in the embodiments of the present application, the second communication device sending the compression configuration to the first communication device is an optional operation. If the second communication device does not send the compression configuration to the first communication device, the first communication device may adopt a default compression configuration, such as the transmission priorities of various default categories, etc.

[0169] S1302: The first communication device generates compressed data and first indication information.

[0170] Specifically, the first communication device may generate compressed data and first indication information according to the compression configuration. Example: The first communication device may, according to one or more categories of sensed data that need to be transmitted indicated by the compression configuration, for each category of sensed data that needs to be transmitted, perform compression according to the compression method corresponding to this category to obtain the compressed data.

[0171] If the compression configuration also indicates information such as compression parameters and / or compression performance respectively corresponding to one or more categories of sensed data that need to be transmitted, when the first communication device performs compression on each category of sensed data that needs to be transmitted according to the compression method corresponding to this category to obtain the compressed data, the compression parameters (such as the number of quantization bits) used may be determined according to the compression parameters and / or compression performance and other information corresponding to this category.

[0172] It can be understood that information such as the compression parameters (such as the number of quantization bits) used by the compression method corresponding to each category may also be default-configured.

[0173] S1303: The first communication device sends the compressed data and the first indication information to the second communication device. Correspondingly, the second communication device receives the compressed data and the first indication information.

[0174] Taking the perception data to be transmitted including category 1 (ground), category 2 (building), category 3 (vehicle), and category 4 (others) as an example, where the first indication information (such as a 2D indication diagram can indicate the category of each perception data). The compressed data can include quantization compression parameters and quantized compressed data. For example: For category 1 (ground), a compression method based on 2D indication + fill value selection can be adopted, which can include an a-value index (defaultable), a-value boundaries min and max (quantization range) + a-value quantized data (which can be entropy-coded and other compressed a-value quantized data); for category 2 (building), a compression method of 2D indication + surface fitting can be adopted, which can include surface parameters, and can also include a residual quantization range + residual quantized data; for category 3 (vehicle), a compression method of 2D indication + in-frame sampled perception data (feature points) compression can be adopted, which can include an a-value index (defaultable), a-value boundaries min and max (quantization range) + a-value quantized data (which can be entropy-coded and other compressed a-value quantized data); for category 4 (others), a compression method based on 2D indication + fill value selection can be adopted, which can include an a-value index (defaultable), a-value boundaries min and max (quantization range) + a-value quantized data (which can be entropy-coded and other compressed a-value quantized data);

[0175] In addition, when the first communication device expands the correspondence between other categories and compression methods, it can also report the information (such as index) of the expanded category and the index of the corresponding compression method to the second communication device. For example, in Table 2, compression method indices 0, 1, 2, 3, 4, and 5 represent different compression methods. If the first communication device expands the correspondence between a certain category and a certain compression method in Table 2, it can report the information (such as index) of this category and the index of the corresponding compression method to the second communication device.

[0176] Table 2

[0177]

[0178] S1304: The second communication device generates multiple perception data.

[0179] The second communication device can recover multiple perception data according to the received first indication information and compressed data.

[0180] In some implementations, in cases such as resource constraints, it is also possible to preferentially transmit the compressed data of the category with a higher corresponding transmission priority (or importance) according to the transmission priority (or importance) corresponding to each category, and incrementally transmit the compressed data of the category with a lower transmission priority (or importance); or it is also possible to allocate different transmission resources for different categories according to the transmission priority (or importance) corresponding to different categories, etc.

[0181] As an example: The corresponding transmission priorities (or importance) can be configured for each category according to different scenarios. For example, in an obstacle avoidance scenario: the transmission priority of a moving target (such as a vehicle) > the transmission priority of a building > the transmission priority of the ground, and the compressed data of the moving target is preferentially transmitted; in a map construction scenario: the transmission priority of a building > the transmission priority of the ground > the transmission priority of a moving target (such as a vehicle), and the surrounding background environment is preferentially transmitted.

[0182] It can be understood that the transmission priorities (or importance) corresponding to each category can be pre-configured in the first communication device, or can be indicated by the second communication device. For example, the second communication device issues them through compression configuration, etc. The present application does not make any limitations in this regard.

[0183] Exemplarily, referring to Figure 14 the shown hierarchical schematic diagram, according to the transmission priorities corresponding to category 1, category 2, category 3, …, category m respectively, category 1, category 2, category 3, …, category m can be divided into layer 1, layer 2, …, layer n, where the transmission priority of any category in layer 1 is greater than the transmission priority of any category in layer 2. Similarly, the transmission priority of any category in layer 2 is greater than the transmission priority of any category in layer 3, …, and the transmission priority of any category in layer n - 1 is greater than the transmission priority of any category in layer n. Referring to Figure 15 the shown incremental layer transmission schematic diagram, the first communication device can first send the compressed data corresponding to each category in layer 1 (the base layer) to the second communication device. If an incremental layer feedback is received from the second communication device, continue to send the compressed data corresponding to each category in the next layer until the data transmission is completed or no incremental layer feedback is received from the second communication device. It can be understood that the first communication device can also send the first indication information (such as a 2D indication diagram) indicating the categories of the acquired perception data to the second communication device before or after sending the compressed data corresponding to each category in layer 1 (the base layer). Of course, the first indication information (such as a 2D indication diagram) can also be sent simultaneously with the compressed data corresponding to each category in layer 1 (the base layer). The present application does not make any limitations in this regard.

[0184] In some implementations, for the compression method based on 2D indication + plane fitting, the incremental layer transmission method can also be adopted to reduce the data volume.

[0185] Referring to Figure 16As shown, the first communication device can compress various types of sensed data to be transmitted according to a compression configuration to obtain various types of compressed data. For the sensed data of categories existing in a planar form such as buildings, the corresponding compressed data may only include planar parameters or planar identification points, and may also include reconstruction performance, where the reconstruction performance can be determined based on the error between the restoration point of the sensed data determined based on the first indication information (such as a 2D indication diagram) and the planar parameters (or planar identification points) and the true sensed data. If the reconstruction performance does not meet the reconstruction performance threshold T, the second communication device feeds back a second indication information (such as continuing to send residual signaling (or resources)) indicating to send the residuals corresponding to multiple sensed data to the second communication device. If the reconstruction performance information meets the reconstruction performance threshold T, this interaction can be ended. If the first communication device receives the second indication information (such as continuing to send residual signaling), it can send the residual data determined based on the true sensed data and the restored data to the second communication device, and the second communication device restores the data compressed based on the plane with high precision based on the residual data.

[0186] Referring to Figure 17 One of the schematic diagrams of the sensed data transmission simulation results shown, from the perspective of the first communication device at 360 degrees, is a schematic diagram of the bit rate (Rate) and mean square error (MSE) corresponding to two frames of sensed data samples of two intersecting streets, where the three-dimensional data included in the sensed data is taken as an example of three-dimensional coordinates. Among them, for the Draco scheme: KD-tree (KDtree) compression, quantization bits are 8 - 16; for the Proj scheme: it refers to a 2D indication diagram (two-dimensional data) + R distance sequence (the third-dimensional data), where the R distance sequence is quantized and compressed through the LZMA compression algorithm (quantization bits are 8 - 13).

[0187] The solution of this application can also be called a classification-based sensed data compression scheme (Proj class-based compression). Taking 4 categories as an example, for the ground: 2D indication diagram (two-dimensional data) + z value sequence (the third-dimensional data), + where the z value sequence is quantized and compressed through the LZMA compression algorithm (quantization bits are 8 - 13); for buildings: 2D indication diagram (two-dimensional data) + plane-based compression (low-precision plane parameters or plane identification points, high-precision plane parameters or plane identification points + residuals); for vehicles: 2D indication diagram (two-dimensional data) + r value sequence with odd positions in the 2D indication diagram as feature points, where the r value sequence is quantized and compressed through the LZMA compression algorithm (quantization bits are 8 - 13), and the r values at even positions are linearly interpolated based on the r values restored at odd positions; for others (such as utility poles, vegetation, etc.), 2D indication diagram (two-dimensional data) + r value sequence (the third-dimensional data), where the r value sequence is quantized and compressed through the LZMA compression algorithm (quantization bits are 8 - 13). It can be Figure 17 seen that when applying the solution of this application to transmit sensed data, the data volume (bit rate) decreases significantly.

[0188] Refer to Figure 18 As shown in the schematic diagram of the perception data transmission simulation result, from the perspective of the first communication device 120, it is a schematic diagram of the rate (Rate) and mean square error (MSE) corresponding to the average of 24-frame perception data samples of two intersecting streets. It can be seen that when the solution of this application is used to transmit perception data, the data volume (rate) also significantly decreases.

[0189] The communication device provided by the embodiments of the present application will be described below. Please refer to Figure 19 , Figure 19 is a schematic structural diagram of the communication device according to the embodiment of the present application. The communication device may include units or modules corresponding to all or part of the steps in the above method embodiments, and may be used to execute the steps performed by the first communication device or the second communication device in the above embodiments. For specific reference, please refer to the relevant descriptions in the above method embodiments.

[0190] As Figure 19 shown, the communication device 1900 includes a processing unit 1910 and an interface unit 1920. The processing unit 1910 may be a processor or a processing circuit, and the interface unit 1920 may also be a transceiver unit or an input / output interface. The communication device 1900 can be used to implement the steps performed by the first communication device or the second communication device in the above embodiments.

[0191] When the communication device 1900 is used to implement the steps performed by the first communication device in the above embodiments:

[0192] The processing unit 1910 is configured to generate compressed data and first indication information. The first indication information indicates the categories of multiple perception data. The compressed data is obtained by compressing multiple perception data according to the compression method corresponding to the categories of multiple perception data. Among them, the perception data of the same category in multiple perception data corresponds to the same compression method in the corresponding relationship between different categories of perception data and different compression methods; the interface unit 1920 is configured to send the compressed data and the first indication information to the second communication device.

[0193] Exemplarily: The categories of multiple perception data are determined according to the categories of the target objects corresponding to the multiple perception data.

[0194] In a possible design, the corresponding relationship between different categories of perception data and different compression methods includes: a compression method of quantizing and compressing perception data without sampling, a compression method of quantizing and compressing perception data with sampling, a compression method based on the geometric structure corresponding to the perception data, a compression method based on the border corresponding to the perception data, a compression method of geometric projection based on the perception data, etc., one or more of them.

[0195] In a possible design, any piece of sensing data includes multi-dimensional data, and the first indication information further indicates the location information of multiple pieces of sensing data, where the location information of any piece of sensing data is determined according to two dimensions of data in the multi-dimensional data of the sensing data.

[0196] In a possible design, multiple pieces of sensing data are of the first category. When the processing unit 1910 generates compressed data, it is specifically configured to perform quantization compression on the third-dimensional data of the multiple pieces of sensing data to obtain a quantization range and quantization-compressed data, where the third-dimensional data of any piece of sensing data is the data other than the two dimensions of data corresponding to the location information in the multi-dimensional data of the sensing data.

[0197] In a possible design, the multi-dimensional data of any piece of sensing data includes at least three of θ, R, x, y, z, r, where θ represents the vertical angle in the spherical coordinate system, represents the horizontal angle in the spherical coordinate system, x represents the abscissa in the Cartesian coordinate system, y represents the ordinate in the Cartesian coordinate system, z represents the vertical coordinate in the Cartesian coordinate system, r represents the projection distance on the horizontal plane in the Cartesian coordinate system, R represents the distance to the origin in the spherical coordinate system, and the third-dimensional data of the multiple pieces of sensing data other than the two dimensions of data corresponding to the location information is selected according to the information entropy or value range of each dimension of data other than the two dimensions of data in the multi-dimensional data.

[0198] In a possible design, the category of multiple pieces of sensing data is the second category. When the processing unit 1910 generates compressed data, it is specifically configured to determine the geometric structure parameters or geometric structure identification points of at least one geometric structure where the multiple pieces of sensing data are located according to the multiple pieces of sensing data, where the first indication information further indicates the geometric structure where the multiple pieces of sensing data are located, and the geometric structure parameters or geometric structure identification points of any geometric structure are used to determine the geometric structure.

[0199] In a possible design, the processing unit 1910 is further configured to perform quantization compression on the residuals corresponding to the multiple pieces of sensing data to obtain quantization-compressed data, where the residuals corresponding to the multiple pieces of sensing data are determined according to the multiple pieces of sensing data and the multiple restoration data corresponding to the multiple pieces of sensing data, and the restoration data corresponding to any piece of sensing data is determined according to the location information of the sensing data and the geometric structure where the sensing data is located.

[0200] In a possible design, when the categories of multiple perception data are the third category and the processing unit 1910 generates compressed data, it is specifically used to determine at least one border corresponding to the multiple perception data, where the first indication information also indicates the border where the multiple perception data is located; perform quantization compression on the border endpoints of the multiple perception data and / or the perception data located at the border endpoints to obtain quantization compressed data; or, perform quantization compression on the third-dimensional data of the multiple perception data located at any border to obtain quantization compressed data, where the third-dimensional data of any perception data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

[0201] In a possible design, when the processing unit 1910 performs quantization compression on the third-dimensional data of the multiple perception data located at any border to obtain quantization compressed data, it is specifically used to perform quantization compression on the third-dimensional data of all the perception data located at the border to obtain quantization compressed data; or, perform quantization compression on the third-dimensional data of the border endpoints of the multiple perception data and / or the perception data located at the border endpoints to obtain quantization compressed data; or, perform quantization compression on the third-dimensional data of multiple sampled perception data among the multiple perception data to obtain quantization compressed data, where the number of the multiple sampled perception data is less than the number of the multiple perception data.

[0202] In a possible design, the interface unit 1920 is further configured to receive a compression configuration from a second communication device, where the compression configuration indicates one or more categories of perception data that need to be transmitted, and the categories of the multiple perception data belong to the categories of perception data that need to be transmitted indicated by the compression configuration.

[0203] In a possible design, the compression configuration further indicates the compression parameters and / or compression performance respectively corresponding to one or more categories of perception data that need to be transmitted, where the compressed data meets the requirements of the compression parameters and / or compression performance.

[0204] In a possible design, the compression configuration further indicates the transmission priorities respectively corresponding to one or more categories of perception data that need to be transmitted, and the compressed data and the first indication information are sent to the second communication device according to the transmission priorities corresponding to the categories of the multiple perception data.

[0205] In a possible design, the compressed data further includes the reconstruction performance of multiple sensed data. The interface unit 1920 is further configured to receive second indication information from a second communication device. The second indication information indicates to send residuals corresponding to the multiple sensed data, and the second indication information is sent by the second communication device when the reconstruction performance does not meet the reconstruction performance threshold; and send the residuals corresponding to the multiple sensed data to the second communication device. The residuals corresponding to the multiple sensed data are determined according to the multiple sensed data and the multiple restored data corresponding to the multiple sensed data. The restored data corresponding to any sensed data is determined according to the position information corresponding to the sensed data and the geometric structure where the sensed data is located.

[0206] When the communication device 1900 is used to implement the steps performed by the second communication device in the above embodiments:

[0207] The interface unit 1920 is configured to receive compressed data and first indication information from a first communication device. The first indication information indicates the categories of multiple sensed data. The compressed data is obtained by compressing the multiple sensed data according to the compression method corresponding to the categories of the multiple sensed data. The processing unit 1910 is configured to generate multiple sensed data. The multiple sensed data is obtained by decompressing the compressed data based on the decompression method corresponding to the categories of the multiple sensed data. The sensed data of the same category in the multiple sensed data corresponds to the same decompression method in the corresponding relationship between different categories of sensed data and different decompression methods.

[0208] Exemplarily: The categories of the multiple sensed data are determined according to the categories of the target objects corresponding to the multiple sensed data.

[0209] In a possible design, the corresponding relationship between different categories of sensed data and different compression methods includes: a compression method of quantizing and compressing the sensed data without sampling, a compression method of quantizing and compressing the sensed data by sampling, a compression method based on the geometric structure corresponding to the sensed data, a compression method based on the border corresponding to the sensed data, a compression method of the sensed data based on geometric projection, etc., one or more of them.

[0210] In a possible design, any sensed data includes multi-dimensional data. The first indication information further indicates the position information of the multiple sensed data. The position information of any sensed data is determined according to two-dimensional data in the multi-dimensional data of the sensed data; the multiple sensed data is obtained by decompressing the compressed data based on the decompression method corresponding to the categories of the multiple sensed data and the position information of the multiple sensed data.

[0211] In a possible design, the categories of multiple sensing data are the first category, and the compressed data includes quantization compressed data obtained by quantizing and compressing the third-dimensional data of the multiple sensing data, where the third-dimensional data of any one of the sensing data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the sensing data.

[0212] In a possible design, the categories of multiple sensing data are the second category, the compressed data includes the geometric structure parameters or geometric structure identification points of at least one geometric structure where the multiple sensing data are located, and the first indication information further indicates the geometric structure where the multiple sensing data are located. The geometric structure parameters or geometric structure identification points of any geometric structure are used to determine the geometric structure.

[0213] In a possible design, the compressed data further includes quantization compressed data obtained by quantizing and compressing the residuals corresponding to the multiple sensing data, where the residuals corresponding to the multiple sensing data are determined according to the multiple sensing data and the multiple restoration data corresponding to the multiple sensing data, and the restoration data corresponding to any one of the sensing data is determined according to the position information corresponding to the sensing data and the geometric structure where the sensing data is located.

[0214] In a possible design, the categories of multiple sensing data are the third category, the first indication information further indicates the border where the multiple sensing data are located, and the compressed data includes quantization compressed data obtained by quantizing and compressing the border endpoints of the multiple sensing data and / or the sensing data located at the border endpoints, or quantization compressed data obtained by quantizing and compressing the third-dimensional data of the multiple sensing data located at any border indicated by the first indication information, where the third-dimensional data of any one of the sensing data is the data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the sensing data.

[0215] In a possible design, the quantization compressed data obtained by quantizing and compressing the third-dimensional data of the multiple sensing data located at any border includes: quantization compressed data obtained by quantizing and compressing the third-dimensional data of all the sensing data located at the border; or, quantization compressed data obtained by quantizing and compressing the border endpoints of the multiple sensing data and / or the third-dimensional data located at the border endpoints; or, quantization compressed data obtained by quantizing and compressing the third-dimensional data of multiple sampled sensing data among the multiple sensing data, where the number of the multiple sampled sensing data is less than the number of the multiple sensing data.

[0216] In a possible design, the interface unit 1920 is further configured to send a compression configuration to the first communication device, and the compression configuration indicates one or more categories of sensing data that need to be transmitted, and the categories of the multiple sensing data belong to the categories of sensing data that need to be transmitted indicated by the compression configuration.

[0217] In a possible design, the compression configuration further indicates compression parameters and / or compression performance corresponding to one or more categories of sensed data to be transmitted, where the compressed data meets the requirements of the compression parameters and / or compression performance.

[0218] In a possible design, the compression configuration further indicates the transmission priorities corresponding to one or more categories of sensed data to be transmitted.

[0219] In a possible design, the compressed data further includes the reconstruction performance of multiple sensed data. The interface unit 1920 is further configured to, when the reconstruction performance does not meet the reconstruction performance threshold, send second indication information to the first communication device, where the second indication information indicates to send the residuals corresponding to the multiple sensed data; receive the residuals corresponding to the multiple sensed data from the first communication device, where the residuals corresponding to the multiple sensed data are determined according to the multiple sensed data and the multiple restored data corresponding to the multiple sensed data, and the restored data corresponding to any sensed data is determined according to the position information corresponding to the sensed data and the geometric structure where the sensed data is located.

[0220] As Figure 20 shown, the present application further provides a communication device 2000, including a processor 2010, and may further include a communication interface 2020. The processor 2010 and the communication interface 2020 are coupled to each other. It can be understood that the communication interface 2020 may be a transceiver, an input / output interface, an input interface, an output interface, an interface circuit, etc. Optionally, the communication device 2000 may further include a memory 2030, configured to store instructions executed by the processor 2010 or store input data required for the processor 2010 to run instructions or store data generated after the processor 2010 runs instructions. Wherein, the memory 2030 may be a physically independent unit, or may be coupled to the processor 2010, or the processor 2010 includes the memory 2030.

[0221] When the communication device 2000 is used to implement the steps performed by the first communication device and the second communication device in the above embodiments, the processor 2010 may be used to implement the functions of the above processing unit 1910, and the communication interface 2020 may be used to implement the functions of the above interface unit 1920.

[0222] It can be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), logic circuits, field programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0223] The method steps in the embodiments of the present application may be implemented in a hardware manner or by a processor executing software instructions. The software instructions may be composed of corresponding software modules, and the software modules may be stored in a random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, removable hard disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may be located in an ASIC. Additionally, the ASIC may be located in a network device or a terminal device. Of course, the processor and the storage medium may also exist as discrete components in the network device or the terminal device.

[0224] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one network device, terminal, computer, server, or data center to another network device, terminal, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a digital video disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or can include both volatile and non-volatile types of storage media.

[0225] In various embodiments of the present application, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0226] In addition, it should be understood that in the embodiments of the present application, the term "exemplary" is used to mean an example, illustration, or description. Any embodiment or design described as "exemplary" in the present application should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of the term "exemplary" is intended to present concepts in a specific manner.

[0227] It can be understood that the various numerical numbers involved in the embodiments of the present application are only for the convenience of description and are not used to limit the scope of the embodiments of the present application. The magnitudes of the serial numbers of the above processes do not mean the order of execution is prior or subsequent, and the order of execution of each process should be determined by its function and internal logic.

Claims

1. A method for transmitting perceptual data, characterized in that: Applied to a first communication device, comprising: Generate compressed data and first indication information, wherein the first indication information indicates categories of a plurality of perception data, the compressed data is obtained by compressing the plurality of perception data according to compression methods corresponding to the categories of the plurality of perception data, wherein perception data of the same category in the plurality of perception data corresponds to the same compression method in a correspondence relationship between categories of different perception data and different compression methods; The compressed data and the first indication information are sent to the second communication device.

2. The method according to claim 1, characterized in that The categories of the plurality of perception data are determined according to the categories of the target objects corresponding to the plurality of perception data.

3. The method according to claim 1 or 2, characterized in that The correspondence between the categories of different perception data and different compression methods includes at least one of the following compression methods: A compression method that quantizes and compresses sensory data without sampling; A compression method for quantizing and compressing the perceptual data samples; Compression based on the geometric structure corresponding to the perceived data; A compression method based on compressing the bounding box corresponding to the perceived data; Compression method for perceptual data based on geometric projection.

4. The method according to claim 1 or 2, characterized in that: Any of the perception data includes multi-dimensional data, and the first indication information further indicates position information of the multiple perception data, wherein the position information of any of the perception data is determined based on two-dimensional data in the multi-dimensional data of the perception data.

5. The method according to claim 4, characterized in that The plurality of perception data are of the first category, and the generating of compressed data comprises: The third-dimensional data of the plurality of perception data are quantized and compressed to obtain quantized compressed data, wherein the third-dimensional data of any of the perception data is data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

6. The method according to claim 5, characterized in that Any of the multi-dimensional data of the perception data includes θ, At least three of R, x, y, z, r, where θ represents the vertical angle in the spherical coordinate system, represents the horizontal angle in the spherical coordinate system, R represents the distance to the origin in the spherical coordinate system, x represents the horizontal coordinate in the Cartesian coordinate system, y represents the vertical coordinate in the Cartesian coordinate system, z represents the vertical coordinate in the Cartesian coordinate system, and r represents the projection distance on the horizontal plane in the Cartesian coordinate system. The third-dimensional data in the multiple perception data except the two-dimensional data corresponding to the position information is selected according to the information entropy or value range of each dimensional data in the multidimensional data except the two-dimensional data.

7. The method according to claim 4, characterized in that The category of the plurality of perception data is the second category, and the generating of compressed data includes: Based on the multiple perception data, determine geometric structure parameters or geometric structure identification points of at least one geometric structure where the multiple perception data are located, wherein the first indication information also indicates the geometric structure where the multiple perception data are located, and the geometric structure parameters or geometric structure identification points of any one of the geometric structures are used to determine the geometric structure.

8. The method according to claim 7, characterized in that The method further comprises: The residuals corresponding to the multiple perception data are quantized and compressed to obtain quantized compressed data, wherein the residuals corresponding to the multiple perception data are determined according to the multiple perception data and multiple restored data corresponding to the multiple perception data, wherein the restored data corresponding to any one of the perception data is determined according to the position information of the perception data and the geometric structure where the perception data is located.

9. The method according to claim 4, characterized in that The category of the plurality of perception data is the third category, and the generating of compressed data comprises: Determine at least one border corresponding to the plurality of perception data, wherein the first indication information further indicates the border in which the plurality of perception data are located; Quantizing and compressing the border endpoints of the plurality of perception data and / or the perception data located at the border endpoints to obtain quantized compressed data; or, The third-dimensional data of the plurality of perception data located in any of the borders are quantized and compressed to obtain quantized compressed data, wherein the third-dimensional data of any of the perception data is data other than the two-dimensional data corresponding to the position information in the multi-dimensional data of the perception data.

10. The method according to claim 9, characterized in that The third dimension data of the plurality of perception data located at any of the borders is quantized and compressed to obtain quantized compressed data, including: Quantizing and compressing the third-dimensional data of all the perception data located in the border to obtain quantized compressed data; or, Quantizing and compressing the border endpoints of the plurality of perception data and / or the third dimension data of the perception data located at the border endpoints to obtain quantized compressed data; or, The third dimension data of multiple sampled perception data among the multiple perception data are quantized and compressed to obtain quantized compressed data, wherein the number of the multiple sampled perception data is less than the number of the multiple perception data.

11. The method according to any one of claims 1 to 10, characterized in that The method further comprises: Receive a compression configuration from the second communication device, the compression configuration indicating one or more categories of perception data that need to be transmitted, and compression parameters and / or compression performance corresponding to the one or more categories of perception data that need to be transmitted, wherein the multiple categories of perception data belong to the categories of perception data that need to be transmitted indicated by the compression configuration, and the compressed data meets the requirements of the compression parameters and / or compression performance.

12. The method according to claim 11, characterized in that The compression configuration also indicates the transmission priorities corresponding to the one or more categories of perception data that need to be transmitted, and the compressed data and the first indication information are sent to the second communication device according to the transmission priorities corresponding to the categories of the multiple perception data.

13. The method according to claim 7, characterized in that The compressed data also includes reconstruction performance of the plurality of perception data, and the method further includes: receiving second indication information from the second communication device, where the second indication information indicates to send residuals corresponding to the plurality of perception data, and the second indication information is sent by the second communication device when the reconstruction performance does not meet the reconstruction performance threshold; The residuals corresponding to the multiple perception data are sent to the second communication device, and the residuals corresponding to the multiple perception data are determined based on the multiple perception data and the multiple restored data corresponding to the multiple perception data, wherein the restored data corresponding to any one of the perception data is determined based on the location information of the perception data and the geometric structure where the perception data is located.

14. A method for transmitting perceptual data, characterized in that: Applied to a second communication device, comprising: receiving compressed data and first indication information from a first communication device, wherein the first indication information indicates categories of a plurality of perception data, and the compressed data is obtained by compressing the plurality of perception data according to compression methods corresponding to the categories of the plurality of perception data; The multiple perception data are generated, and the multiple perception data are obtained by decompressing the compressed data based on a decompression method corresponding to the category of the multiple perception data. The perception data of the same category in the multiple perception data corresponds to the same decompression method in the correspondence between the categories of different perception data and different decompression methods.

15. The method according to claim 14, characterized in that The categories of the plurality of perception data are determined according to the categories of the target objects corresponding to the plurality of perception data.

16. The method according to claim 14 or 15, characterized in that The correspondence between the categories of different perception data and different compression methods includes at least one of the following compression methods: A compression method that quantizes and compresses sensory data without sampling; A compression method for quantizing and compressing the perceptual data samples; Compression based on the geometric structure corresponding to the perceived data; A compression method based on compressing the bounding box corresponding to the perceived data; Compression method for perceptual data based on geometric projection.

17. The method according to claim 14 or 15, characterized in that Any of the perception data includes multi-dimensional data, and the first indication information further indicates position information of the plurality of perception data, wherein the position information of any of the perception data is determined according to two-dimensional data in the multi-dimensional data of the perception data; The multiple perception data are obtained by decompressing the compressed data based on the decompression methods corresponding to the categories of the multiple perception data and the location information of the multiple perception data.

18. The method according to claim 17, characterized in that The category of the multiple perception data is the first category, and the compressed data includes quantized compressed data obtained by quantizing and compressing the third dimensional data of the multiple perception data, wherein the third dimensional data of any of the perception data is data in the multidimensional data of the perception data except the two-dimensional data corresponding to the position information.

19. The method according to claim 17, characterized in that The category of the multiple perception data is the second category, the compressed data includes geometric structure parameters or geometric structure identification points of at least one geometric structure where the multiple perception data are located, and the first indication information also indicates the geometric structure where the multiple perception data are located, and the geometric structure parameters or geometric structure identification points of any one of the geometric structures are used to determine the geometric structure.

20. The method of claim 19, wherein: The compressed data also includes quantized compressed data obtained by quantizing and compressing the residuals corresponding to the multiple perception data, wherein the residuals corresponding to the multiple perception data are determined based on the multiple perception data and multiple restored data corresponding to the multiple perception data, wherein the restored data corresponding to any one of the perception data is determined based on the position information of the perception data and the geometric structure where the perception data is located.

21. The method of claim 17, wherein: The category of the multiple perception data is the third category, the first indication information also indicates the border where the multiple perception data are located, and the compressed data includes quantized compressed data obtained by quantizing and compressing the border endpoints of the multiple perception data and / or the perception data located at the border endpoints, or quantized compressed data obtained by quantizing and compressing the third dimensional data of the multiple perception data located at any border indicated by the first indication information, wherein the third dimensional data of any of the perception data is data in the multidimensional data of the perception data except the two-dimensional data corresponding to the position information.

22. The method according to claim 21, characterized in that The quantized compressed data obtained by quantizing and compressing the third-dimensional data of the plurality of perception data located in any of the borders includes: Quantized compressed data obtained by quantizing and compressing the third-dimensional data of all the perception data located in the border; or, Quantized compressed data obtained by quantizing and compressing the border endpoints of the plurality of perception data and / or the third dimension data of the perception data located at the border endpoints; or, The quantized compressed data is obtained by quantizing and compressing the third dimension data of a plurality of sampled perceptual data among the plurality of perceptual data, wherein the number of the plurality of sampled perceptual data is less than the number of the plurality of perceptual data.

23. The method according to any one of claims 14 to 22, characterized in that The method further comprises: A compression configuration is sent to the first communication device, wherein the compression configuration indicates one or more categories of perception data that need to be transmitted, and compression parameters and / or compression performance corresponding to the one or more categories of perception data that need to be transmitted, wherein the multiple categories of perception data belong to the categories of perception data that need to be transmitted indicated by the compression configuration, and the compressed data meets the requirements of the compression parameters and / or compression performance.

24. The method of claim 23, wherein: The compression configuration further indicates transmission priorities corresponding to the one or more categories of the perception data to be transmitted.

25. The method of claim 19, wherein: The compressed data also includes reconstruction performance of the plurality of perception data, and the method further includes: When the reconstruction performance does not meet the reconstruction performance threshold, sending second indication information to the first communication device, where the second indication information indicates sending residuals corresponding to the plurality of perception data; Receive residuals corresponding to the multiple perception data from the first communication device, wherein the residuals corresponding to the multiple perception data are determined based on the multiple perception data and multiple restored data corresponding to the multiple perception data, and the restored data corresponding to any one of the perception data is determined based on location information of the perception data and a geometric structure where the perception data is located.

26. A communication device, characterized in that: The method comprises a module or a unit for executing the method as claimed in any one of claims 1 to 25.

27. A communication device, characterized in that: It includes a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices outside the communication device and transmit them to the processor, or send signals from the processor to other communication devices outside the communication device, and the processor is used to implement the method as described in any one of claims 1-25 through a logic circuit or execution instructions.

28. A computer program product, characterized in that The method comprises a computer program or an instruction, and when the computer program or the instruction is executed by a processor, the method according to any one of claims 1 to 25 is implemented.

29. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction. When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 25 is implemented.

30. A communication system, characterized in that: The communication system comprises a first communication device and a second communication device; The first communication device is used to implement the method according to any one of claims 1 to 13; The second communication device is used to implement the method as described in any one of claims 14-25.

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