Sensing result transmission method, and apparatus, device, medium and program product

Abstract

The present application belongs to the technical field of communications. Disclosed are a sensing result transmission method, and an apparatus, a device, a medium and a program product. The method is executed by a first node. The method comprises: sending a sensing result on the basis of model information of one or more first models, wherein the first models are mathematical models configured to represent echo characteristics of a first echo. Since the first echo is a typical echo in a sensing scenario, complete information may not be sent. Instead, the sensing result is sent with reference to the model information of the first models, thereby reducing the total amount of information to be sent, lowering the overheads of wireless spectrum resources, and improving information transmission efficiency.

Description

Methods, devices, equipment, media, and program products for transmitting sensing results. Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, device, medium, and program product for transmitting sensing results. Background Technology

[0002] Wireless communication and sensing are two major applications of modern radio frequency technology. Sensing uses radio waves to detect environmental parameters to achieve functions such as target localization, action recognition, and target imaging. The sensing receiving node feeds back complete information to the sensing management node, enabling the sensing management node to perform tasks such as analyzing the sensed target and determining its direction of movement based on the complete information.

[0003] In the field of sensing, providing complete information to the sensing target consumes a large amount of wireless spectrum resources and affects transmission efficiency. Summary of the Invention

[0004] This application provides a method, apparatus, device, medium, and program product for transmitting sensing results, the technical solution of which includes at least:

[0005] According to one aspect of the embodiments of this application, a method for transmitting sensing results is provided. The method is executed by a first node, and the method includes: sending the sensing results based on model information of one or more first models; wherein the first model is a mathematical model used to characterize the echo features of a first echo.

[0006] According to another aspect of the embodiments of this application, a method for transmitting model information is provided, the method being executed by a first node, the method comprising:

[0007] Receive model information of one or more first models; wherein, the model information is used by the first node to send the sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo.

[0008] According to another aspect of the embodiments of this application, a method for transmitting sensing results is provided, the method being executed by a second node, the method comprising: receiving sensing results; wherein the sensing results are sent by a first node based on model information of one or more first models, the first model being a mathematical model for characterizing the echo characteristics of a first echo.

[0009] According to another aspect of the embodiments of this application, a method for transmitting model information is provided, the method being executed by a second node, the method comprising: sending model information of one or more first models; wherein the model information is used by the first node to send sensing results, and the first model is a mathematical model used to characterize the echo features of a first echo.

[0010] According to another aspect of the embodiments of this application, a first apparatus is provided, the first apparatus comprising: a transmitting module, configured to transmit a sensing result based on model information of one or more first models; wherein the first model is a mathematical model for characterizing the echo features of a first echo.

[0011] According to another aspect of the embodiments of this application, a second device is provided, the second device comprising: a receiving module for receiving a sensing result; wherein the sensing result is sent by a first device based on model information of one or more first models, the first model being a mathematical model for characterizing the echo characteristics of a first echo.

[0012] According to another aspect of the embodiments of this application, a first device is provided, the first device comprising: a receiving module, configured to receive model information of one or more first models; wherein the model information is used by the first device to send sensing results, and the first model is a mathematical model for characterizing the echo features of a first echo.

[0013] According to another aspect of the embodiments of this application, a second apparatus is provided, the second apparatus comprising: a transmitting module, configured to transmit model information of one or more first models; wherein the model information is used by the first apparatus to transmit sensing results, and the first model is a mathematical model for characterizing echo features of a first echo.

[0014] According to another aspect of the embodiments of this application, a first node is provided, the first node comprising:

[0015] A processor; a transceiver connected to the processor; a memory for storing executable instructions of the processor; wherein the processor is configured to load and execute the executable instructions to achieve the transmission method of perception results and / or the transmission method of model information as described above.

[0016] According to another aspect of the embodiments of this application, a second node is provided, the second node comprising:

[0017] A processor; a transceiver connected to the processor; a memory for storing executable instructions of the processor; wherein the processor is configured to load and execute the executable instructions to achieve the transmission method of perception results and / or the transmission method of model information as described above.

[0018] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores at least one program that is loaded and executed by a processor to implement a method for transmitting perception results and / or a method for transmitting model information as described in the various aspects above.

[0019] According to another aspect of the embodiments of this application, a chip is provided, the chip including programmable logic circuits and / or program instructions, which, when the chip is running on a first node, are used to implement the method for transmitting the perception results and / or the method for transmitting model information of the above-mentioned aspects; and when the chip is running on a second node, are used to implement the method for transmitting the perception results and / or the method for transmitting model information of the above-mentioned aspects.

[0020] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium, a processor retrieving the computer instructions from the computer-readable storage medium, and the processor executing the computer instructions to implement a method for transmitting perception results and / or a method for transmitting model information as described in the various aspects above.

[0021] The technical solutions provided in this application embodiment may include the following beneficial effects:

[0022] This method transmits sensing results based on model information from one or more first models. The first model is a mathematical model used to characterize the echo features of the first echo. The first echo is a typical echo in the sensing scenario, so it is not necessary to transmit complete information. Instead, the sensing results are transmitted with reference to the model information of the first model, which reduces the total amount of information that needs to be transmitted, reduces the overhead of wireless spectrum resources, and improves the efficiency of information transmission. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 shows a schematic diagram of a mobile communication system provided in an exemplary embodiment of this application;

[0025] Figure 2 shows a schematic diagram of the sensing modes provided by the relevant technologies;

[0026] Figure 3 shows a schematic diagram of multiple sensing nodes participating in sensing provided by related technologies;

[0027] Figure 4 shows a schematic diagram of the micro-motion sensing applications provided by the related technologies;

[0028] Figure 5 shows a flowchart of a method for transmitting sensing results provided in an exemplary embodiment of this application;

[0029] Figure 6 shows a flowchart of a method for transmitting sensing results provided in an exemplary embodiment of this application;

[0030] Figure 7 shows a waveform diagram of the first and second models provided in an exemplary embodiment of this application;

[0031] Figure 8 shows a flowchart of a method for transmitting sensing results provided in an exemplary embodiment of this application;

[0032] Figure 9 shows a flowchart of a method for transmitting sensing results provided in an exemplary embodiment of this application;

[0033] Figure 10 shows a flowchart of a method for transmitting model information provided in an exemplary embodiment of this application;

[0034] Figure 11 shows a flowchart of a method for transmitting sensing results provided in an exemplary embodiment of this application;

[0035] Figure 12 shows a flowchart of a method for transmitting model information provided in an exemplary embodiment of this application;

[0036] Figure 13 shows a block diagram of a first apparatus provided in an exemplary embodiment of this application;

[0037] Figure 14 shows a block diagram of a second apparatus provided in an exemplary embodiment of this application;

[0038] Figure 15 shows a block diagram of a first apparatus provided in an exemplary embodiment of this application;

[0039] Figure 16 shows a block diagram of a second apparatus provided in an exemplary embodiment of this application;

[0040] Figure 17 shows a schematic diagram of the structure of a first node provided in an exemplary embodiment of this application;

[0041] Figure 18 shows a schematic diagram of the structure of a second node provided in an exemplary embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Exemplary embodiments will be described in detail here, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0043] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “a,” “the,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0044] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0045] The technical solutions described in some embodiments of this application can be applied to various communication systems, such as: Long Term Evolution (LTE) systems, Advanced Long Term Evolution (LTE-A) systems, New Radio (NR) systems, evolution systems of NR systems, LTE-based access to unlicensed spectrum (LTE-U) systems, NR-based access to unlicensed spectrum (NR-U) systems, Non-Terrestrial Networks (NTN) systems, Universal Mobile Telecommunication System (UMTS), Wireless Local Area Networks (WLAN), Wireless Fidelity (WiFi), 5th-Generation (5G) systems, cellular IoT systems, cellular passive IoT systems, and can also be applied to subsequent evolution systems of 5G NR systems, as well as 6G and subsequent evolution systems.

[0046] It should be understood that in some embodiments of this application, "5G" may also be referred to as "5G NR" or "NR".

[0047] It should be understood that in the description of the embodiments of this application, the term "correspondence" may indicate that there is a direct or indirect correspondence between the two, or that there is an association between the two, or that there is a relationship of instruction and being instructed, configuration and being configured, etc.

[0048] In this embodiment of the application, "predefined" can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including terminal devices and network devices). This application does not limit the specific implementation method. For example, predefined can refer to what is defined in the protocol.

[0049] In this application embodiment, "protocol" may refer to standard protocols in the field of communication, such as LTE protocol, NR protocol and related protocols applied to future communication systems, and this application does not limit it.

[0050] In the embodiments of this application, any concept that can represent the meaning related to perception can replace "perception", such as positioning, ranging, velocity measurement, angle measurement, target imaging, target detection, target tracking and target recognition, etc.

[0051] Figure 1 shows a schematic diagram of a mobile communication system provided in an exemplary embodiment of this application. The mobile communication system includes a network device 110 and a terminal device 120, and may or may not include a terminal device 130; this application does not limit this.

[0052] The network device 110 in this application provides wireless communication functionality. This network device 110 includes, but is not limited to: an evolved Node B (eNB), a Radio Network Controller (RNC), a Node B (NB), a Base Station Controller (BSC), a Base Transceiver Station (BTS), a Home Evolved Node B (or Home Node B, HNB), a Base Band Unit (BBU), an Access Point (AP) in a Wireless Fidelity (Wi-Fi) system, a wireless relay node, a wireless backhaul node, a Transmission Point (TP), or a Transmission and Reception Point (TRP), etc. It can also be used for next-generation Node B (Next Generation Node) systems in 5G mobile communication systems. B, gNB) or transmission point (TRP or TP), or, in a 5G system, one or a group of antenna panels (including multiple antenna panels) of a base station, or, network nodes constituting a gNB or transmission point, such as baseband unit (BBU) or distributed unit (DU), or base stations in Beyond Fifth Generation (B5G) or 6th Generation (6G) mobile communication systems, or core network (CN), fronthaul, backhaul, radio access network (RAN), network slicing, etc., or serving cell, primary cell (PCell), primary secondary cell (PSCell), special cell (SpCell), secondary cell (SCell), neighboring cell, etc. of terminal equipment.

[0053] The terminal equipment 120 in this application is also referred to as user equipment (UE), access terminal equipment, user unit, user station, mobile station, mobile station, remote station, remote terminal equipment, mobile device, user terminal equipment, terminal equipment, wireless communication equipment, user agent, or user device. The terminal devices include, but are not limited to: handheld devices, wearable devices, in-vehicle devices, and IoT devices, such as: mobile phones, tablets, e-readers, laptops, desktop computers, televisions, game consoles, mobile internet devices (MID), augmented reality (AR) terminal devices, virtual reality (VR) terminal devices, mixed reality (MR) terminal devices, extended reality (XR) terminal devices, baffle reality (BR) terminal devices, cinematic reality (CR) terminal devices, deceive reality (DR) terminal devices, wearable devices, controllers, controllers, wireless terminal devices in industrial control, wireless terminal devices in self-driving, wireless terminal devices in remote medical care, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, and smart city technologies. Wireless terminal devices in cities, smart homes, remote medical surgeries, cellular phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), Set-Top Boxes (STBs), Customer Premise Equipment (CPEs), etc.

[0054] In some embodiments, network device 110 and terminal device 120 communicate with each other through some air interface technology, such as the Uu interface.

[0055] For example, there are two communication scenarios between network device 110 and terminal device 120: uplink communication scenario and downlink communication scenario. Uplink communication, or uplink transmission, refers to sending signals or data to network device 110; downlink communication, or downlink transmission, refers to sending signals or data to terminal device 120.

[0056] In some embodiments, terminal device 120 and terminal device 130 communicate with each other through some air interface technology, such as the PC5 interface.

[0057] For example, there are two communication scenarios between terminal device 120 and terminal device 130: a first side-by-side communication scenario and a second side-by-side communication scenario. The first side-by-side communication refers to terminal device 120 sending signals to terminal device 130; the second side-by-side communication refers to terminal device 130 sending signals to terminal device 120.

[0058] In some embodiments, terminal device 120 and terminal device 130 are both within network coverage and located in the same cell, or terminal device 120 and terminal device 130 are both within network coverage but located in different cells, or terminal device 120 is within network coverage but terminal device 130 is outside network coverage.

[0059] In some embodiments of this application, "NR" may also be referred to as a 5G NR system or a 5G system. The 5G mobile communication system may include non-standalone (NSA) and / or standalone (SA) networking.

[0060] The technical solutions provided in the embodiments of this application can also be applied to Machine-Type Communication (MTC), Long Term Evolution-Machine (LTE-M) technology, Device-to-Device (D2D) networks, Machine-to-Machine (M2M) networks, Internet of Things (IoT) networks, or other networks. Among them, IoT networks may include, for example, vehicle-to-everything (V2X) networks. The communication methods in V2X systems are collectively referred to as Vehicle to X (V2X), where X can represent anything. For example, V2X may include: Vehicle to Vehicle (V2V) communication, Vehicle to Infrastructure (V2I) communication, Vehicle to Pedestrian (V2P) communication, or Vehicle to Network (V2N) communication, etc.

[0061] The mobile communication system provided in this application embodiment can be applied to at least one of the following communication scenarios: uplink communication scenario, downlink communication scenario, and sidelink communication scenario.

[0062] In this embodiment of the application, the first node is usually a terminal device 120 and the second node is a network device 110, and the specific types of the first node and the second node are not limited.

[0063] The following section describes the relevant technologies involved in the embodiments of this application:

[0064] • Integrated communication and sensing:

[0065] Next-generation networks (such as 6G networks) are expected to be a fusion of mobile communication networks, sensing networks, and computing networks. In a narrow sense, a sensing network refers to a system with capabilities such as target localization (ranging, velocity, angle measurement), target imaging, target detection, target tracking, and target recognition. In a broad sense, a sensing network refers to a system that possesses the attributes and states of all services, networks, users, terminals, and environmental objects. From the perspective of sensing applications, sensing can be categorized as follows:

[0066] • Outdoor / Wide Area / Local Area Applications: including smart cities (e.g., weather monitoring), smart transportation / high-speed rail (e.g., high-precision map building, road monitoring, intrusion detection), low-altitude applications (e.g., drone monitoring and obstacle avoidance, flight intrusion detection, flight path management), etc.

[0067] • Indoor / Local Area Applications: Including smart home and health management (e.g., respiratory monitoring, intrusion detection, gesture / posture recognition, motion monitoring, movement trajectory tracking, etc.), smart factories (e.g., intrusion detection, material detection, object defect detection, etc.).

[0068] The above are just examples to provide some classifications of sensing applications; the scope of sensing applications is not limited to the examples above.

[0069] Wireless communication and sensing are two major applications of modern radio frequency (RF) technology. Sensing utilizes radio waves to detect parameters of the physical environment to achieve environmental perception such as target localization, action recognition, and target imaging. Traditionally, sensing and wireless communication exist independently, and this separate design leads to a waste of wireless spectrum and hardware resources. With the advent of B5G and 6G, communication spectrum is moving towards millimeter waves, terahertz, and visible light communication; in the future, the spectrum of wireless communication will overlap with the spectrum of traditional sensing. Integrated communication and sensing technology merges these two functions. It can utilize the wireless resources of wireless communication to achieve sensing capabilities; it can leverage widely deployed cellular networks to achieve sensing services over a wider area; it can utilize base stations and multiple terminals for joint sensing to achieve higher sensing accuracy; and it can reuse wireless communication hardware modules to achieve sensing functions, reducing costs. In short, integrated communication and sensing technology enables future wireless communication systems to possess sensing capabilities, providing a foundation for the development of future smart transportation, smart cities, smart factories, drones, and other related businesses.

[0070] Figure 2 shows a schematic diagram of the sensing modes provided by the related technologies, including the following eight sensing modes: (1) Base station self-sending and receiving sensing: The base station sends a sensing signal, and the sensing target sends a reflected signal after receiving the sensing signal; (2) Terminal self-sending and receiving sensing: The terminal sends a sensing signal, and the sensing target sends a reflected signal after receiving the sensing signal; (3) Base station cooperative sensing: Base station A sends a sensing signal, and the sensing target sends a reflected signal to base station B after receiving the sensing signal; (4) Terminal cooperative sensing: Terminal A sends a sensing signal, and the sensing target sends a reflected signal to terminal B after receiving the sensing signal; (5) Base station-terminal cooperative sensing: The base station sends a sensing signal, and the sensing target sends a reflected signal to the terminal after receiving the sensing signal; (6) Terminal-base station cooperative sensing: The terminal sends a sensing signal, and the sensing target sends a reflected signal to the base station after receiving the sensing signal; (7) The sensed target is the sensing signal sending node: The terminal sends a sensing signal, and the base station is the sensed target; (8) The sensed target is the sensing signal receiving node: The base station sends a sensing signal, and the terminal sends a feedback after receiving the sensing signal, and the terminal is the sensed target.

[0071] Sensing signal transmitting nodes and sensing signal receiving nodes can be collectively referred to as sensing nodes. In the eight sensing modes mentioned above, there are only single or pairs of sensing nodes. However, in wireless communication systems, the number of terminals (mobile phones, IoT devices, etc.) is large. When multiple sensing nodes (i.e., base stations, mobile phones, IoT devices, etc. that transmit and / or receive sensing signals) exist around a sensed terminal, the joint participation of multiple sensing nodes can improve the accuracy of sensing and meet more complex sensing service requirements, providing richer sensing services. When multiple sensing nodes exist in the system, a sensing control node may exist to control and manage the entire sensing service to improve efficiency. This sensing control node can be a base station, a terminal, or a core network element.

[0072] Figure 3 illustrates a schematic diagram of multiple sensing nodes participating in sensing, provided by related technologies. Taking the vehicle-mounted device as the sensing terminal 310 and the terminal or base station as the sensing control node 320 as an example, the sensing control node 320 can send communication signals to sensing node 1 and the sensing terminal 310. Sensing node 1, sensing node 2, and sensing node 3 can send sensing signals to the sensing terminal 310, thereby enabling multiple sensing nodes to participate in sensing together, improving the accuracy of sensing, meeting more complex sensing service needs, and providing richer sensing services.

[0073] Micro-Doppler applications:

[0074] The micro-Doppler effect is a concept in signal processing that describes the minute changes in the echo signal caused by micro-motions (such as vibration, rotation, or tumbling) of a target or its structural components. The micro-Doppler effect reflects the fine features and attitude of a target and can be used in target detection, target imaging, and target recognition.

[0075] Related technologies provide applications for micro-motion sensing of targets using millimeter waves. Figure 4 shows a schematic diagram of the micro-motion sensing applications provided by related technologies. Through the WLAN sensing function provided by the sensing device, specific applications include at least one of the following: footprint tracking, activity recognition, gesture control, imaging, vital signs monitoring, and fall detection.

[0076] Among these, footprint tracking monitors and records an individual's walking path and footprints, which can be used in fields such as security monitoring, crowd analysis, and behavioral research; activity recognition identifies and classifies an individual's ongoing activities, such as walking, running, and standing, which can be used in fields such as smart homes, health monitoring, and elderly care; gesture control controls devices or interfaces by recognizing the target's gestures, typically involving cameras, sensors, or other motion capture technologies, to enhance the human-computer interaction experience; imaging uses sensor technology to create images of objects or scenes, which can be used in fields such as security monitoring, medical diagnosis, and environmental monitoring; vital sign monitoring monitors an individual's vital signs, such as heart rate, respiratory rate, body temperature, and blood pressure, which can be used in fields such as health monitoring, disease prevention, and emergency medical response; and fall detection detects whether an individual has fallen and can automatically alarm or notify emergency contacts when a fall is detected, which can be used in fields such as security monitoring and elderly care.

[0077] Wireless communication and sensing are two major applications of modern radio frequency technology. Sensing utilizes radio waves to detect environmental parameters to achieve functions such as target localization, action recognition, and target imaging. Sensing receiving nodes feed back complete information to sensing management nodes, enabling these nodes to perform tasks such as analyzing the sensed target and determining its direction of movement. However, in the sensing field, feeding back complete information to the sensed target consumes significant amounts of wireless spectrum resources, impacting transmission efficiency.

[0078] To address the aforementioned issues, Figure 5 illustrates a flowchart of a method for transmitting perception results provided in an exemplary embodiment of this application. This method is executed by a first node and includes:

[0079] Step 510: Based on the model information of one or more first models, send the perception results; wherein, the first model is a mathematical model used to characterize the echo features of the first echo.

[0080] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0081] In some embodiments, the first echo is a preset echo, a predefined echo, or a preconfigured echo. Each first echo is related to the sensing scenario. For example, a first echo is set for a breathing monitoring sensing scenario; a first echo is set for a drone sensing scenario; and a first echo is set for a gesture sensing scenario. The first echo can be considered as a typical echo, echo template, common echo, etc., under a certain sensing scenario. Since each sensing scenario has its own characteristics, each sensing scenario has at least one typical first echo. This first echo can be determined in advance through experiments, statistics, etc.

[0082] In some embodiments, each first model is used to characterize a first echo, and different first models correspond to different first echoes. The first model is a preset model, a predefined model, or a pre-configured model.

[0083] In some embodiments, model information includes model features used to describe a first model or a first echo.

[0084] In some embodiments, model features are indicated by at least one of the following: model pattern; model parameters; model index.

[0085] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0086] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0087] The time-frequency diagram of the first echo is used to show the changes of the first echo in time and frequency; the time-delay diagram of the first echo is used to show the time delay of the first echo arriving at the receiving end (first node) as time changes; the displacement diagram of the first echo is used to show the position of the sensing target relative to a certain reference point as time changes; the amplitude diagram of the first echo is used to show the amplitude of the first echo as time changes; and the phase diagram of the first echo is used to show the amplitude of the first echo as time changes.

[0088] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0089] The pattern type of the first echo is used to indicate the waveform characteristics of the first echo, such as circular wave, sine wave, curved wave, straight wave, etc.; the repetition frequency of the first echo is used to indicate the number of first echoes sent; the first echo may be displayed by a single pattern or by a combination of multiple patterns; the pattern combination coefficient of the first echo is used to represent the coefficients corresponding to each waveform pattern when the first echo is described by a combination of multiple waveform patterns.

[0090] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes.

[0091] Define the first model number in the communication protocol; or, when the first node interacts with other nodes, agree on or negotiate the first model number.

[0092] The indication methods for model features may also include other methods, and model patterns, model parameters, and model indexes may also include other types, which are not limited in the embodiments of this application.

[0093] In some embodiments, the model information further includes an error range, which indicates the permissible range of differences between the model features of the second model and the model features of the first model, wherein the second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0094] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean-square error (MSE); percentage of error; standard deviation; root mean square error; mean absolute error.

[0095] For example, the error range can be set to mean square error less than 4, or the error range can be set to error percentage less than 10%.

[0096] In some embodiments, the sensing result is sent when the measurement error between the first model and the second model is within the error range; wherein the second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process.

[0097] The second echo is the wave reflected back by the first node during the sensing process. For example, the first node sends a transmitted wave, which, upon contact with the sensing target, is reflected back by the target as a second echo. The second model is a mathematical model used to characterize the echo features of the second echo; in this application's embodiments, it is typically illustrated using a function as an example.

[0098] If the measurement error between the first model and the second model is outside the error range, no sensing results are sent, because this indicates that the difference between the first model and the second model is large and they do not match, so there is no need to send sensing results.

[0099] In some embodiments, the method further includes: receiving model information of one or more first models.

[0100] After receiving model information from one or more first models, the first node sends the perception results based on the model information, without needing to send complete information, thus reducing the amount of information sent.

[0101] In some embodiments, the perception result includes at least one of the following:

[0102] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0103] For example, the perception results include: model matching results, a first index, and measurement error. Reporting the measurement error to the second node allows for the representation of the second model without needing to report complete information, reducing wireless spectrum resource overhead and improving information transmission efficiency.

[0104] The perception results can also include: model matching results, first index, and error matching results. In this case, the model information includes model features and error range. By reporting whether the measurement error between the second and first models is within the error range, it is not necessary to report the measurement error, further reducing the total amount of information that needs to be sent.

[0105] The perception results may also include parameters from the second model. By reporting the parameters of the second model, perception accuracy can be improved, and the perception target can be more accurately indicated.

[0106] The embodiments of this application do not limit the combination of perception results. Different perception results correspond to different perception scenarios, which can flexibly adapt to a variety of perception scenarios, improve the versatility of this method, and meet the actual needs in different scenarios.

[0107] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0108] Scenario 1: The second node sends a first model, and the first node responds with whether the second model matches the first model;

[0109] Figure 6 shows a flowchart of a method for transmitting perception results provided in an exemplary embodiment of this application, which is executed by a first node 610 and a second node 620.

[0110] In some embodiments, the second node 620 sends a first model to the first node 610.

[0111] If the first node 610 stores multiple first models, the second node 620 indicates a specific first model by sending the index corresponding to the first model; if the first node 610 does not store a first model, the second node 620 indicates a specific first model by sending the complete first model or the model features of the first model, such as sending the complete function.

[0112] In some embodiments, the first node 610 measures the second echo to obtain the second model.

[0113] The second echo is the wave reflected back by the first node 610 during the sensing process, and the second model is used to characterize the echo features of the second echo.

[0114] In some embodiments, the first node 610 sends the sensing results to the second node 620.

[0115] For example, the perception results include measurement error, namely the mean square error between the first model and the second model reported by the first node 610, which determines whether the second model matches the first model.

[0116] For example, in the field of respiratory monitoring, it is necessary to detect the presence of a breathing target. For instance, both the first and second models are sinusoidal functions; if the second model matches the first model, it indicates the presence of a breathing target. The first model is represented as: phase = m h sinω h t;

[0117] Where, m h The ω represents the maximum value of the phase amplitude, sin represents the phase change as a sine function graph, and ω represents the maximum value of the phase amplitude. h This represents the rate of change of the angle of phase change.

[0118] The second model corresponding to the second echo measured by the first node during the sensing process is represented as: phase′=m h ′sinω h 't;

[0119] Where, m h ' represents the maximum value of the phase amplitude, ω h ′ represents the rate of change of the phase angle.

[0120] Figure 7 shows a waveform diagram of the first and second models provided in an exemplary embodiment of this application.

[0121] In Figure 7, the solid line represents the waveform of the first model, and the dashed line represents the waveform of the second model. The maximum phase amplitude of the first model is m(m h ), with a period of 2π / w(2π / ω) hThe maximum phase amplitude of the second model is m'(m h ′), with a period of 2π / w'(2π / ω). h ′).

[0122] In some embodiments, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0123] The second model and the first model are compared to obtain the measurement error between them. The comparison methods include the following two:

[0124] Method 1: Calculate the mean squared error between the second model and the first model;

[0125] The mean square error (MSE) between phase and phase′:

[0126] Where k refers to the index of the first sampling point of phase′ that minimizes MSE. M is the number of sampling points.

[0127] Method 2: Calculate the mean square error of the same parameters in the second model and the first model;

[0128] For example, calculate the mean square error of the phase amplitude (MSE-a) in the second model and the first model, or calculate the mean square error of the rate of change of the angle (MSE-p) in the second model and the first model.

[0129] Where k refers to the m that minimizes MSE-a. h The index of the first sampling point of ', or the ω that minimizes MSE-p. h The index of the first sampling point is ', where I is the number of sampling points and i is the sequence number of the sampling point.

[0130] In some embodiments, when there are multiple parameters, the measurement error includes the mean square error corresponding to one parameter or the mean square error corresponding to multiple parameters.

[0131] In some embodiments, model information includes model features, and perception results include at least one of the following: model matching results, a first index, and measurement error.

[0132] For example, the sensing results include measurement errors, and the first node reports the mean square error between the first and second models. Reporting the measurement errors to the second node allows the second model to be represented without reporting complete information, reducing wireless spectrum resource overhead, improving information transmission efficiency, and meeting the needs of scenarios requiring low transmission latency.

[0133] In some embodiments, the model information includes model features and error range, and the perception result includes at least one of the following: model matching result, first index, measurement error, error matching result, and third index.

[0134] For example, the perception results include error matching results, and the first node reports whether the measurement error is within the error range. At this time, the model information includes model features and error range. By reporting whether the measurement error between the second model and the first model is within the error range, it is not necessary to report the measurement error, further reducing the total amount of information that needs to be sent, thus meeting the needs of scenarios where low transmission latency is the priority.

[0135] In some embodiments, where the perception result does not include the parameters of the second model, the perception result includes the perception information of the perceived target.

[0136] For example, the first node reports information such as the velocity of the perceived target, the distance between the first node and the perceived target, and the displacement of the perceived target. By reporting the perceived information of the perceived target, and not being limited to the parameters of the second model, a more comprehensive understanding of the perceived target can be achieved, meeting the needs of scenarios requiring a large amount of perceived information about the perceived target.

[0137] Scenario 2: The second node sends a first model, and the first node returns the parameters of the second model;

[0138] Figure 8 shows a flowchart of a method for transmitting perception results provided in an exemplary embodiment of this application, which is executed by a first node 610 and a second node 620.

[0139] In some embodiments, the second node 620 sends a first model to the first node 610.

[0140] In some embodiments, the first node 610 measures the second echo to obtain the second model.

[0141] For specific implementation details, please refer to the embodiment shown in Figure 6, which will not be repeated here.

[0142] In some embodiments, the first node 610 sends the sensing results to the second node 620.

[0143] For example, the perception result includes the parameters of the second model, that is, the parameters of the second model reported by the first node 610, which are then combined by the second node 620 with the parameters of the second model to obtain the second model. By reporting the parameters of the second model, the perception accuracy is improved, and the perception target is indicated more precisely.

[0144] Referring to the relevant content in Scenario 1, the first model is represented as: phase = m h sinω h t;

[0145] The second model is represented as: phase′=m h ′sinω h 't;

[0146] The perception results include parameters of the second model, such as m. h ′ and ω h ′.

[0147] In some embodiments, the perception result includes the parameter name and corresponding parameter value of the second model's parameters; or, the parameter name of the second model's parameters and the difference between the second model's parameters and the first model's parameters.

[0148] For example, the perception results include m h ′ and its corresponding value, and ω′ h and the corresponding value; or, the perception result includes m h ′ and m h ′ and m h The difference between them, and ω′ h and ω h ′ and ω h The difference between them.

[0149] In some embodiments, the perception result includes the parameter values ​​of the parameters of the second model; or, the difference between the parameters of the second model and the parameters of the first model.

[0150] The first and second nodes pre-agree on the parameter order, so the sensing result does not need to send the parameter names, but only the parameter values ​​or differences in sequence, reducing the amount of information that needs to be sent. For example, the pre-agreement order is to send m first. h ′, then send ω h ′, then the perception results include m h The value of ω′, and ω′ h The value; or, the perceived result includes m h ′ and m h The difference between them, and ω h ′ and ω h The difference between them.

[0151] Scenario 3: The second node sends multiple first models, and the first node returns the model index;

[0152] Figure 9 shows a flowchart of a method for transmitting perception results provided in an exemplary embodiment of this application, which is executed by a first node 610 and a second node 620.

[0153] In some embodiments, the second node 620 sends multiple first models to the first node 610.

[0154] In some fields, such as the field of drones, the first model may be of more than one type. The second node 620 sends multiple first models to the first node 610, so that subsequent first nodes can provide feedback on the model index corresponding to the first model that is close to the second model.

[0155] For example, in the field of drones, the first model is represented as: φ(t)=θ max sin(2πf bird t)=θ max sin(ω0t);

[0156] Where α is the azimuth angle. φ(t) is the pitch angle, φ(t) is the yaw angle, and θ is the pitch angle. max It is the maximum swing angle, f bird ω0 is the oscillation frequency, and ω0 is the oscillation angular frequency.

[0157] The second first model is represented as:

[0158] Where L1 is the distance between the blade root and the center of rotation of the UAV, L2 is the distance between the blade tip and the center of rotation of the UAV, and β is the elevation angle of the rotor relative to the radar line-of-sight (LOS). It is the phase of the k-th blade of the d-th rotor, where ω represents the initial rotation angle. d It is the rotational angular frequency of the d-th rotor.

[0159] In some embodiments, the first node 610 measures the second echo to obtain the second model.

[0160] For specific implementation details, please refer to the embodiment shown in Figure 6, which will not be repeated here.

[0161] The second model obtained by measuring the second echo at the first node is represented as: phase′=m h ′sinω h 't;

[0162] The second model is compared with the first first model to obtain the first mean squared error (MSE1). The second model is compared with the second first model to obtain the second mean squared error (MSE2). The calculation method of the mean squared error (MSE) is the same as that in Scenario 1, and will not be repeated here. By comparing MSE1 and MSE2, the second index, i.e., the first model with the smallest measurement error, can be obtained.

[0163] In some embodiments, the first node 610 sends the perception result to the second node 620, and the perception result includes the model index.

[0164] In some embodiments, the model information includes model features, and the perception result includes at least one of the following: model matching result, first index, measurement error, and second index.

[0165] For example, the perception result includes a second index, which is the first model reported by the first node with the smallest mean square error. By reporting the second index, the second node can obtain the first model that is closest to the second model, thereby improving the accuracy of perception. In scenarios where the model information does not include the error range, reporting the first model with the smallest mean square error among multiple first models is simple to implement.

[0166] In some embodiments, the model information includes model features and error range, and the perception result includes at least one of the following: model matching result, first index, measurement error, second index, error matching result, and third index.

[0167] For example, the perception result includes a third index, which is the first model reported by the first node whose mean squared error is within the error range. This can be a first model with the smallest mean squared error, or multiple first models whose mean squared errors are within the error range. By reporting the third index, in scenarios with specific requirements for mean squared error, the lower limit of the first model can be guaranteed, and the problem of reporting a first model whose mean squared error is outside the error range can be avoided.

[0168] In summary, the method provided in this embodiment transmits sensing results based on model information from one or more first models. The first model is a mathematical model used to characterize the echo features of the first echo. The first echo is a typical echo in the sensing scenario; therefore, it is possible to transmit sensing results without transmitting complete information, but rather by referencing the model information of the first model. This reduces the total amount of information that needs to be transmitted, lowers wireless spectrum resource overhead, and improves information transmission efficiency.

[0169] In the method provided in this embodiment, different perception results correspond to different perception scenarios, which can flexibly adapt to a variety of perception scenarios, improve the versatility of the method, and meet the actual needs in different scenarios.

[0170] Figure 10 illustrates a flowchart of a model information transmission method provided in an exemplary embodiment of this application. The method is executed by a first node and includes:

[0171] Step 1010: Receive model information of one or more first models; wherein, the model information is used by the first node to send the sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo.

[0172] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0173] In some embodiments, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0174] In some embodiments, model information includes model features, which are used to describe the first model.

[0175] In some embodiments, model features are indicated by at least one of the following methods: model pattern; model parameters; model index;

[0176] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0177] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0178] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0179] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes.

[0180] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0181] In some embodiments, the model information further includes an error range, which indicates the permissible range of differences between the model features of the second model and the model features of the first model, wherein the second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0182] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0183] In some embodiments, the method further includes: sending perception results based on model information of one or more first models.

[0184] After receiving model information from one or more first models, the first node sends the perception results based on the model information, without needing to send complete information, thus reducing the amount of information sent.

[0185] In some embodiments, if the measurement error between the first model and the second model is within the error range, the sensing result is sent.

[0186] The second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process.

[0187] In some embodiments, the perception result includes at least one of the following:

[0188] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0189] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0190] Scenario 1: The second node sends a first model, and the first node responds with whether the second model matches the first model;

[0191] In some embodiments, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0192] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0193] Scenario 2: The second node sends a first model, and the first node returns the parameters of the second model;

[0194] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0195] Scenario 3: The second node sends multiple first models, and the first node returns the model index;

[0196] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0197] In summary, the method provided in this embodiment receives model information of one or more first models. The model information is used by the first node to send sensing results. The first model is a mathematical model used to characterize the echo characteristics of the first echo. The first echo is a typical echo in the sensing scenario. Therefore, when sending sensing results in the future, complete information does not need to be sent. Instead, the sensing results are sent with reference to the model information of the first model, which reduces the total amount of information that needs to be sent, reduces the wireless spectrum resource overhead, and improves information transmission efficiency.

[0198] In the method provided in this embodiment, different perception results correspond to different perception scenarios, which can flexibly adapt to a variety of perception scenarios, improve the versatility of the method, and meet the actual needs in different scenarios.

[0199] Figure 11 shows a flowchart of a method for transmitting perception results provided in an exemplary embodiment of this application. The method is executed by a second node and includes:

[0200] Step 1110: Receive the sensing result; wherein the sensing result is sent by the first node based on model information of one or more first models, the first model being a mathematical model used to characterize the echo features of the first echo.

[0201] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0202] In some embodiments, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0203] In some embodiments, model information includes model features, which are used to describe the first model.

[0204] In some embodiments, model features are indicated by at least one of the following methods: model pattern; model parameters; model index;

[0205] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0206] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0207] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0208] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes.

[0209] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0210] In some embodiments, the model information further includes an error range, which indicates the permissible range of differences between the model features of the second model and the model features of the first model, wherein the second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0211] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0212] In some embodiments, the method further includes: sending model information of one or more first models.

[0213] In some embodiments, the perception result includes at least one of the following:

[0214] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0215] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0216] Scenario 1: The second node sends a first model, and the first node responds with whether the second model matches the first model;

[0217] In some embodiments, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0218] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0219] Scenario 2: The second node sends a first model, and the first node returns the parameters of the second model;

[0220] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0221] Scenario 3: The second node sends multiple first models, and the first node returns the model index;

[0222] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0223] In summary, the method provided in this embodiment receives sensing results; wherein the sensing results are sent by a first node based on model information of one or more first models. The first model is a mathematical model used to characterize the echo characteristics of the first echo. The first echo is a typical echo in the sensing scenario. Therefore, the sensing results may not be complete information, reducing the total amount of information to be received, reducing wireless spectrum resource overhead, and improving information transmission efficiency.

[0224] In the method provided in this embodiment, different perception results correspond to different perception scenarios, which can flexibly adapt to a variety of perception scenarios, improve the versatility of the method, and meet the actual needs in different scenarios.

[0225] Figure 12 shows a flowchart of a model information transmission method provided in an exemplary embodiment of this application. The method is executed by a second node and includes:

[0226] Step 1210: Send model information of one or more first models; wherein, the model information is used by the first node to send the perception results, and the first model is a mathematical model used to characterize the echo features of the first echo.

[0227] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0228] In some embodiments, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0229] In some embodiments, model information includes model features, which are used to describe the first model.

[0230] In some embodiments, model features are indicated by at least one of the following methods: model pattern; model parameters; model index;

[0231] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0232] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0233] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0234] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes.

[0235] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0236] In some embodiments, the model information further includes an error range, which indicates the permissible range of differences between the model features of the second model and the model features of the first model, wherein the second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0237] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0238] In some embodiments, the method further includes receiving a sensing result.

[0239] In some embodiments, the perception result includes at least one of the following:

[0240] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0241] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0242] Scenario 1: The second node sends a first model, and the first node responds with whether the second model matches the first model;

[0243] In some embodiments, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0244] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0245] Scenario 2: The second node sends a first model, and the first node returns the parameters of the second model;

[0246] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0247] Scenario 3: The second node sends multiple first models, and the first node returns the model index;

[0248] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0249] In summary, the method provided in this embodiment receives sensing results; wherein the sensing results are sent by a first node based on model information of one or more first models, the first model being a mathematical model used to characterize the echo characteristics of a first echo, and the first echo being a typical echo in the sensing scenario. Therefore, the sensing results may not be complete information, reducing wireless spectrum resource overhead and improving information transmission efficiency.

[0250] In the method provided in this embodiment, different perception results correspond to different perception scenarios, which can flexibly adapt to a variety of perception scenarios, improve the versatility of the method, and meet the actual needs in different scenarios.

[0251] In the above embodiments, the embodiments corresponding to FIG5, FIG10, FIG11 and FIG12 can be implemented individually or in combination. For example, the embodiments corresponding to FIG5 and FIG11 can be implemented in combination, and the embodiments corresponding to FIG10 and FIG12 can be implemented in combination. This application does not limit this.

[0252] Figure 13 shows a block diagram of a first device provided in an exemplary embodiment of this application. The device can be implemented as a first node, or as part of a first node, by software or hardware or a combination of both. The device includes:

[0253] The transmitting module 1310 is used to transmit the sensing results based on model information of one or more first models; wherein the first model is a mathematical model used to characterize the echo features of the first echo.

[0254] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0255] In one possible design of this embodiment, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0256] In one possible design of this embodiment, the model information includes model features, which are used to describe the first model.

[0257] In one possible design of this embodiment, model features are indicated by at least one of the following methods: model drawing; model parameters; model index;

[0258] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0259] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0260] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0261] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first device and other nodes.

[0262] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0263] In one possible design of this embodiment, the model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0264] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0265] In one possible design of this embodiment, the sensing result is sent when the measurement error between the first model and the second model is within the error range; wherein, the second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process.

[0266] In one possible design of this embodiment, the receiving module 1320 is used to receive model information of one or more first models.

[0267] In one possible design of this embodiment, the perception result includes at least one of the following:

[0268] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0269] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0270] Scenario 1: The second device sends a first model, and the first device responds to whether the second model matches the first model;

[0271] In one possible design of this embodiment, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0272] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0273] Scenario 2: The second device sends a first model, and the first device returns the parameters of the second model;

[0274] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0275] Scenario 3: The second device sends multiple first models, and the first device returns the model index;

[0276] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0277] This embodiment uses one transmitting module 1310 and one receiving module 1320 as an example for illustration, and the number of transmitting modules 1310 and receiving modules 1320 is not limited.

[0278] For a description of the function of the sending module 1310, please refer to step 510 in the embodiment shown in Figure 5. For a description of the function of the receiving module 1320, please refer to step 510 in the embodiment shown in Figure 5.

[0279] Figure 14 shows a block diagram of a second device provided in an exemplary embodiment of this application. This device can be implemented as a second node, or as part of a second node, by software or hardware, or a combination of both. The device includes:

[0280] The receiving module 1410 is used to receive the sensing result; wherein the sensing result is sent by the first device based on model information of one or more first models, the first model being a mathematical model used to characterize the echo characteristics of the first echo.

[0281] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0282] In one possible design of this embodiment, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0283] In one possible design of this embodiment, the model information includes model features, which are used to describe the first model.

[0284] In one possible design of this embodiment, model features are indicated by at least one of the following methods: model drawing; model parameters; model index;

[0285] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0286] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0287] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0288] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first device and other nodes.

[0289] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0290] In one possible design of this embodiment, the model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0291] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0292] In one possible design of this embodiment, the method further includes: sending model information of one or more first models.

[0293] In one possible design of this embodiment, the perception result includes at least one of the following:

[0294] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0295] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0296] Scenario 1: The second device sends a first model, and the first device responds to whether the second model matches the first model;

[0297] In one possible design of this embodiment, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0298] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0299] Scenario 2: The second device sends a first model, and the first device returns the parameters of the second model;

[0300] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0301] Scenario 3: The second device sends multiple first models, and the first device returns the model index;

[0302] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0303] This embodiment uses one receiving module 1410 and one transmitting module 1420 as an example for illustration, and the number of receiving modules 1410 and transmitting modules 1420 is not limited.

[0304] For a description of the function of the receiving module 1410, please refer to step 1110 in the embodiment shown in Figure 11. For a description of the function of the sending module 1420, please refer to step 1110 in the embodiment shown in Figure 11.

[0305] Figure 15 shows a block diagram of a first device provided in an exemplary embodiment of this application. The device can be implemented as a first node, or as part of a first node, by software or hardware, or a combination of both. The device includes:

[0306] The receiving module 1510 is used to receive model information of one or more first models; wherein the model information is used by the first device to send sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo.

[0307] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0308] In one possible design of this embodiment, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0309] In one possible design of this embodiment, the model information includes model features, which are used to describe the first model.

[0310] In one possible design of this embodiment, model features are indicated by at least one of the following methods: model drawing; model parameters; model index;

[0311] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0312] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0313] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0314] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first device and other nodes.

[0315] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0316] In one possible design of this embodiment, the model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0317] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0318] In one possible design of this embodiment, the method further includes: sending perception results based on model information of one or more first models.

[0319] In one possible design of this embodiment, the sensing result is sent when the measurement error between the first model and the second model is within the error range;

[0320] The second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process.

[0321] In one possible design of this embodiment, the perception result includes at least one of the following:

[0322] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0323] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0324] Scenario 1: The second device sends a first model, and the first device responds to whether the second model matches the first model;

[0325] In one possible design of this embodiment, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0326] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0327] Scenario 2: The second device sends a first model, and the first device returns the parameters of the second model;

[0328] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0329] Scenario 3: The second device sends multiple first models, and the first device returns the model index;

[0330] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0331] This embodiment uses one receiving module 1510 and one transmitting module 1520 as an example for illustration, and the number of receiving modules 1510 and transmitting modules 1520 is not limited.

[0332] For a description of the function of the receiving module 1510, please refer to step 1010 in the embodiment shown in Figure 10. For a description of the function of the sending module 1520, please refer to step 1010 in the embodiment shown in Figure 10.

[0333] Figure 16 shows a block diagram of a second device provided in an exemplary embodiment of this application. This device can be implemented as a second node, or as part of a second node, by software or hardware, or a combination of both. The device includes:

[0334] The transmitting module 1610 is used to transmit model information of one or more first models; wherein the model information is used by the first device to transmit sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo.

[0335] The mathematical model can be represented in various forms, including graphs, functions, statistical properties, parameter values, etc. This application does not limit these forms, but usually uses functions as an example for explanation.

[0336] In one possible design of this embodiment, the first echo is a preset echo, a predefined echo, or a preconfigured echo.

[0337] In one possible design of this embodiment, the model information includes model features, which are used to describe the first model.

[0338] In one possible design of this embodiment, model features are indicated by at least one of the following methods: model drawing; model parameters; model index;

[0339] Among them, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo.

[0340] (1) The model diagram includes at least one of the following: the time-frequency diagram of the first echo; the time-delay diagram of the first echo; the displacement diagram of the first echo; the amplitude diagram of the first echo; and the phase diagram of the first echo.

[0341] (2) The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo.

[0342] (3) The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first device and other nodes.

[0343] For specific implementation details, please refer to (1) to (3) of the embodiment in Figure 5, which will not be repeated here.

[0344] In one possible design of this embodiment, the model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process.

[0345] By way of example and not limitation, the error range may be expressed in at least one of the following ways: mean squared error (MSE); error percentage.

[0346] In one possible design of this embodiment, the method further includes: receiving the sensing result.

[0347] In one possible design of this embodiment, the perception result includes at least one of the following:

[0348] The model matching result indicates whether the measured second echo matches the first model during the sensing process; the first index indicates the first model that matches the echo characteristics of the second echo; the measurement error indicates the measurement difference between the first model and the second model; the second index indicates the first model with the smallest measurement error; the error matching result indicates whether the measurement error is within the error range; the third index indicates the first model whose measurement error is within the error range; the sensing information of the sensing target; the parameters of the second model; wherein, the second model is a mathematical model used to characterize the echo characteristics of the second echo.

[0349] The above perception results correspond to different implementation scenarios, such as the following three implementation scenarios.

[0350] Scenario 1: The second device sends a first model, and the first device responds to whether the second model matches the first model;

[0351] In one possible design of this embodiment, the measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model.

[0352] For specific implementation details, please refer to Scenario 1 of the embodiment shown in Figure 5, which will not be repeated here.

[0353] Scenario 2: The second device sends a first model, and the first device returns the parameters of the second model;

[0354] For specific implementation details, please refer to Scenario 2 of the embodiment in Figure 5, which will not be repeated here.

[0355] Scenario 3: The second device sends multiple first models, and the first device returns the model index;

[0356] For specific implementation details, please refer to scenario 3 of the embodiment in Figure 5, which will not be repeated here.

[0357] This embodiment uses one transmitting module 1610 and one receiving module 1620 as an example for illustration, and the number of transmitting modules 1610 and receiving modules 1620 is not limited.

[0358] For a description of the function of the sending module 1610, please refer to step 1210 in the embodiment shown in Figure 12. For a description of the function of the receiving module 1620, please refer to step 1210 in the embodiment shown in Figure 12.

[0359] Figure 17 shows a schematic diagram of the structure of a first node provided in an exemplary embodiment of this application. The first node 1700 can be used to execute the method steps performed by the first node in the above embodiments. The first node 1700 may include: a processor 1701, a transceiver 1702, and a memory 1703. The processor 1701 can be used to control transmission and / or reception. The transceiver 1702 can be used to implement transmission and / or reception functions, such as implementing the functions of at least one of the transmission module 1310, reception module 1320, reception module 1510, and transmission module 1520 described above.

[0360] The processor 1701 includes one or more processing cores, and the processor 1701 executes various functional applications and information processing by running software programs and modules.

[0361] Transceiver 1702 may include a receiver and a transmitter, for example, the receiver and transmitter may be implemented as the same wireless communication component, which may include a wireless communication chip and a radio frequency antenna.

[0362] The memory 1703 can be connected to the processor 1701 and the transceiver 1702.

[0363] The memory 1703 can be used to store a computer program executed by the processor, and the processor 1701 is used to execute the computer program to implement the various steps in the above method embodiments.

[0364] Furthermore, the memory 1703 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to: magnetic disks or optical disks, electrically erasable programmable read-only memory, erasable programmable read-only memory, static on-demand memory, read-only memory, magnetic memory, flash memory, and programmable read-only memory.

[0365] For details not described in this embodiment, please refer to the method-side embodiment above, which will not be repeated here.

[0366] Figure 18 shows a schematic diagram of the structure of a second node provided in an exemplary embodiment of this application. The second node 1800 can be used to execute the method steps performed by the second node in the above embodiments. The second node 1800 may include a processor 1801, a transceiver 1802, and a memory 1803. The processor 1801 can be used to control transmission and / or reception. The transceiver 1802 can be used to implement transmission and / or reception functions, such as implementing the functions of at least one of the receiving module 1410, transmitting module 1420, transmitting module 1610, and receiving module 1620 described above.

[0367] The processor 1801 includes one or more processing cores, and the processor 1801 executes various functional applications and information processing by running software programs and modules.

[0368] Transceiver 1802 may include a receiver and a transmitter. For example, transceiver 1802 may include a wired communication component, which may include a wired communication chip and a wired interface (such as a fiber optic interface). Transceiver 1802 may also include a wireless communication component, which may include a wireless communication chip and a radio frequency antenna.

[0369] The memory 1803 can be connected to the processor 1801 and the transceiver 1802.

[0370] The memory 1803 can be used to store a computer program executed by the processor, and the processor 1801 is used to execute the computer program to implement the various steps in the above method embodiments.

[0371] Furthermore, the memory 1803 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to: magnetic disks or optical disks, electrically erasable programmable read-only memory, erasable programmable read-only memory, static on-demand memory, read-only memory, magnetic memory, flash memory, and programmable read-only memory.

[0372] For details not described in this embodiment, please refer to the method-side embodiment above, which will not be repeated here.

[0373] This application also provides a computer-readable storage medium storing a computer program for execution by a processor to implement the method for transmitting perception results and / or model information at the first node, or the method for transmitting perception results and / or model information at the second node. In some embodiments, the computer-readable storage medium may include ROM (Read-Only Memory), RAM (Random-Access Memory), SSD (Solid State Drives), or optical disc, etc. The random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).

[0374] This application also provides a chip, which includes programmable logic circuits and / or program instructions. When the chip is running, it is used to implement the above-mentioned method for transmitting the perception results and / or the method for transmitting model information on the first node side, or the method for transmitting the perception results and / or the method for transmitting model information on the second node side.

[0375] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. A processor reads and executes the computer program from the computer-readable storage medium to implement the above-mentioned method for transmitting perception results and / or method for transmitting model information on the first node side, or the method for transmitting perception results and / or method for transmitting model information on the second node side.

[0376] It should be understood that the term "instruction" mentioned in the embodiments of this application can be a direct instruction, an indirect instruction, or an indication of a relationship. For example, A instructing B can mean that A directly instructs B, such as B being able to obtain information through A; it can also mean that A indirectly instructs B, such as A instructing C, so B can obtain information through C; or it can mean that there is a relationship between A and B.

[0377] In the description of the embodiments of this application, the term "correspondence" may indicate that there is a direct or indirect correspondence between two things, or that there is an association between two things, or that there is a relationship of instruction and being instructed, configuration and being configured, etc.

[0378] In some embodiments of this application, "predefined" can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including network devices and network equipment). This application does not limit the specific implementation method. For example, predefined can refer to what is defined in the protocol.

[0379] In some embodiments of this application, "protocol" may refer to standard protocols in the field of communications, such as LTE protocol, NR protocol and related protocols applied to future communication systems, and this application does not limit it.

[0380] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0381] In this article, "greater than or equal to" can mean greater than or equal to, and "less than or equal to" can mean less than or equal to.

[0382] Furthermore, the step numbers described herein are merely illustrative of one possible execution order between steps. In some other embodiments, the steps may not be executed in the order of their numbers, such as two steps with different numbers being executed simultaneously, or two steps with different numbers being executed in the reverse order of the illustration. This application does not limit this.

[0383] Those skilled in the art will recognize that the functions described in the embodiments of this application in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0384] The above are merely exemplary embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application shall be included within the protection scope of this application.

Claims

A method for transmitting sensing results, characterized in that, The method is executed by the first node, and the method includes: Based on model information from one or more first models, the perception results are sent. The first model is a mathematical model used to characterize the echo features of the first echo. The method according to claim 1, characterized in that, The transmission of perception results based on model information from one or more first models includes: If the measurement error between the first model and the second model is within the error range, the perception result is sent. The second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process. The method according to claim 1 or 2, characterized in that, The perception result includes at least one of the following: The model matching result is used to indicate whether the measured second echo during the sensing process matches the first model; A first index is used to indicate a first model that matches the echo characteristics of the second echo; Measurement error, used to indicate the measurement difference between the first model and the second model; A second index is used to indicate the first model with the smallest measurement error; Error matching results are used to indicate whether the measurement error is within the error range; A third index is used to indicate the first model where the measurement error is within the error range; Perceived information about the target; The parameters of the second model; The second model is a mathematical model used to characterize the echo features of the second echo. The method according to any one of claims 1 to 3, characterized in that, The model information includes model features, which are used to describe the first model. The method according to claim 4, characterized in that, The model features are indicated by at least one of the following methods: Model diagram; Model parameters; Model index; Wherein, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo. The method according to claim 5, characterized in that, The model diagram includes at least one of the following: a time-frequency diagram of the first echo; a time-delay time-varying diagram of the first echo; a displacement time-varying diagram of the first echo; an amplitude time-varying diagram of the first echo; and a phase time-varying diagram of the first echo. The method according to claim 5, characterized in that, The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo. The method according to claim 5, characterized in that, The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes. The method according to any one of claims 4 to 8, characterized in that, The model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process. The method according to claim 9, characterized in that, The error range can be represented by at least one of the following: mean square error; error percentage. The method according to any one of claims 1 to 10, characterized in that, The measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model. The method according to any one of claims 1 to 11, characterized in that, The method further includes: Receive model information of the one or more first models. A method for transmitting model information, characterized in that, The method is executed by the first node, and the method includes: Receive model information for one or more first models; The model information is used by the first node to send the sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo. The method according to claim 13, characterized in that, The perception result includes at least one of the following: The model matching result is used to indicate whether the measured second echo during the sensing process matches the first model; A first index is used to indicate a first model that matches the echo characteristics of the second echo; Measurement error, used to indicate the measurement difference between the first model and the second model; A second index is used to indicate the first model with the smallest measurement error; Error matching results are used to indicate whether the measurement error is within the error range; A third index is used to indicate the first model where the measurement error is within the error range; Perceived information about the target; The parameters of the second model; The second model is a mathematical model used to characterize the echo features of the second echo. The method according to claim 13 or 14 is characterized in that, The model information includes model features, which are used to describe the first model. The method according to claim 15, characterized in that, The model features are indicated by at least one of the following methods: Model diagram; Model parameters; Model index; Wherein, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo. The method according to claim 16, characterized in that, The model diagram includes at least one of the following: a time-frequency diagram of the first echo; a time-delay time-varying diagram of the first echo; a displacement time-varying diagram of the first echo; an amplitude time-varying diagram of the first echo; and a phase time-varying diagram of the first echo. The method according to claim 16, characterized in that, The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo. The method according to claim 16, characterized in that, The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes. The method according to any one of claims 15 to 19, characterized in that, The model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process. The method according to claim 20, characterized in that, The error range can be represented by at least one of the following: mean square error; error percentage. The method according to any one of claims 13 to 21, characterized in that, The measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model. The method according to any one of claims 13 to 22, characterized in that, The method further includes: Based on the model information of one or more first models, the perception results are sent. The method according to claim 23, characterized in that, Sending the perception result based on model information from one or more of the first models includes: If the measurement error between the first model and the second model is within the error range, the perception result is sent. The second model is a mathematical model used to characterize the echo characteristics of the measured second echo during the sensing process. A method for transmitting sensing results, characterized in that, The method is executed by the second node, and the method includes: Receive the sensing results; The perception result is sent by the first node based on model information of one or more first models, where the first model is a mathematical model used to characterize the echo features of the first echo. The method according to claim 25, characterized in that, The perception result includes at least one of the following: The model matching result is used to indicate whether the measured second echo during the sensing process matches the first model; A first index is used to indicate a first model that matches the echo characteristics of the second echo; Measurement error, used to indicate the measurement difference between the first model and the second model; A second index is used to indicate the first model with the smallest measurement error; Error matching results are used to indicate whether the measurement error is within the error range; A third index is used to indicate the first model where the measurement error is within the error range; Perceived information about the target; The parameters of the second model; The second model is a mathematical model used to characterize the echo features of the second echo. The method according to claim 25 or 26 is characterized in that, The model information includes model features, which are used to describe the first model. The method according to claim 27, characterized in that, The model features are indicated by at least one of the following methods: Model diagram; Model parameters; Model index; Wherein, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo. The method according to claim 28, characterized in that, The model diagram includes at least one of the following: a time-frequency diagram of the first echo; a time-delay time-varying diagram of the first echo; a displacement time-varying diagram of the first echo; an amplitude time-varying diagram of the first echo; and a phase time-varying diagram of the first echo. The method according to claim 28, characterized in that, The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo. The method according to claim 28, characterized in that, The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes. The method according to any one of claims 27 to 31, characterized in that, The model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process. The method according to claim 32, characterized in that, The error range can be represented by at least one of the following: mean square error; error percentage. The method according to any one of claims 25 to 33, characterized in that, The measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model. The method according to any one of claims 25 to 34, characterized in that, The method further includes: Send model information of the one or more first models. A method for transmitting model information, characterized in that, The method is executed by the second node, and the method includes: Send model information for one or more first models; The model information is used by the first node to send the sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo. The method according to claim 36, characterized in that, The perception result includes at least one of the following: The model matching result is used to indicate whether the measured second echo during the sensing process matches the first model; A first index is used to indicate a first model that matches the echo characteristics of the second echo; Measurement error, used to indicate the measurement difference between the first model and the second model; A second index is used to indicate the first model with the smallest measurement error; Error matching results are used to indicate whether the measurement error is within the error range; A third index is used to indicate the first model where the measurement error is within the error range; Perceived information about the target; The parameters of the second model; The second model is a mathematical model used to characterize the echo features of the second echo. The method according to claim 36 or 37, characterized in that, The model information includes model features, which are used to describe the first model. The method according to claim 38, characterized in that, The model features are indicated by at least one of the following methods: Model diagram; Model parameters; Model index; Wherein, the model pattern is used to indicate the waveform pattern of the first echo, the model parameters are used to indicate the waveform parameters of the first echo, and the model index is used to indicate the first model number corresponding to the first echo. The method according to claim 39, characterized in that, The model diagram includes at least one of the following: a time-frequency diagram of the first echo; a time-delay time-varying diagram of the first echo; a displacement time-varying diagram of the first echo; an amplitude time-varying diagram of the first echo; and a phase time-varying diagram of the first echo. The method according to claim 39, characterized in that, The model parameters include at least one of the following: the pattern type of the first echo; the repetition frequency of the first echo; and the pattern combination coefficient of the first echo. The method according to claim 39, characterized in that, The model index is defined by the communication protocol, or the model index is agreed upon or negotiated by the first node and other nodes. The method according to any one of claims 38 to 42, characterized in that, The model information also includes an error range, which is used to indicate the range of allowable differences between the model features of the second model and the model features of the first model. The second model is a mathematical model used to characterize the echo features of the measured second echo during the sensing process. The method according to claim 43 is characterized in that, The error range can be represented by at least one of the following: mean square error; error percentage. The method according to any one of claims 36 to 44, characterized in that, The measurement error between the second model and the first model includes at least one of the following: the mean square error between the second model and the first model; the mean square error between the same parameters of the second model and the first model. The method according to any one of claims 36 to 45, characterized in that, The method further includes: Receive the perception results. A first device, characterized in that, The first device includes: The sending module is used to send the perception results based on the model information of one or more first models; The first model is a mathematical model used to characterize the echo features of the first echo. A second device, characterized in that, The second device includes: The receiving module is used to receive the sensing results; The sensing result is sent by the first device based on model information of one or more first models, where the first model is a mathematical model used to characterize the echo features of the first echo. A first device, characterized in that, The first device includes: The receiving module is used to receive model information of one or more first models; The model information is used by the first device to send the sensing results, and the first model is a mathematical model used to characterize the echo features of the first echo. A second device, characterized in that, The second device includes: The sending module is used to send model information of one or more first models; The model information is used by the first device to send the sensing results, and the first model is a mathematical model used to characterize the echo characteristics of the first echo. A first node, characterized in that, The first node includes: A processor; a transceiver connected to the processor; a memory for storing executable instructions of the processor; wherein the processor is configured to load and execute the executable instructions to implement the method for transmitting perception results as described in any one of claims 1 to 12, and / or the method for transmitting model information as described in any one of claims 13 to 24. A second node, characterized in that, The second node includes: A processor; a transceiver connected to the processor; a memory for storing executable instructions of the processor; wherein the processor is configured to load and execute the executable instructions to implement the method for transmitting perception results as described in any one of claims 25 to 35, and / or the method for transmitting model information as described in any one of claims 36 to 46. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one program, which is loaded and executed by a processor to implement the method for transmitting perception results as described in any one of claims 1 to 12, and / or the method for transmitting model information as described in any one of claims 13 to 24, and / or the method for transmitting perception results as described in any one of claims 25 to 35, and / or the method for transmitting model information as described in any one of claims 36 to 46. A chip characterized in that, The chip includes programmable logic circuits and / or program instructions. When the chip is running on a first node, it is used to implement the method for transmitting perception results as described in any one of claims 1 to 12, and / or the method for transmitting model information as described in any one of claims 13 to 24. When the chip is running on a second node, it is used to implement the method for transmitting perception results as described in any one of claims 25 to 35, and / or the method for transmitting model information as described in any one of claims 36 to 46. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium. A processor retrieves the computer instructions from the computer-readable storage medium and executes the computer instructions to implement the method for transmitting perception results as described in any one of claims 1 to 12, and / or the method for transmitting model information as described in any one of claims 13 to 24, and / or the method for transmitting perception results as described in any one of claims 25 to 35, and / or the method for transmitting model information as described in any one of claims 36 to 46.

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