Navigation method and device, computer equipment and readable storage medium

Abstract

The invention relates to a navigation method and device, computer equipment and a readable storage medium. The method is applied to a first terminal, the first terminal is configured with a first agent and a second agent, and the method comprises the following steps: in response to a navigation starting signal detected by the first agent, triggering the second agent to start through the first agent; path planning is carried out through a second intelligent agent according to the positioning sensing data collected by the positioning sensing device, and navigation path data is obtained; and outputting the navigation path data. By adopting the method, the problem of failure of a traditional navigation mode in a signal-free or strong-signal-attenuation environment can be solved, and effective navigation path data can be provided for a user.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a navigation method, apparatus, computer device, and readable storage medium. Background Technology

[0002] Currently, in various scenarios where users need route navigation, such as outdoor hiking and vehicle driving, users often rely on navigation methods such as GPS (Global Positioning System), Wi-Fi (Wireless Fidelity) signal positioning, and mobile phone signal positioning.

[0003] However, existing navigation methods are often limited by signal coverage. As a result, in environments with no signal or significant signal attenuation, such as natural disasters (e.g., earthquakes, floods), remote areas (e.g., mountains, forests), and underground spaces (e.g., tunnels, mines), users often lose their way or even become disconnected from the outside world due to navigation failure, thus falling into dangerous situations.

[0004] Therefore, how to solve the problem of traditional navigation methods failing in environments with no signal or strong signal attenuation, and how to provide users with effective navigation path data, has become an urgent technical problem to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a navigation method, device, computer equipment, and readable storage medium to address the above-mentioned technical problems, which can solve the problem of the failure of traditional navigation methods in environments with no signal or strong signal attenuation, and provide users with effective navigation path data.

[0006] In a first aspect, this application provides a navigation method applied to a first terminal, the first terminal being configured with a first intelligent agent and a second intelligent agent, including:

[0007] In response to the detection of a navigation start signal by the first intelligent agent, the second intelligent agent is triggered to start through the first intelligent agent;

[0008] The second intelligent agent performs path planning based on the positioning and sensing data collected by the positioning and sensing device to obtain navigation path data;

[0009] Output navigation path data.

[0010] In one embodiment, detecting a navigation start signal by a first intelligent agent includes: determining that a navigation start signal has been detected by the first intelligent agent in response to the first intelligent agent detecting that the target signal interruption duration of the first terminal has reached a preset duration; wherein the target signal includes cellular network signals and / or location positioning signals; or, determining that a navigation start signal has been detected by the first intelligent agent in response to the first intelligent agent detecting a navigation start command from the user of the first terminal.

[0011] In one embodiment, the second intelligent agent performs path planning based on the positioning sensing data collected by the positioning sensing device to obtain navigation path data, including: the second intelligent agent performs path planning based on the positioning sensing data collected by the positioning sensing device to obtain initial path data; the second intelligent agent corrects the initial path data based on preset offline map data to obtain navigation path data.

[0012] In one embodiment, the second intelligent agent performs path planning based on the positioning sensing data collected by the positioning sensing device to obtain navigation path data, including: transmitting historical positioning sensing data collected by the positioning sensing device to the second intelligent agent through the first intelligent agent; and performing path planning based on the historical positioning sensing data and the real-time positioning sensing data collected by the positioning sensing device through the second intelligent agent to obtain navigation path data; wherein, the navigation path data includes navigation recommended path data and historical backtracking path data.

[0013] In one embodiment, the positioning sensing device includes multiple types of positioning sensing devices; a second intelligent agent performs path planning based on historical positioning sensing data and real-time positioning sensing data collected by the positioning sensing devices to obtain navigation path data, including: extracting data features from real-time positioning sensing data and historical positioning sensing data collected by the same type of positioning sensing device to obtain positioning feature data for each type of positioning sensing device; assigning positioning weights to the positioning feature data of different types of positioning sensing devices based on the status information of the first terminal; and performing path planning based on the positioning feature data and corresponding positioning weights of each type of positioning device to obtain navigation path data.

[0014] In one embodiment, the positioning sensing device includes a positioning sensing device of a first terminal and a visual sensor of a second terminal; the navigation method further includes: in response to detecting a navigation start signal through a first intelligent agent, triggering the second terminal to start the visual sensor through the first intelligent agent.

[0015] In one embodiment, outputting navigation path data includes: sending navigation path data to a second terminal through a second intelligent agent; wherein, after receiving the navigation path data, the second terminal outputs the navigation path data.

[0016] In one embodiment, the first terminal is further configured with a third intelligent agent, and the navigation method further includes: transmitting navigation path data to the third intelligent agent through the second intelligent agent; and in response to the third intelligent agent detecting the existence of an available communication channel, sending an alarm signal carrying the navigation path data to a preset receiving end through the third intelligent agent using the communication method corresponding to the available communication channel.

[0017] In one embodiment, an alarm signal carrying navigation path data is sent to a preset receiving end by a third intelligent agent using a communication method corresponding to an available communication channel. This includes sending an alarm signal carrying preset data and navigation path data to the preset receiving end by the third intelligent agent using a communication method corresponding to an available communication channel. The preset data includes environmental description information and / or user information of the user of the first terminal.

[0018] Secondly, this application also provides a navigation device applied to a first terminal, the first terminal being configured with a first intelligent agent and a second intelligent agent, including:

[0019] A startup module is used to trigger the startup of a second agent in response to the detection of a navigation startup signal by a first agent.

[0020] The path planning module is used by a second intelligent agent to plan a path based on the positioning and sensing data collected by the positioning and sensing device, and obtain navigation path data.

[0021] The path output module is used to output navigation path data.

[0022] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the various method embodiments provided in the first aspect.

[0023] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the various method embodiments provided in the first aspect.

[0024] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the various method embodiments provided in the first aspect.

[0025] The aforementioned navigation method, apparatus, computer equipment, and readable storage medium, by configuring a first intelligent agent and a second intelligent agent in a first terminal, enable the second intelligent agent to be activated upon detecting a navigation activation signal. The second intelligent agent then performs path planning based on positioning data collected by the positioning sensing device to obtain navigation path data, and outputs this navigation path data to guide the user of the first terminal. In this way, by combining the positioning sensing data collected by the positioning sensing device with the intelligent reasoning and decision-making capabilities of the intelligent agents, and through dynamic collaboration among multiple intelligent agents, a complete emergency navigation system is constructed. This system enables navigation path determination in environments with no signal or strong signal attenuation, solving the problem of traditional navigation methods failing and providing users with effective navigation path data. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a navigation method provided in some embodiments of this application;

[0028] Figure 2 This is a flowchart illustrating the process of determining navigation path data provided in some embodiments of this application;

[0029] Figure 3 Flowcharts illustrating the determination of navigation path data provided in other embodiments of this application;

[0030] Figure 4 A flowchart illustrating the process of determining navigation path data is provided in some embodiments of this application;

[0031] Figure 5 A flowchart illustrating a navigation method provided in other embodiments of this application;

[0032] Figure 6A A flowchart illustrating a navigation method provided in some embodiments of this application;

[0033] Figure 6B An overall framework diagram of the navigation method provided in some embodiments of this application;

[0034] Figure 7 Structural block diagrams of navigation devices provided in some embodiments of this application;

[0035] Figure 8This is an internal structural diagram of a computer device provided in some embodiments of this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments or any combination of multiple embodiments.

[0038] Current navigation technologies are often limited by signal coverage. Consequently, in environments with no signal or significant signal attenuation, such as natural disasters, remote areas, or underground spaces, users often lose their way due to navigation failure, or even become cut off from the outside world, leading to dangerous situations. Therefore, solving the problem of traditional navigation methods failing in environments with no signal or severe signal attenuation, and providing users with effective navigation path data, has become an urgent technical challenge.

[0039] Therefore, to address the aforementioned technical problems, a navigation method is provided in an exemplary embodiment. This method can be applied to a first terminal, which may be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices may include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices may include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc.

[0040] The aforementioned first terminal is configured with a first agent and a second agent. An agent is an entity capable of perceiving its environment, making autonomous decisions, and executing actions. It possesses characteristics such as autonomy, responsiveness, goal orientation, and interactivity. Based on the current environmental state and internal models, it achieves specific goals through perception, reasoning, planning, and action, and is widely used in robotics, software systems, game AI (Artificial Intelligence), and autonomous driving. Optionally, the first and second agents in the first terminal can be integrated into the same application or integrated into different applications. Further, optionally, the aforementioned application can be a built-in function program of the first terminal system (such as a built-in system application, a function program hidden in a system module, etc.), or a standalone application downloaded and installed by the user.

[0041] In an exemplary embodiment, the navigation method described above can be executed by a device such as a chip or a chip module. Optionally, the chip or chip module may have algorithms or models corresponding to the first and second intelligent agents programmed into it, so as to implement the functions of the first and second intelligent agents and thus execute the navigation method described above. Optionally, the algorithms or models corresponding to the first and second intelligent agents may be programmed into the same chip or chip module, or into different chips or chip modules respectively.

[0042] In one exemplary embodiment, such as Figure 1 As shown, a navigation method is provided. Taking the application of this method to a first terminal as an example, the first terminal is configured with a first intelligent agent and a second intelligent agent, and may include the following steps:

[0043] S101, in response to the detection of a navigation start signal by the first intelligent agent, the second intelligent agent is triggered to start by the first intelligent agent.

[0044] Optionally, the first intelligent agent can start automatically when the first terminal is powered on, or the first intelligent agent can start in response to a wake-up command from the user of the first terminal, such as when the user of the first terminal clicks on the application space corresponding to the first intelligent agent or when the user of the first terminal issues a voice wake-up command.

[0045] After the first agent starts, it continuously detects navigation start signals. Upon detecting a navigation start signal, the first agent triggers the start of the second agent. Optionally, the first agent communicates with the second agent based on the A2A (Agent-to-Agent) protocol to transmit a start signal to the second agent, which then starts in response to the received start signal. The A2A protocol is an open-source, standardized agent interaction protocol designed to solve interoperability issues between AI agents built on different platforms, technology stacks, or from different vendors. This protocol enables agents to collaborate securely and efficiently, like a "common language," by defining unified communication standards, task management, and capability discovery mechanisms. Its core functions include Agent Card (a JSON-formatted agent capability description file that enables dynamic service discovery), task lifecycle management (supporting state transitions from immediate response to long-term complex tasks), multimodal interaction (compatible with data formats such as text, audio, and video), and enterprise-level security (integrated authentication and authorization mechanisms). This drives the transformation of agents from "isolated operation" to "open collaboration," and is suitable for cross-system task orchestration (such as the linkage between ERP (Enterprise Resource Planning) and cloud analytics services) and multimodal processing (such as meeting minutes + summary generation). It also complements the MCP (Model Context Protocol) to jointly build a standardized infrastructure for the AI ​​ecosystem.

[0046] In one optional embodiment, S101 may include determining that a navigation start signal has been detected by the first intelligent agent in response to the first intelligent agent detecting that the target signal interruption duration of the first terminal has reached a preset duration; wherein the target signal includes cellular network signal and / or location positioning signal.

[0047] In this embodiment, after the first intelligent agent is activated, it can continuously detect the communication status of the cellular network signal and / or location signal of the first terminal. If the target signal interruption duration of the first terminal reaches a preset duration, it determines that a navigation activation signal has been detected. Therefore, in response to the navigation activation signal, the first terminal triggers the activation of the second terminal. Optionally, the first intelligent agent uses a sliding window algorithm to detect the target signal status of the first terminal in real time (such as GPS positioning signal strength, cellular network signal connectivity, etc.) to determine whether the first terminal has a target signal terminal, and thus determines whether a navigation activation signal has been detected. Optionally, the location signal includes satellite positioning signals such as GPS positioning signals.

[0048] In another alternative embodiment, S101 may include determining that a navigation start signal has been detected by the first intelligent agent in response to the first intelligent agent detecting a navigation start command from the user of the first terminal.

[0049] In this embodiment, the user of the first terminal can actively control the first intelligent agent to trigger the startup of the second intelligent agent. After the first intelligent agent is started, it can continuously detect the navigation startup command from the user of the first terminal. Upon detecting the navigation startup command, it determines that a navigation startup signal has been detected, and thus, in response to the navigation startup signal, the first terminal triggers the startup of the second terminal. Optionally, the navigation startup command is a voice command from the user of the first terminal containing startup keywords, or an command generated in response to a specified triggering operation (such as double-clicking, clicking a specified control, etc.) by the user of the first terminal on the first intelligent agent.

[0050] Optionally, in response to the first intelligent agent detecting an emergency navigation activation signal, the first intelligent agent outputs emergency prompt information. The first intelligent agent can interact with the user of the first terminal via voice. Therefore, in response to the first intelligent agent detecting the emergency navigation activation signal, while triggering the activation of the second intelligent agent, the first intelligent agent can also output emergency prompt information to remind the user of the first terminal, such as the first intelligent agent outputting the current navigation status, emergency instructions to adjust the route, location signal strength, cellular network signal connectivity status, and environmental information.

[0051] S102, the second intelligent agent performs path planning based on the positioning and sensing data collected by the positioning and sensing device to obtain navigation path data.

[0052] After the second intelligent agent is activated, it acquires the positioning data collected by the positioning sensing device. Then, the second intelligent agent can perform path planning based on the positioning data to obtain navigation path data. This navigation path data includes at least recommended navigation path data, which indicates the possible movement paths that the user of the first terminal can choose if the first intelligent agent detects a navigation activation signal and continues moving forward.

[0053] Optionally, the positioning sensing device can store the positioning sensing data collected most recently within a preset time period or most recently within a preset number of times in its local storage space. Thus, after the second intelligent agent is activated, it can send a data acquisition request to the positioning sensing device, so that the positioning sensing device responds to the received data acquisition request and feeds back the locally stored positioning sensing data to the second intelligent agent. For example, it feeds back the positioning sensing data collected within the time period between two consecutive data acquisition requests (which can be called real-time positioning sensing data).

[0054] Optionally, the positioning sensing device includes at least a visual sensor and / or multiple motion sensors of the first terminal. The positioning sensing data then includes environmental visual data (such as environmental image data and / or environmental video data) collected by the visual sensor and / or motion data collected by the multiple motion sensors (motion data collected by gyroscopes, geomagnetic sensors, accelerometers, etc.). Further, optionally, in addition to including a visual sensor and / or multiple motion sensors of the first terminal, the positioning sensing device may also include a satellite positioning device (such as a GPS device) of the first terminal. In this case, the positioning sensing data also includes satellite positioning data (such as high-precision GPS data). Still, optionally, the positioning sensing device may also include other positioning sensing devices capable of participating in path planning, such as a lidar sensor of the first terminal.

[0055] Optionally, the second intelligent agent can perform multi-source data fusion on motion data collected by multiple motion sensors to obtain positioning feature data, and then perform path planning based on the positioning feature data to obtain navigation path data. Optionally, the second intelligent agent inputs the motion data collected by multiple motion sensors into the multi-source fusion data model to obtain the positioning feature data output by the multi-source fusion data model.

[0056] Optionally, the second agent can perform Simultaneous Localization and Mapping (SLAM) based on visual data, and determine navigation path data based on the results of SLAM.

[0057] In one optional embodiment, the positioning sensing device includes a positioning sensing device of a first terminal and a visual sensor of a second terminal. The path navigation method further includes, in response to detecting a navigation initiation signal by a first intelligent agent, triggering the second terminal to activate its visual sensor via the first intelligent agent. In this embodiment, the navigation path data can be determined through the cooperation of the positioning sensing device of the first terminal and the visual sensor of the second terminal. When the first intelligent agent detects a navigation initiation signal, it triggers the second terminal to activate its visual sensor. After activation, the visual sensor of the second terminal collects environmental visual data (such as environmental image data and / or environmental video data) and feeds the collected environmental visual data back to the second intelligent agent. The second intelligent agent then performs path planning based on the environmental visual data and the positioning sensing data collected by the positioning sensing device of the first terminal to obtain navigation path data. Optionally, the positioning sensing device of the first terminal includes a satellite positioning device and / or multiple motion sensors.

[0058] Optionally, the second terminal sends the aforementioned environmental visual data to the first intelligent agent, and the first intelligent agent, in response to receiving the aforementioned environmental visual data, transmits the aforementioned environmental visual data to the second intelligent agent; or, the second terminal directly sends the aforementioned environmental visual data to the second intelligent agent.

[0059] Optionally, the second terminal and the second intelligent agent employ a hybrid BLE (Bluetooth Low Energy) / Wi-Fi transmission protocol to transmit environmental visual data. Specifically, when the amount of environmental visual data is lower than a set data volume (low bandwidth), the environmental visual data is directly transmitted to the second intelligent agent via the BLE protocol; conversely, when the amount of environmental visual data is not lower than the set data volume (high bandwidth), the environmental visual data is directly transmitted to the second intelligent agent via the Wi-Fi protocol. This approach ensures the stability of environmental visual data transmission while reducing transmission power consumption.

[0060] Optionally, the second terminal may be a terminal of the same type as the first terminal, or a terminal of a different type from the first terminal.

[0061] S103, output navigation path data.

[0062] After the second intelligent agent obtains the navigation path data, the first terminal outputs the navigation path data to guide the user of the first terminal. Optionally, the first terminal can output the navigation path data in various ways. For example, the first terminal can broadcast the navigation path data via voice, or the first terminal can display a navigation screen including the navigation path data through an output device such as a display screen. The navigation path data can be displayed in the navigation screen in various ways, such as a map, virtual directional arrows, obstacle prompts, etc.

[0063] In one optional embodiment, S103 includes sending navigation path data to a second terminal through a second intelligent agent; wherein, after receiving the navigation path data, the second terminal outputs the navigation path data.

[0064] In this embodiment, navigation for the user of the first terminal can be achieved through the cooperation of the first terminal and the second terminal using navigation path data. Specifically, after receiving the navigation path data, the second intelligent agent sends the navigation path data to the second terminal, which then outputs the navigation path data in response. Optionally, the second terminal can output the navigation path data in various ways. For example, the second terminal can announce the navigation path data via voice, or the first terminal can display a navigation screen including the navigation path data through an output device such as an AR (Augmented Reality) interaction module. The navigation path data can be displayed on the navigation screen in various ways, such as a map, virtual directional arrows, and obstacle prompts.

[0065] In the aforementioned navigation method, a first intelligent agent and a second intelligent agent are configured in the first terminal. When the first intelligent agent detects a navigation initiation signal, it triggers the second intelligent agent to start. The second intelligent agent then performs path planning based on the positioning data collected by the positioning sensing device to obtain navigation path data. This navigation path data is then output to guide the user of the first terminal. In this way, by combining the positioning data collected by the positioning sensing device with the intelligent reasoning and decision-making capabilities of the intelligent agents, and through dynamic collaboration among multiple intelligent agents, a complete emergency navigation system is constructed. This system can determine navigation path data in environments with no signal or strong signal attenuation, solving the problem of traditional navigation methods failing and providing users with effective navigation path data.

[0066] Based on the above embodiments, in an exemplary embodiment, the determination of the navigation path data is further refined. Optionally, such as... Figure 2 As shown, the following steps may be included:

[0067] S201, the second intelligent agent performs path planning based on the positioning and sensing data collected by the positioning and sensing device to obtain initial path data.

[0068] In this embodiment, after the second intelligent agent is started, the path data obtained by it through path planning based on the positioning and sensing data collected by the positioning and sensing device can be used as the initial path data.

[0069] S202, through the second intelligent agent, the initial path data is corrected according to the preset offline map data to obtain navigation path data.

[0070] Offline map data can be pre-stored in the local storage space of the second intelligent agent or in the shared storage space of the first terminal, corresponding to the storage space of the second intelligent agent. Since the offline map data can be high-accuracy map data such as real map data, after obtaining the initial path data, the second intelligent agent can correct the initial path data based on the offline map data to obtain the final output navigation path data.

[0071] In this embodiment, the initial path data determined based on the positioning and sensing data is corrected based on highly accurate offline map data, which can reduce the path deviation of the final navigation path data and ensure the accuracy of the navigation path data.

[0072] Based on the above embodiments, in an exemplary embodiment, the determination of the navigation path data is further refined. Optionally, such as... Figure 3 As shown, the following steps may be included:

[0073] S301, through the first intelligent agent, transmits the historical positioning and sensing data collected by the positioning and sensing device to the second intelligent agent.

[0074] Understandably, each positioning sensing device can continuously collect positioning sensing data during the movement of the user of the first terminal. For example, the positioning sensing devices of the first terminal (such as gyroscopes, geomagnetic sensors, accelerometers, satellite positioning devices, etc.) can automatically start upon power-on of the first terminal and continuously collect positioning sensing data. Similarly, the visual sensor of the second terminal can continuously collect environmental visual data in response to a trigger operation by the user of the first terminal. Based on this, when the first intelligent agent detects a navigation start signal, there may be historical positioning sensing data collected by the positioning sensing devices before the first intelligent agent detected the navigation start signal. Therefore, in response to the detection of the navigation start signal, after the first intelligent agent triggers the second intelligent agent to start, the first intelligent agent can transmit the historical positioning sensing data collected by the positioning sensing devices to the second intelligent agent. Optionally, the first intelligent agent transmits historical positioning sensing data to the second intelligent agent within a preset time period, ending at the moment the navigation positioning signal is detected.

[0075] Optionally, after the first intelligent agent is started, it can periodically send data acquisition requests to each positioning and sensing device, so that the positioning and sensing devices respond to the received data acquisition requests and feed back the positioning and sensing data collected in the current period to the first intelligent agent.

[0076] Optionally, the first intelligent agent communicates with the second intelligent agent based on the A2A protocol, transmitting historical positioning and sensing data collected by the positioning and sensing device to the second intelligent agent.

[0077] S302, through the second intelligent agent, performs path planning based on historical positioning perception data and real-time positioning perception data collected by the positioning perception device, and obtains navigation path data.

[0078] The navigation path data includes recommended navigation path data and historical backtracking path data.

[0079] Since each positioning and sensing device continuously collects positioning and sensing data, even after the first agent detects the navigation initiation signal, it will continue to collect positioning and sensing data to obtain real-time positioning and sensing data. The second agent can then perform path planning based on historical and real-time positioning and sensing data to obtain navigation path data. Because the positioning and sensing data obtained by the second agent during path planning includes historical positioning and sensing data, during the path planning process, the second agent can use this historical data to trace back the user's historical movement path before the first agent detected the navigation initiation signal, obtaining historical backtracking path data. Based on this, the final navigation path data obtained by the second terminal includes both recommended navigation path data and historical backtracking path data.

[0080] Optionally, in some cases, the positioning sensing device includes a satellite positioning device of the first terminal and a visual sensor of the second terminal. Before the first intelligent agent detects the navigation start signal, there may be no historical positioning sensing data (i.e., historical environmental visual data) collected by the visual sensor of the second terminal, and after the first intelligent agent detects the navigation start signal, there may be no real-time positioning sensing data (i.e., real-time satellite positioning data) collected by the satellite positioning device of the first terminal. Thus, the second intelligent agent can perform path planning based on the existing historical positioning sensing data and the real-time positioning sensing data that the positioning sensing device can collect, in order to obtain navigation path data.

[0081] Optionally, after obtaining the above navigation path data, the second intelligent agent can modify the navigation path data according to preset offline map data to obtain the final navigation path data for output.

[0082] In this embodiment, the second intelligent agent can obtain navigation path data, including navigation recommendation path data and historical backtracking path data, based on historical positioning perception data and real-time positioning perception data. It can automatically backtrack the historical path based on historical positioning perception data without requiring manual recording by the user of the first terminal, and without relying on stable target signal support, thus providing accuracy in path recording in complex environments. Furthermore, when outputting the above navigation path data, it can guide the user of the first terminal to return according to the above historical backtracking path, so as to restore contact with the outside world as soon as possible and escape from the dangerous situation.

[0083] Based on the above embodiments, in one exemplary embodiment, the determination of the navigation path data is further refined. Optionally, the positioning sensing device includes multiple types of positioning sensing devices, such as... Figure 4 As shown, the following steps may be included:

[0084] S401, through the second intelligent agent, extracts data features from the real-time positioning sensing data and historical positioning sensing data collected by the same type of positioning sensing device to obtain the positioning feature data of each type of positioning sensing device.

[0085] The positioning sensing device can include multiple types of positioning sensing devices, such as visual sensors, satellite positioning devices, and multiple motion sensors. After obtaining the aforementioned real-time positioning sensing data and historical positioning sensing data, the second intelligent agent can first classify the aforementioned real-time positioning sensing data and historical positioning sensing data to obtain the real-time positioning sensing data and historical positioning sensing data collected by each type of positioning sensing device. Then, for each type of positioning sensing device, the second intelligent agent performs data feature extraction on the real-time positioning sensing data and historical positioning sensing data collected by that type of positioning sensing device to obtain the positioning feature data of that type of positioning sensing device.

[0086] For example, for motion sensors, the second agent performs multi-source data fusion on historical motion data and real-time motion data collected by multiple motion sensors to obtain the positioning feature data corresponding to the motion sensors; for vision sensors, the second agent performs SLAM on historical environmental vision data and real-time environmental vision data collected by the vision sensors to obtain the SLAM result, which is used as the positioning feature data corresponding to the motion sensors; for satellite positioning devices, satellite positioning data is directly used as positioning feature data.

[0087] S402, through the second intelligent agent, positioning weights are assigned to the positioning feature data of different types of positioning sensing devices based on the status information of the first terminal.

[0088] Optionally, the status information of the first terminal includes battery level, signal strength, timestamp information, etc.

[0089] After obtaining the positioning feature data of each type of positioning sensing device, the second intelligent agent can acquire the state information of the first terminal and assign positioning weights to the positioning feature data of different types of positioning sensing devices based on the state information of the first terminal.

[0090] S403, through the second intelligent agent, performs path planning based on the positioning feature data and corresponding positioning weight of each type of positioning device to obtain navigation path data.

[0091] After obtaining the positioning feature data and corresponding positioning weights of each type of positioning device, the second intelligent agent can perform path planning based on the positioning feature data and corresponding positioning weights of each type of positioning device to obtain navigation path data.

[0092] Optionally, the second intelligent agent inputs the positioning feature data and corresponding positioning weights of each type of positioning device into the path backtracking model to obtain the navigation path data output by the path backtracking model.

[0093] In this embodiment, by assigning positioning weights to the positioning feature data of different types of positioning sensing devices based on the status information of the first terminal, it is possible to ensure that the amount of data involved in the path planning operation matches the operating status of the first terminal, thereby reducing the power consumption of the first terminal while ensuring the accuracy of the navigation path data.

[0094] Based on the above embodiments, in an exemplary embodiment, the first terminal is further configured with a third intelligent agent.

[0095] Optionally, the first, second, and third intelligent agents in the first terminal are integrated into the same application or into at least two different applications. Optionally, the chip or chip module contains algorithms or models corresponding to the first, second, and third intelligent agents, enabling the implementation of their functions and thus executing the aforementioned navigation method. Optionally, the algorithms or models corresponding to the first, second, and third intelligent agents are programmed into the same chip or chip module, or into at least two different chips or chip modules.

[0096] Correspondingly, such as Figure 5 As shown, the navigation method may include the following steps:

[0097] S501, in response to the detection of a navigation start signal by the first intelligent agent, the second intelligent agent is triggered to start by the first intelligent agent.

[0098] Optionally, the first intelligent agent communicates with the third intelligent agent based on the A2A protocol to transmit a start signal to the third intelligent agent, and then the third intelligent agent starts in response to the received start signal.

[0099] S502, the second intelligent agent performs path planning based on the positioning and sensing data collected by the positioning and sensing device to obtain navigation path data.

[0100] S503 outputs navigation path data.

[0101] The specific implementation methods of S501-S503 are the same as those of S101-S103, and will not be repeated here.

[0102] S504 transmits navigation path data to the third intelligent agent through the second intelligent agent.

[0103] After obtaining the navigation path data, the second agent transmits the navigation path data to the third agent.

[0104] Optionally, the second agent communicates with the third agent based on the A2A protocol to transmit navigation path data to the third agent.

[0105] S505, in response to the third agent detecting the existence of an available communication channel, the third agent sends an alarm signal carrying navigation path data to a preset receiver using the communication method corresponding to the available communication channel.

[0106] After activation, the third intelligent agent continuously detects communication channels to determine the availability of any available channels. This includes checking GPS signal strength, satellite channel quality (for satellite communication), and cellular network connectivity (for sending text messages, making phone calls, and sending instant messaging messages). If an available communication channel is detected, the third intelligent agent automatically sends an alarm signal carrying the navigation path data to a preset recipient (such as a preset platform or preset contact person) using the corresponding communication method (sending text messages, making satellite phone calls, or sending instant messaging messages). This improves the timeliness of the alarm and the positioning efficiency of the preset recipient for the user of the first terminal. Consequently, the preset recipient can take timely measures to help the user of the first terminal quickly restore contact with the outside world and escape danger.

[0107] In one optional embodiment, S505 includes sending an alarm signal carrying preset data and navigation path data to a preset receiving end via a third intelligent agent using a communication method corresponding to an available communication channel; wherein the preset data includes environmental description information and / or user information of the user of the first terminal. Optionally, the user information includes name, health status, etc. In this way, the preset receiving end can better connect with the current situation of the user of the first terminal and take more targeted actions to help the user of the first terminal.

[0108] Based on the above embodiments, in an exemplary embodiment, the first terminal is configured with a first intelligent agent, a second intelligent agent, and a third intelligent agent, and the positioning sensing device includes a positioning sensing device of the first terminal and a visual sensor of the second terminal, such as... Figure 6A As shown, the navigation method may include the following steps:

[0109] S601, in response to the first intelligent agent detecting that the target signal interruption duration of the first terminal has reached a preset duration, it is determined that a navigation start signal has been detected by the first intelligent agent; or, in response to the first intelligent agent detecting a navigation start command from the user of the first terminal, it is determined that a navigation start signal has been detected by the first intelligent agent.

[0110] S602, in response to detecting a navigation start signal through the first intelligent agent, triggering the second and third intelligent agents to start through the first intelligent agent, and triggering the second terminal to start the visual sensor.

[0111] S603 transmits historical positioning data collected by the positioning sensing device to the second intelligent agent through the first intelligent agent.

[0112] S604, through the second intelligent agent, extracts data features from the real-time positioning sensing data and historical positioning sensing data collected by the same type of positioning sensing device to obtain the positioning feature data of each type of positioning sensing device, and assigns positioning weights to the positioning feature data of different types of positioning sensing devices according to the status information of the first terminal.

[0113] S605, through the second intelligent agent, performs path planning based on the positioning feature data and corresponding positioning weight of each type of positioning device to obtain initial path data, and corrects the initial path data based on preset offline map data to obtain navigation path data.

[0114] S606, through the second intelligent agent, sends navigation path data to the second terminal; wherein, after receiving the navigation path data, the second terminal outputs the navigation path data.

[0115] S607 transmits navigation path data to the third intelligent agent through the second intelligent agent.

[0116] S608, in response to the third agent detecting the existence of an available communication channel, the third agent sends an alarm signal carrying preset data and navigation path data to a preset receiver using the communication method corresponding to the available communication channel.

[0117] The specific implementation methods of S601-S608 are the same as those in the above method embodiments, and will not be repeated here.

[0118] For example, such as Figure 6B As shown, taking a smartphone as the first terminal and smart glasses as the second terminal as an example, this navigation method is illustrated.

[0119] Among them, the personal assistant agent is the first intelligent agent, the path backtracking agent is the second intelligent agent, and the emergency assistant agent is the third intelligent agent. The different intelligent agents communicate with each other through the A2A protocol.

[0120] The LLM (Large Language Model) in different agents is used to coordinate the interaction between different agents and make decisions based on the detection results of the agent and other factors, and generate corresponding instructions.

[0121] Common data acquisition refers to the acquisition of positioning and sensing data by different positioning and sensing devices. Among these, gyroscopes, geomagnetic sensors, and accelerometers are motion sensors in smartphones; GNSS (Global Navigation Satellite System) is a satellite positioning device in smartphones; visual images are environmental visual data (video data) collected by the camera in smart glasses and transmitted to the smartphone via a BLE / Wi-Fi hybrid transmission protocol. Battery level and signal strength indicate the smartphone's operating status.

[0122] The personal assistant agent acts as a steward within the smartphone, deciding to schedule and activate the functions required by the user (i.e., the smartphone user). For example, when the user activates emergency navigation (the first agent detects the user's navigation activation command) or when the personal assistant agent decides that activation is necessary (the first agent detects that the target signal interruption duration of the smartphone has reached a preset duration), the personal assistant agent will communicate with the emergency assistant agent and the route backtracking agent based on the A2A protocol to activate the functions that each agent is responsible for.

[0123] The personal assistant, Agent, can also interact with users through natural language, providing real-time feedback on navigation status and emergency commands (such as voice prompts for route adjustments).

[0124] The personal assistant Agent will make real-time autonomous decisions to control sensor data acquisition based on the results of communication status monitoring (target signal monitoring results). When the positioning signals are intersecting and the user may be in an emergency environment, it will start multi-source data acquisition for use in subsequent navigation backtracking, and at the same time prompt the user for signal strength or surrounding environment information to prevent loss of contact.

[0125] The path backtracking agent takes multi-source data (public data) as input, processes it through a path backtracking hybrid expert model (BMOE, a hybrid model consisting of a multi-source fusion data model, high-precision GPS data, device data, a deep SLAM model, a door unit, and a path backtracking model), and outputs navigation path data for use by the emergency assistant agent and the AR interaction module in the smart glasses. Among these:

[0126] The multi-source fusion data model is based on AI neural networks to extract the fusion features of motion data (geomagnetic sensor data, accelerometer data, and gyroscope data) collected by motion sensors. These features are used as positioning feature data for the motion sensor pairs, input to the gating unit, and used as input to the path backtracking model.

[0127] Deep SLAM models are based on convolutional neural networks. They take visual images as input and obtain localization feature data corresponding to the visual sensors, including camera pose features and environmental map features. Continuous camera pose features allow for further trajectory mapping and optimization, while environmental map features provide spatial structure information, supporting environmental comparison and relocalization during path backtracking.

[0128] High-precision GPS data is used to assist the path backtracking model in correcting path backtracking information. Specifically, when a GPS positioning signal is available, the acquired positioning information is used to correct the backtracking path.

[0129] Device data, including smartphone battery level, signal strength, and timestamps, is used by the gating unit to set positioning weights for different types of positioning sensing devices (motion sensors, satellite positioning devices, and visual sensors) to control the weights of different input features (positioning feature data) in the path backtracking model. For example, when the battery is low, the gating model controls the weight calculation parameters of the multi-source fusion data model or deep SLAM model through feedback connections, reducing the computational parameters involved in path planning by the path backtracking model, thereby reducing device power consumption.

[0130] The gating unit dynamically controls the feature weights of positioning feature data input from different types of positioning sensing devices to the path backtracking model. For example, the weight of deep SLAM (i.e., the weight of positioning feature data corresponding to visual sensors) decreases from 0.8 to 0.3, while the weight of multi-source fusion data (i.e., the weight of positioning feature data from motion sensor pairs) increases from 0.2 to 0.5. If the expert model (i.e., the multi-source fusion data model or the deep SLAM model) fails to respond within a timeout period, the gating unit automatically switches to another expert (i.e., the deep SLAM model or the multi-source fusion data model) to ensure robustness. The scheduling mechanism of the gating unit can be controlled by LLM instructions in the path backtracking agent. When low battery is detected or a low-power instruction is received, the gating unit controls the calculation parameters of the multi-source fusion data model, the deep SLAM model, and the path backtracking model through feedback connections and outputs, thereby reducing power consumption.

[0131] The path backtracking model is based on a neural network. It receives feature information (positioning feature data and corresponding positioning weights of different types of positioning sensing devices) output by the gating unit, and combines it with offline map data to output the final navigation path data (i.e., navigation data).

[0132] The Emergency Assistant Agent executes tasks autonomously without user intervention. Upon receiving an emergency command from the Personal Assistant Agent (i.e., initiating an emergency distress call, or responding to the Agent's trigger), it autonomously monitors the smartphone's GPS signal strength, satellite channel quality, and cellular network connection status in real time via a communication status monitoring module. It then uses a sliding window algorithm to determine if communication has been restored (whether an available communication channel exists). Once an available communication channel is detected, it immediately sends an emergency distress message (alarm information carrying navigation path data) to the rescue platform (i.e., the preset recipient) via the corresponding method (e.g., sending a text message, making an emergency call, or satellite communication), ensuring a delivery rate of ≥99%. The distress message includes current location information and a route backtracking information (navigation path data) to guide the user's return journey. Users can also preset custom distress message templates (i.e., preset data, such as user information (name, health status, etc.), environmental description information, etc.) and send them to the rescue platform and emergency contacts (i.e., the preset recipient) via encrypted communication protocols (e.g., TLS 1.3 (Transport Layer Security 1.3)).

[0133] The AR interaction module is mounted on the smart glasses. The navigation path data output by the BMoE model is transmitted to the smart glasses through a BLE / Wi-Fi hybrid transmission protocol, and is matched and visualized overlaid with the environmental path in real time (i.e., output path navigation data).

[0134] The BLE / Wi-Fi hybrid transmission protocol design primarily achieves energy efficiency optimization and stable transmission through a layered transmission mechanism and dynamic sleep strategy, balancing real-time performance and low power consumption requirements. Specifically:

[0135] Layered transmission mechanism: Low-bandwidth data (such as commands and navigation coordinate data) is transmitted in real time via low-power BLE, while high-bandwidth image data is transmitted via Wi-Fi packet segmentation (each image frame is divided into 4 packets), and timing consistency is ensured by synchronization through packet header timestamps (error <10ms).

[0136] Packet-based sleep strategy: The Wi-Fi module enters deep sleep (RF off) during non-image transmission periods and wakes up only during active periods; the BLE module wakes up periodically (every 100ms) to process sensor data.

[0137] Low power consumption control: Combining Dynamic Voltage and Frequency Scaling (DVFS) technology with hardware power management, Wi-Fi operates in a high-power mode of 120MHz when active and drops to 10MHz (Megahertz) when idle; the sensor module uses event-driven sampling to avoid continuous high power consumption.

[0138] Stability assurance: Through a sliding window packet loss mechanism and timestamp calibration (synchronized every 10 seconds), the success rate of image packet reassembly is ensured to be over 99%, and the total energy consumption is reduced by 35%.

[0139] In this way, by integrating multiple positioning and sensing devices, dynamically cooperating with multiple intelligent agents, establishing communication recovery mechanisms, and coordinating AR navigation interaction, the problem of traditional navigation methods failing in environments with no signal or strong signal attenuation is solved. It can be widely applied in urban disaster emergency response (earthquake and fire escape route linkage rescue), outdoor exploration (navigation in mountainous / tunnel scenarios without GPS) and medical emergency (health data and location-optimized emergency dispatch) fields, with high intelligence, high robustness and high security.

[0140] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0141] Based on the same inventive concept, this application also provides a navigation device for implementing the navigation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more navigation device embodiments provided below can be found in the limitations of the navigation method described above, and will not be repeated here.

[0142] In one exemplary embodiment, such as Figure 7 As shown, a navigation device is provided, including: a startup module 710, a path planning module 720, and a path output module 730, wherein:

[0143] The startup module 710 is used to trigger the startup of the second agent in response to the detection of a navigation startup signal by the first agent;

[0144] The path planning module 720 is used to perform path planning based on the positioning sensing data collected by the positioning sensing device through the second intelligent agent, and obtain navigation path data.

[0145] The path output module 730 is used to output navigation path data.

[0146] In an exemplary embodiment, the startup module 710 is specifically configured to: determine that a navigation startup signal has been detected by the first intelligent agent in response to the first intelligent agent detecting that the target signal interruption duration of the first terminal has reached a preset duration; wherein the target signal includes cellular network signals and / or location positioning signals; or, determine that a navigation startup signal has been detected by the first intelligent agent in response to the first intelligent agent detecting a navigation startup instruction from the user of the first terminal.

[0147] In an exemplary embodiment, the path planning module 720 is specifically used to: perform path planning based on the positioning sensing data collected by the positioning sensing device through the second intelligent agent to obtain initial path data; and correct the initial path data based on preset offline map data through the second intelligent agent to obtain navigation path data.

[0148] In an exemplary embodiment, the path planning module 720 includes: a data transmission unit, configured to transmit historical positioning sensing data collected by the positioning sensing device to a second intelligent agent via a first intelligent agent; and a path planning unit, configured to perform path planning based on the historical positioning sensing data and the real-time positioning sensing data collected by the positioning sensing device via the second intelligent agent to obtain navigation path data; wherein the navigation path data includes navigation recommended path data and historical backtracking path data.

[0149] In an exemplary embodiment, the positioning sensing device includes multiple types of positioning sensing devices; the path planning unit is specifically used to: extract data features from real-time positioning sensing data and historical positioning sensing data collected by the same type of positioning sensing device through a second intelligent agent to obtain positioning feature data for each type of positioning sensing device; assign positioning weights to the positioning feature data of different types of positioning sensing devices according to the status information of the first terminal through the second intelligent agent; and perform path planning according to the positioning feature data and corresponding positioning weights of each type of positioning device through the second intelligent agent to obtain navigation path data.

[0150] In an exemplary embodiment, the positioning sensing device includes a positioning sensing device of a first terminal and a visual sensor of a second terminal; the activation module 710 is further configured to: in response to detecting a navigation activation signal through a first intelligent agent, trigger the second terminal to activate the visual sensor through the first intelligent agent.

[0151] In an exemplary embodiment, the path output module 730 is specifically used to: send navigation path data to a second terminal through a second intelligent agent; wherein, after receiving the navigation path data, the second terminal outputs the navigation path data.

[0152] In an exemplary embodiment, the first terminal is further configured with a third intelligent agent, and the startup module 710 is further configured to trigger the third intelligent agent to start in response to the first intelligent agent detecting a navigation startup signal; the navigation device further includes: a data transmission module, configured to transmit navigation path data to the third intelligent agent through a second intelligent agent; and a signal transmission module, configured to send an alarm signal carrying navigation path data to a preset receiving end in response to the third intelligent agent detecting the existence of an available communication channel, using the communication method corresponding to the available communication channel.

[0153] In an exemplary embodiment, the signal sending module is specifically used to: send an alarm signal carrying preset data and navigation path data to a preset receiving end through a third intelligent agent using a communication method corresponding to an available communication channel; wherein, the preset data includes environmental description information and / or user information of the user of the first terminal.

[0154] The modules in the aforementioned navigation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0155] In one exemplary embodiment, the navigation device described above may be a chip or a chip module.

[0156] Regarding the modules / units included in the navigation devices described in the above embodiments, they can be software modules / units, hardware modules / units, or a combination of both. For example, for navigation devices applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs running on a processor integrated within the chip, while the remaining modules / units can be implemented using hardware methods such as circuits. For navigation devices applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules... Each module / unit can be implemented using software programs that run on a processor integrated within the chip module. The remaining modules / units can be implemented using hardware methods such as circuits. For each navigation device applied to or integrated into the terminal, each of its modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or in different components within the terminal. Alternatively, at least some modules / units can be implemented using software programs that run on a processor integrated within the terminal, while the remaining modules / units can be implemented using hardware methods such as circuits.

[0157] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a navigation method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0158] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the various method embodiments of the navigation method described above.

[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the various method embodiments of the navigation method described above.

[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the various method embodiments of the navigation method described above.

[0162] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0164] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A navigation method characterized by, The method is applied to a first terminal, the first terminal is configured with a first agent and a second agent, and the method comprises: In response to detecting a navigation starting signal by the first agent, triggering the second agent to start by the first agent; Planning a path by the second agent according to positioning perception data collected by a positioning perception device to obtain navigation path data; Outputting the navigation path data.

2. The method of claim 1, wherein, The method comprises: In response to the first agent detecting that the target signal interruption duration of the first terminal reaches a preset duration, determining that the navigation starting signal is detected by the first agent; wherein the target signal comprises a cellular network signal and / or a location positioning signal; or In response to the first agent detecting a navigation starting instruction of a user of the first terminal, determining that the navigation starting signal is detected by the first agent.

3. The method of claim 1, wherein, The method comprises: Planning a path by the second agent according to positioning perception data collected by a positioning perception device to obtain initial path data; Correcting the initial path data according to preset offline map data by the second agent to obtain navigation path data.

4. The method of claim 1, wherein, The method comprises: Transmitting historical positioning perception data collected by a positioning perception device to the second agent by the first agent; Planning a path according to the historical positioning perception data and real-time positioning perception data collected by the positioning perception device by the second agent to obtain navigation path data; wherein the navigation path data comprises navigation recommendation path data and historical backtracking path data.

5. The method of claim 4, wherein, The positioning perception device comprises multiple types of positioning perception devices. The method comprises: Extracting data features of real-time positioning perception data and historical positioning perception data collected by the same type of positioning perception device by the second agent to obtain positioning feature data of each type of positioning perception device; Assigning positioning weights to the positioning feature data of different types of positioning perception devices according to the state information of the first terminal by the second agent; Planning a path according to the positioning feature data of each type of positioning device and the corresponding positioning weight by the second agent to obtain navigation path data.

6. The method of claim 1, wherein, The positioning perception device comprises a positioning perception device of the first terminal and a visual sensor of a second terminal; the method further comprises: In response to detecting a navigation starting signal by the first agent, triggering the second terminal to start the visual sensor by the first agent.

7. The method of claim 6, wherein, The method comprises: The second terminal receives the navigation path data, and outputs the navigation path data.

8. The method according to any one of claims 1 to 7, characterized in that, The first terminal is further configured with a third agent, and the method further comprises: In response to detecting a navigation start signal through the first agent, triggering the third agent to start through the first agent; Transmitting the navigation path data to the third agent through the second agent; In response to the third agent detecting that there is an available communication channel, transmitting an alarm signal carrying the navigation path data to a preset receiving end through the third agent using a communication mode corresponding to the available communication channel.

9. The method of claim 8, wherein, The third agent uses a communication mode corresponding to the available communication channel to transmit an alarm signal carrying the navigation path data to a preset receiving end, comprising: The third agent uses a communication mode corresponding to the available communication channel to transmit an alarm signal carrying preset data and the navigation path data to a preset receiving end; wherein the preset data includes environmental description information and / or user information of a user of the first terminal.

10. A navigation device characterized by The first terminal is configured with a first agent and a second agent, and the device comprises: A start module for triggering the second agent to start through the first agent in response to detecting a navigation start signal through the first agent; A path planning module for performing path planning through the second agent according to positioning perception data collected by a positioning perception device to obtain navigation path data; A path output module for outputting the navigation path data.

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