A wake-up test method and system

Abstract

Embodiments of the present application provide a wake-up test method and system applied to a distributed voice wake-up test scene. The method determines a first detection path corresponding to a first device by using a first algorithm based on basic parameter information and a first condition in the distributed voice wake-up test scene, and obtains wake-up test information for performing wake-up tests on at least two second devices on the first detection path, the wake-up test information being used to determine a wake-up test result. The method can effectively reduce the probability of false wake-up, false detection and false recording while ensuring communication efficiency.

Description

Technical Field

[0001] This application relates to the field of quality inspection, and more specifically, to a wake-up test method and system. Background Technology

[0002] In the field of smart home appliance quality testing, quality testing is a crucial step in ensuring the performance and user experience of smart home appliances. The voice wake-up success rate is a very important product / technical parameter, and an accurate voice wake-up success rate is of great significance and value for appliance manufacturers in evaluating product performance. Currently, most smart home appliance manufacturers conduct voice wake-up success rate tests manually. However, this manual quality inspection method cannot maintain a high accuracy rate for repeated testing over a long period, increasing the probability of false wake-ups, misdetections, and incorrect recordings, thus failing to meet the current needs of smart home appliance quality testing. Summary of the Invention

[0003] This application provides a wake-up testing method and system to improve the success rate of voice wake-up.

[0004] To solve the aforementioned technical problems and achieve the aforementioned objectives, this application provides the following technical solutions:

[0005] Firstly, this application provides a wake-up testing method. This method can be applied to electronic devices in a distributed voice wake-up testing scenario. Unless otherwise specified, the "electronic device" in this application can refer to the electronic device itself, a component within the electronic device (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the electronic device. The method includes: acquiring basic parameter information in the distributed voice wake-up testing scenario; determining a first detection path corresponding to a first device using a first algorithm based on the basic parameter information and a first condition, wherein the first detection path is associated with N test locations, where N is an integer greater than or equal to 2, and the first condition is determined based on test requirements; acquiring wake-up test information, which is obtained by performing wake-up tests on at least two second devices on the first detection path, and the wake-up test information is used to determine the wake-up test result.

[0006] The electronic device in this application can be a robot or other device with detection capabilities. Therefore, compared with manual quality inspection, the solution adopted in this application can achieve automated quality inspection of the wake-up success rate of smart home appliances in a long-term, efficient, and uninterrupted manner. Quality inspectors do not need to work under high workloads for extended periods, reducing labor costs. Furthermore, the automated recording of wake-up success rates minimizes the probability of false detections and misrecordings. In addition, since this application introduces a scheme that uses different algorithms to generate detection paths based on different test scenarios, it can conduct tests in different scenarios according to different test requirements, minimizing the probability of false wake-up rates, false detections, and misrecordings while keeping the detection path as short as possible. On the other hand, the technical solution provided in this application can be further applied to wake-up testing scenarios for dynamic, mobile home appliances, and can optimize the detection path in a timely manner according to the real-time location changes of dynamic home appliances. Compared with distributed smart home appliance testing methods in static scenarios, it has wider applicability and higher efficiency.

[0007] In conjunction with the first aspect, in one possible implementation of the first aspect, the basic parameter information includes the location information of all devices within the first test environment. The first test environment is used as a test environment for static broad testing of static devices in a distributed voice wake-up test scenario or as a test environment for static fine testing of static devices in a distributed voice wake-up test scenario. All devices within the first test environment use the same test environment map.

[0008] In conjunction with the first aspect, in another possible implementation of the first aspect, the first condition includes one or more of the following: the number of steps, step size, and direction change probability set according to the testing requirements; or the access order of the N test locations; or the minimization of the distance of the first detection path.

[0009] In conjunction with the first aspect, in another possible implementation of the first aspect, the first algorithm is a random walk algorithm, and the step of determining the first detection path corresponding to the first device based on the basic parameter information and the first condition using the first algorithm includes: determining the first detection path corresponding to the first device using the random walk algorithm based on the basic parameter information and the first condition, wherein the first detection path passes through the N test locations.

[0010] In distributed voice wake-up testing, the number of steps, step size, and direction change probability of the random walk algorithm need to be set according to factors such as the size of the test area, device distribution, and voice wake-up range. By dynamically adjusting the parameters, a comprehensive test path that meets the actual test requirements can be generated, improving the comprehensiveness of the test. The algorithm is easy to implement, has low computational complexity, and is suitable for the testing needs of large-scale distributed systems. Compared with manual detection, which relies on manual operation, it is less time-consuming and more efficient. Since the test path can cover the entire voice wake-up test range of the second device, it can avoid missed detections and make the test results more reliable, thereby achieving the goal of effectively evaluating the performance of the voice wake-up system.

[0011] In conjunction with the first aspect, in another possible implementation of the first aspect, the first condition includes one or more of the following: the N test locations determined according to test requirements; or, the access order of the N test locations; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each of the N test locations and at least one noise simulation device is less than or equal to a second value; or, each of the at least two second devices is at the same distance from the nearest test location; or, the distance of the first detection path is minimized.

[0012] In conjunction with the first aspect, in another possible implementation of the first aspect, the first algorithm is a simulated annealing algorithm. The step of determining the first detection path corresponding to the first device by using the first algorithm based on the basic parameter information and the first condition includes: determining the first detection path corresponding to the first device by using the simulated annealing algorithm based on the basic parameter information and the first condition, wherein the first detection path passes through the N test positions.

[0013] In static precision testing scenarios, the simulated annealing algorithm employs a global search, dynamic adjustment, and stepwise optimization approach. Furthermore, the first condition can be flexibly defined according to testing requirements to adapt to different testing objectives. Compared to manual quality inspection, which is prone to getting stuck in local optima and failing to fully cover complex networks, the simulated annealing algorithm can escape local optima and find globally optimal or near-global optimal test paths. It also generates test paths quickly, making it suitable for large-scale distributed voice wake-up testing systems. It has low labor costs and is not affected by uncertainties such as the experience and condition of quality inspectors. It effectively meets the testing requirements, node coverage requirements, environment simulation requirements, and performance optimization requirements in distributed voice wake-up detection paths, ensuring a more comprehensive, efficient, and adaptable testing process.

[0014] In conjunction with the first aspect, in another possible implementation of the first aspect, the basic parameter information includes the dynamic location information of all devices in the second test environment. The second test environment is a test environment used to test dynamic devices in a distributed voice wake-up test scenario. All devices in the second test environment use the same test environment map and update their location information in real time.

[0015] In conjunction with the first aspect, in another possible implementation of the first aspect, the first condition includes one or more of the following: prioritizing the selection of points closer to the N test locations based on testing requirements; or, the access order of the N test locations; or, the priority of path node access when path costs are the same.

[0016] In conjunction with the first aspect, in another possible implementation of the first aspect, the first algorithm is an A* path planning algorithm. The step of determining the first detection path corresponding to the first device using the first algorithm based on the basic parameter information and the first condition includes: determining the first detection path corresponding to the first device using the A* path planning algorithm according to the basic parameter information and the first condition, wherein the first detection path is related to the N test positions. Based on the basic parameter information and the second condition, the A* path planning algorithm is used to determine the detection path corresponding to each of the at least two second devices, and the detection path corresponding to each of the at least two second devices is indicated to each of the at least two second devices. The detection path corresponding to each second device passes through the N test positions, and the detection path corresponding to each second device is used by each second device to perform wake-up tests at the N test positions on its corresponding detection path.

[0017] In conjunction with the first aspect, in another possible implementation of the first aspect, the second condition includes one or more of the following: it is necessary to preferentially select points that pass through the N test locations; or, the detection paths corresponding to each of the at least two second devices do not overlap at the N test locations; or, the access order of the N test locations; or, the priority of path node access when the path costs are the same.

[0018] In dynamic testing scenarios, the A* path planning algorithm is used to generate detection paths, which can quickly realize real-time path planning and obstacle avoidance. It is suitable for large-scale distributed systems. Compared with manual detection, it is less time-consuming, more efficient, and less costly. Moreover, the A* algorithm covers a wide area of ​​the network through heuristic search, which can avoid missed detections and is not affected by subjective factors (the state and experience of the inspector). Through global search, dynamic adjustment, and step-by-step optimization, it can find high-quality test paths in complex networks.

[0019] In conjunction with the first aspect, in another possible implementation of the first aspect, the method further includes: optimizing a first detection path corresponding to a first device using an A* path planning algorithm based on second basic parameter information and the first condition, determining a second detection path corresponding to the first device, wherein the second basic parameter information includes dynamic position information of devices whose positions change within the second test environment; and / or optimizing a detection path corresponding to at least one second device using an A* path planning algorithm based on the second basic parameter information and the second condition, determining a third detection path corresponding to each of the at least one second device, and indicating the corresponding third detection path to each of the at least one second device, wherein the second basic parameter information includes dynamic position information of devices whose positions change within the second test environment.

[0020] By employing the above method, the path can be updated in real time to adapt to environmental changes (such as the movement or addition of obstacles), ensuring that the detection path is always optimal. Furthermore, by optimizing the path in real time, the A* algorithm can quickly adjust the path, reducing unnecessary movements, improving the execution efficiency of distributed voice wake-up testing tasks, and saving time and energy.

[0021] In conjunction with the first aspect, in another possible implementation of the first aspect, the acquisition of wake-up test information includes: acquiring wake-up test information from at least one visual signal processing device within the first test environment, and acquiring wake-up test information from at least one acoustic processing device within the first test environment.

[0022] In conjunction with the first aspect, in another possible implementation of the first aspect, the acquisition of wake-up test information includes: acquiring wake-up test information determined by the first device on the first detection path within the second test environment, and wake-up test information determined by each of the at least two second devices on its corresponding test path.

[0023] In conjunction with the first aspect, in another possible implementation of the first aspect, the method further includes: acquiring first wake-up test information, the first wake-up test information including wake-up test information acquired by the first device on the second detection path within the second test environment and wake-up test information acquired by each of the at least one second device on its corresponding third detection path.

[0024] Secondly, this application provides a distributed voice wake-up testing system applicable to static precision testing scenarios and static broad-based testing scenarios. It includes a first device using the same test environment map, at least one noise simulation device, at least one visual signal processing device, at least one acoustic signal processing device, and at least two second devices, wherein...

[0025] The first device is used to acquire basic parameter information in the distributed voice wake-up test scenario. The basic parameter information includes the location information of all devices in the first test environment. Based on the basic parameter information and the first condition, a first algorithm is used to determine the first detection path corresponding to the first device. The first detection path is related to N test locations, where N is an integer greater than or equal to 2. The first condition is determined based on test requirements.

[0026] The at least one noise simulation device is used to simulate different noises;

[0027] The at least one visual signal processing device and the at least one acoustic signal processing device are used to acquire wake-up test information obtained by performing wake-up tests on the at least two second devices at the N test locations, and report the wake-up test information to the first device;

[0028] The first device is used to acquire the wake-up test information, which is used to determine the wake-up test result.

[0029] In conjunction with the second aspect, in one possible implementation of the first aspect, the first algorithm is a random walk algorithm, and the first condition includes one or more of the following: the number of steps, step size, and direction change probability set according to the test requirements; or, the access order of the N test locations; or, minimizing the distance of the first detection path.

[0030] In conjunction with the second aspect, in another possible implementation of the first aspect, the first algorithm is a simulated annealing algorithm, and the first condition includes one or more of the following: the N test locations determined according to test requirements; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each test location among the N test locations and at least one noise simulation device is less than or equal to a second value; or, each of the at least two second devices is at the same distance from the nearest test location.

[0031] Thirdly, embodiments of this application provide a distributed voice wake-up testing system applied to dynamic testing scenarios, including a first device using the same test environment map, at least one noise simulation device, and at least two second devices, wherein the test environment map is updated in real time, and the system includes:

[0032] The first device is used to acquire basic parameter information in the distributed voice wake-up test scenario, the basic parameter information including the dynamic position information of all devices in the dynamic test scenario; based on the basic parameter information and a first condition, the first detection path corresponding to the first device is determined by the A* path planning algorithm, the first detection path is related to N test positions, where N is an integer greater than or equal to 2, and wake-up tests are performed on at least two second devices on the first detection path to acquire wake-up test information, wherein the first condition is determined based on test requirements, and the first condition includes one or more of the following: prioritizing the selection of points close to the N test positions based on test requirements, and the access order of the N test positions;

[0033] The first device is configured to determine the detection path corresponding to each of the at least two second devices using the A* path planning algorithm based on the basic parameter information and the second condition, and to indicate the detection path corresponding to each of the at least two second devices to each of the second devices, wherein the detection path corresponding to each second device passes through the N test positions, and to control each second device to perform wake-up test at the N test positions on its corresponding detection path;

[0034] Each of the second devices is used to perform wake-up tests at the N test positions on its corresponding detection path and send wake-up test information to the first device;

[0035] The second condition includes one or more of the following: the detection paths corresponding to each of the at least two second devices do not overlap at the N test locations; the access order of the N test locations; or the access priority of path nodes when the path costs are the same.

[0036] The at least one noise simulation device is used to simulate different noises.

[0037] In conjunction with the third aspect, in another possible implementation of the first aspect, the first device is further configured to optimize the first detection path corresponding to the first device using a first algorithm based on the second basic parameter information and the first condition, determine a second detection path, and the first device performs a wake-up test on the second detection path. The second basic parameter information includes the dynamic position information of devices whose positions change within the second test environment; and / or,

[0038] Based on the second basic parameter information and the second condition, the first algorithm is used to optimize the detection path corresponding to the at least one second device, determine the third detection path corresponding to each of the at least one second device, indicate the corresponding third detection path to each of the at least one second device, and control each of the at least one second device to perform a wake-up test on its corresponding third detection path.

[0039] Each of the at least one second device is configured to perform a wake-up test on the corresponding third detection path and report wake-up test information to the first device.

[0040] Fourthly, embodiments of this application provide an electronic device, including: one or more processors; one or more memories; the one or more memories storing one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the method described in the first aspect or any optional embodiment of the first aspect.

[0041] Fifthly, embodiments of this application provide a communication device including a module for performing the method described in the first aspect or any optional embodiment of the first aspect.

[0042] In a sixth aspect, embodiments of this application provide a computer program product including instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect or any optional implementation thereof.

[0043] In a seventh aspect, embodiments of this application provide a computer storage medium for storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect or any optional embodiment of the first aspect.

[0044] Eighthly, embodiments of this application provide a chip system including a processor for supporting a device in implementing the functions involved in the foregoing aspects, such as transmitting or processing data and / or information involved in the foregoing methods. In one possible design, the chip system further includes a memory for storing program instructions and data necessary for the device. This chip system may be composed of chips or may include chips and other discrete devices.

[0045] In a ninth aspect, embodiments of this application provide a chip including one or more interface circuits and one or more processors; the interface circuits are configured to receive signals from the memory of an electronic device and send signals to the processors, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, it causes the electronic device to perform the communication method in the first aspect or any optional embodiment of the first aspect. Attached Figure Description

[0046] Figure 1 A schematic diagram of a test environment architecture for conducting distributed voice wake-up testing, provided for an embodiment of this application;

[0047] Figure 2 The embodiments of this application are based on Figure 1 The test architecture shown is a flowchart illustrating a distributed voice wake-up testing method.

[0048] Figure 3 This application provides a schematic diagram of a path for determining a first detection path using a random walk algorithm, as shown in an embodiment of the present application.

[0049] Figure 4 This application provides a schematic diagram of a path for determining a first detection path using a simulated annealing algorithm, as shown in the embodiments of the present application.

[0050] Figure 5 A schematic diagram of another test environment architecture for conducting distributed voice wake-up tests, provided as an embodiment of this application;

[0051] Figure 6 The embodiments of this application are based on Figure 5 The test environment shown is a flowchart illustrating a distributed voice wake-up test method.

[0052] Figure 7 A schematic diagram of another test environment architecture for conducting distributed voice wake-up tests, provided as an embodiment of this application;

[0053] Figure 8 The embodiments of this application are based on Figure 7 The test environment shown is a flowchart illustrating a distributed voice wake-up test method.

[0054] Figures 8A to 8F A flowchart illustrating the process of determining the first detection path using the A* path planning algorithm, provided for an embodiment of this application;

[0055] Figures 9A to 9J A schematic diagram illustrating another process for determining the first detection path using the A* path planning algorithm, provided for an embodiment of this application;

[0056] Figure 10 The embodiments of this application are based on Figure 7 The diagram shows the path of the detection path determined by the test environment.

[0057] Figure 11 A schematic diagram of another test environment architecture for conducting distributed voice wake-up tests, provided as an embodiment of this application;

[0058] Figure 12 The embodiments of this application are based on Figure 11 The test environment shown is a flowchart illustrating a distributed voice wake-up test method.

[0059] Figure 13 A schematic diagram of a distributed voice wake-up testing device provided in an embodiment of this application;

[0060] Figure 14 This is a schematic diagram of another distributed voice wake-up test device provided in an embodiment of this application. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the embodiments of this application, "one or more" refers to one, two, or more; "and / or" describes the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0062] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0063] The "multiple" mentioned in the embodiments of this application refers to two or more. It should be noted that in the description of the embodiments of this application, terms such as "first" and "second" are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying order.

[0064] The step numbers in the embodiments of this application do not represent the necessary order of the steps. They are used only for the purpose of distinguishing and describing, and should not be construed as indicating or implying relative importance or order.

[0065] In the embodiments provided in this application, the electronic device can be of various forms, such as a mobile phone, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, an in-vehicle terminal device, a wireless terminal in self-driving, a wireless terminal in remote medical care, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a wearable terminal device, etc. The control terminal may also be referred to as a terminal device, user equipment (UE), access terminal device, in-vehicle terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal device, mobile device, UE terminal device, terminal device, wireless communication device, UE agent, or UE device, etc. The terminal can also be a fixed terminal or a mobile terminal.

[0066] Before introducing the proposed solution, let's first explain the relevant concepts used in this document:

[0067] Static wide-area testing scenarios are used to comprehensively evaluate the performance of devices undergoing voice wake-up testing under different conditions, identify common problems of the tested devices, and optimize device performance based on the test results of static wide-area testing, thereby improving the performance of the tested devices.

[0068] Static precision testing scenarios are used to conduct in-depth testing under specific conditions to discover performance bottlenecks of devices undergoing voice wake-up testing in specific scenarios. Performance evaluation is then performed based on the test results of the voice wake-up test in order to solve specific problems.

[0069] Dynamic test scenarios simulate user behavior in real-world usage scenarios to test the performance of the device under test (DUT) in voice wake-up situations. Compared to static broad-based test scenarios and static fine-grained test scenarios, dynamic test scenarios place greater emphasis on performance under dynamic factors such as device movement and device interaction.

[0070] The random walk algorithm, also known as the random walk algorithm or random walk algorithm, is based on the concept of a movement trajectory consisting of a series of random steps, which describes the process of an object moving randomly in space.

[0071] Simulated annealing is a probabilistic optimization algorithm often used to search for near-global optima in a large solution space. Based on constraints, simulated annealing can plan the optimal path from the starting point to the ending point.

[0072] The A* path planning algorithm is a global path planning algorithm. It finds the shortest path by comprehensively considering the heuristic evaluation function and the actual cost function, searching for nodes in the state space.

[0073] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0074] Currently, in the field of quality inspection for smart devices (such as home appliances), voice wake-up rate is a crucial product / technical parameter. An accurate wake-up rate is of significant importance and value for manufacturers in evaluating the performance of smart devices and in comparing them with similar products from competitors. However, if multiple similar products exist in a given scenario, a single wake word may simultaneously wake up multiple devices, thus reducing the user experience. To better serve users, the concept of "distributed wake-up" has been introduced. Distributed wake-up refers to the process where, in the same scenario, if multiple similar devices exist simultaneously, the user's wake word only wakes up the device closest to the user. However, distributed wake-up has several drawbacks. First, this method relies on manual quality inspection, and it's difficult for quality inspectors to maintain a high accuracy rate for repeated testing over a long period. Second, this method is time-consuming and labor-intensive, requiring a large number of quality inspectors to perform automated quality inspections on various products across production lines. Furthermore, manual quality inspection schemes are difficult to maintain 24 / 7 operation, necessitating a large number of personnel working in shifts to meet the continuous quality inspection needs of the production line.

[0075] This application provides a wake-up testing method applicable to various scenarios of distributed voice wake-up detection. The method involves an electronic device determining a first detection path corresponding to a first device based on basic parameter information and a first condition within the distributed voice wake-up testing scenario, where the first condition is determined based on testing requirements. The electronic device acquires wake-up test information obtained by performing wake-up tests on at least two second devices along the first detection path. This wake-up test information is used to determine the wake-up test result. Using an electronic device for distributed voice wake-up testing can meet the continuous quality inspection requirements of product lines and improve the success rate of voice wake-up, while reducing the probability of false detections and erroneous recordings.

[0076] To ensure the accuracy of voice wake-up testing, a professional testing environment needs to be set up. This environment should have low noise, stable acoustic characteristics, and be able to simulate real-world usage scenarios. The testing environment needs to be under wireless network coverage. Furthermore, the testing environment also needs to have adjustable volume and sound quality to evaluate the voice wake-up effect under different conditions.

[0077] Figure 1 This is a schematic diagram of the first test environment architecture for distributed voice wake-up testing. All devices in the first test environment are equipped with positioning modules and use the same test environment map, which identifies the location information (such as coordinates) of all devices within the test environment. For ease of understanding, this application example uses two second devices (Device 1 and Device 2) for illustration. Figure 1 As shown, in accordance with relevant acoustic standards, noise simulation device 1 is placed vertically 2 meters above the center point of the perpendicular bisector of devices 1 and 2, and noise simulation device 2 is placed vertically 2 meters below the center point of the perpendicular bisector of devices 1 and 2. Visual signal processing device 1 and visual signal processing device 2, and acoustic signal processing device 1 and acoustic signal processing device 2 are respectively arranged near devices 1 and 2. Device 1 and device 2 are of the same type. Figure 1 The layout of the devices in the test environment shown, and the number and types of devices included in the test scenario, are merely examples for understanding the technical solution of this application. This application includes, but is not limited to, the above examples. The following sections will discuss... Figure 1 The equipment shown is described below:

[0078] Visual signal processing device 1 and visual signal processing device 2 are used to capture, analyze and process visual signals. For example, the visual signal processing device can be a camera.

[0079] Acoustic signal processing device 1 and acoustic signal processing device 2 are used to analyze and process acoustic signals. For example, the acoustic signal processing device can be a high-precision microphone or a recording device.

[0080] Noise simulation device 1 and noise simulation device 2 are used to simulate different noises (such as white noise, human voice, and music).

[0081] The first device, responsible for determining the detection path and performing wake-up tests, needs the following capabilities: 1. LiDAR (Light Detection and Ranging) for mapping, navigation, and obstacle avoidance; 2. Motion module for driving the movement of the first device; 3. Industrial computer for core control and communication; 4. Lifting rod to simulate various user voice sources at different heights; 5. Power supply module to provide power to various devices on the first device; 6. Artificial mouth for simulating user voice. For example, the first device can be a robot or other equipment capable of voice wake-up testing.

[0082] If the test operation in the first test environment is controlled by the first device, the first device can also be used to receive visual test information and acoustic test information reported by the visual signal processing device and the acoustic signal processing device, and send the test information to the control platform of the control terminal. The first device can also obtain the location information of each device in the first test environment and mark it in the test environment map to establish an electronic map of the entire closed-loop experimental environment.

[0083] To more precisely differentiate distributed voice wake-up testing scenarios, they can be categorized into dynamic testing, static wide-range testing, and static precision testing scenarios. Dynamic testing refers to distributed voice wake-up testing conducted while the first device and at least two second devices move within a certain range. Static wide-range testing refers to distributed voice wake-up testing conducted by the first device at multiple angles and positions against a stationary second device. Static precision testing refers to distributed voice wake-up testing conducted at several fixed angles and distances against a stationary second device. These three testing scenarios are described below.

[0084] This application provides a wake-up test method, which can be applied to... Figure 1 The test environment is shown. (As shown in the image) Figure 2 As shown, the wake-up test method includes the following steps:

[0085] 200. The first device acquires basic parameter information in the distributed voice wake-up test scenario.

[0086] For example, basic parameter information may include the first test environment (such as...) Figure 1The test environment shown contains the location information of all devices within the test environment. The first test environment is used for static wide-range testing and static fine-range testing. The devices in the first test environment may include a first device, at least two second devices, at least one visual signal processing device and at least one acoustic signal processing device, and at least one noise simulation device.

[0087] For example, Figure 1 After the test environment is successfully set up, the first device acquires the location information (such as coordinate information) of each device in the test environment based on the positioning module on each device. This includes the location information of the first device, the location information of at least two second devices, the location information of at least one visual signal processing device, the location information of at least one acoustic signal processing device, and the location information of at least one noise simulation device. The first device then marks the location information of each device in the test environment on the detection environment map, thus establishing an electronic map of the entire closed-loop experimental environment.

[0088] 201. Based on the basic parameter information and the first condition, the first detection path corresponding to the first device is determined by the first algorithm. The first detection path is related to N test positions, where N is an integer greater than or equal to 2.

[0089] For example, the first condition is determined based on testing requirements, which may include path coverage requirements, node coverage requirements, environment simulation requirements, and performance evaluation requirements. For instance, path coverage requirements may involve randomly selecting paths to ensure the test path covers different nodes (such as test locations) in the distributed voice wake-up test system; node coverage requirements may ensure that each node has a chance to be accessed to verify the success rate of the distributed voice wake-up test; environment simulation requirements may involve introducing noise interference into the detection path to test the robustness of the system in complex environments; and performance evaluation requirements may involve testing the response time under different paths to evaluate the system performance. The above testing requirements are merely examples for understanding the technical solution of this application, and this application includes, but is not limited to, these requirements.

[0090] For example, the first detection path associated with N test locations may include the first detection path needing to pass through N test locations. The test locations simulate the positions of the sound source. The first detection path is the path used for the first device to move within the test environment, covering the entire voice wake-up test range of the second device, ensuring that the wake-up function can be correctly triggered in different locations.

[0091] For example, if distributed voice wake-up testing is required for at least two immovable second devices from multiple angles and locations, a static wide-range testing scenario can be adopted.

[0092] For example, in a static wide-area testing scenario, a distributed voice wake-up method is used to test the voice wake-up of static devices such as Device 1 and Device 2. Device 1 can use a random walk algorithm to determine a first detection path based on a first condition. This first detection path passes through N test locations. The random walk algorithm can be used to simulate the movement path of Device 1 between different locations.

[0093] For example, the first condition may include the number of steps, step size, and direction change probability set according to testing requirements, the access order of the N test locations, and minimizing the distance of the first detection path. The number of steps, step size, and direction change probability are key parameters of the random walk algorithm, directly affecting the coverage of the detection path and the testing effect of the wake-up test. Wherein:

[0094] The number of steps determines the length of the first detection path and the duration of the distributed voice wake-up test. When the test area is large, the number of steps should be higher to ensure full coverage. When the test area is small, the number of steps should be reduced appropriately. When multiple second devices are densely distributed, the number of steps can be reduced appropriately. When multiple second devices are sparsely distributed, the number of steps should be increased. For example, for a medium-sized room (e.g., 10m x 10m), the number of steps can be set to 50-100. For larger rooms or complex scenarios, the number of steps can be set to 100-200.

[0095] The step size determines the distance the first device moves each time and needs to be set according to the test scenario and the distribution of multiple second devices. For example, if the test scenario area is large, the step size can be set to a larger distance to quickly cover the entire area; if the test scenario area is small, the step size can be set to a smaller distance to improve test accuracy. When multiple second devices are densely distributed, the step size can be set to a smaller distance to ensure that the first detection path passes near each second device; similarly, when multiple second devices are sparsely distributed, the step size can be set to a larger distance. The step size can also be dynamically adjusted, for example, based on the distance between the current position and multiple second devices, decreasing the step size when approaching multiple second devices and increasing the step size when moving away from multiple second devices.

[0096] The probability of direction change determines the randomness and coverage of the random walk path. The specific setting of the probability of direction change depends on the distribution of the test target and multiple second devices. For example, if the test target requires full coverage of the test area, the probability of direction change can be set higher to increase the randomness of the path. If the test target requires testing a specific path (such as straight-line movement), the probability of direction change can be set lower. When the multiple second devices are unevenly distributed, the probability of direction change can be dynamically adjusted to guide the path towards areas with a high density of second devices. The probability of direction change can be a fixed value, such as 0.2, meaning there is a 20% probability of changing direction with each movement. Alternatively, a dynamic probability can be used, which adjusts the probability of direction change based on the distance between the current position and the second devices, increasing the probability when closer to the second device and decreasing it when farther away.

[0097] For example, such as Figure 3 As shown, starting from the current position of the first device, the step size and direction change probability are dynamically adjusted based on the current position and the distance to multiple second devices (Device 1 and Device 2). Test position 1 is determined and recorded based on the adjusted step size and direction change probability. Starting from test position 1, the step size and direction change probability are dynamically adjusted based on the distance between test position 1 and the second devices. Test position 2 is determined and recorded based on the adjusted step size and direction change probability. Starting from test position 2, the step size and direction change probability are dynamically adjusted based on the distance between test position 2 and the second devices. Test position 3 is determined and recorded based on the adjusted step size and direction change probability. This process continues until test position 15 is determined and recorded. At this point, the voice wake-up test range of all second devices is covered, completing the generation of the first detection path. The first detection path covers 15 test positions.

[0098] In distributed voice wake-up testing, the number of steps, step size, and direction change probability of the random walk algorithm need to be set according to factors such as the size of the test area, device distribution, and voice wake-up range. By dynamically adjusting the parameters, a comprehensive test path that meets the actual test requirements can be generated, improving the comprehensiveness of the test. The algorithm is easy to implement, has low computational complexity, and is suitable for the testing needs of large-scale distributed systems. Compared with manual detection, which relies on manual operation, it is less time-consuming and more efficient. Since the first detection path determined by the random walk algorithm can cover the entire voice wake-up test range of the second device, it can avoid missed detections and make the test results more reliable, thereby achieving the goal of effectively evaluating the performance of the voice wake-up system.

[0099] For example, if it is necessary to conduct a distributed voice wake-up success rate test on multiple immovable second devices at several fixed angles and distances, a static precision test scenario can be adopted.

[0100] For example, in a static precision testing scenario, a distributed voice wake-up method is used to test the voice wake-up of multiple second devices, such as Device 1 and Device 2. The first algorithm can be the simulated annealing algorithm, which can be used to simulate the movement path of the first device between different locations.

[0101] For example, a distributed voice wake-up method for home appliances can be used to test the voice wake-up of multiple second devices, such as Device 1 and Device 2. The voice wake-up test needs to be conducted at different test locations to verify the voice wake-up success rate of Device 1 and Device 2. Therefore, based on the basic parameter information and the first condition, a simulated annealing algorithm can be used to determine the first detection path corresponding to the first device. The first detection path needs to pass through N test locations.

[0102] For example, the first device may determine a first condition based on actual testing requirements. For instance, the first condition may include one or more of the following: N test locations determined according to testing requirements; or, the access order of the N test locations; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each test location among the N test locations and at least one noise simulation device is less than or equal to a second value; or, the distance of the first detection path is minimized; or, each of the at least two second devices uses the same distance from the nearest test location.

[0103] For example, assuming N=2, meaning two test locations are needed, the location of the first device 20 is the starting position. A target location is randomly generated based on the starting position. For example, test location 1, target location If the first condition is met, then ,in It is a randomly generated movement vector. The energy difference is calculated using the following formula:

[0104] (1)

[0105] in, The energy difference represents the difference from the starting position. Move to target location The energy change. For example, the penalty constraints required for the test can be introduced into formula (2):

[0106] (2)

[0107] in, The base energy value can represent the default energy when constraints are not considered, and is usually used to quantify the basic cost of a path, such as path length; The distance between the current solution and the constraint boundary is used to measure whether the current solution violates the constraint conditions (such as the test location). If the distance to the noise simulation device exceeds the second value, then The value is negative, where k is the penalty weight coefficient, used to adjust the intensity of the penalty for constraint violation. When it is negative, Increase This penalizes solutions that violate the constraints; the larger the value of K, the stronger the penalty for constraint violations.

[0108] Among them, if <0 indicates a new path (from) to If the distance is better, then accept the new solution; if If the value is greater than or equal to 0, it indicates that the new path is worse. Therefore, the new solution is accepted with probability P, and the probability is calculated as shown in formula (3):

[0109] (3)

[0110] in, The energy difference is the result calculated using Formula 1; T is the current temperature, a parameter controlling randomness, with a high initial value that gradually decreases with iteration. When the value is ≥0 (the new solution is worse), the inferior solution is accepted with probability P. The higher the temperature T, the greater the probability of accepting the inferior solution.

[0111] Each iteration starts from the last position of the current path and randomly generates a new target position, for example... Departure, randomly generate new target locations. That is, test position 3. Based on the constraints, the energy difference is calculated using formula (1). The new target position is accepted by the energy function (formula 2) and the probability judgment (formula 3). If accepted, the new target position is added to the path. If rejected, it is regenerated.

[0112] Each time a test location is generated, the temperature T will decrease once according to formula (4). Therefore, the current temperature T will be updated using formula (4) for each calculation:

[0113] (4)

[0114] in, Used to represent the cooling rate, used to control the rate at which the temperature decreases, with a value range between (0,1).

[0115] Through the above iterative process, until two test locations that satisfy the constraints are obtained, such as... and This ultimately accumulates to form the first test path, which includes two test locations. For example... Figure 4The diagram shows the path of the first detection path determined by the simulated annealing algorithm.

[0116] The following example illustrates the process of obtaining the first detection path using the simulated annealing algorithm.

[0117] Assuming the starting position Generate new position If the noise simulation device is located at position (3,0), the first condition is that the distance between the test position and the noise simulation device must be greater than or equal to 2. The Manhattan distance (path length from the starting point to the new location), penalty coefficient k=2, initial temperature T0=100, cooling rate Starting position The distance to the noise simulation device is 3 (satisfying the first condition). (No penalty). ,Right now The distance from the new location (1,0) to the noise simulation device is 2 (satisfying the first condition). (No penalty). Energy difference ,Right now ,because Accept the new location directly. Update temperature. After each iteration, the temperature T is updated according to formula (4), and the temperature gradually decreases, thus reducing the probability of accepting a worse solution.

[0118] Furthermore, in the second iteration, T0=95, the starting position... , New location The distance from the new location (2,0) to the noise simulation device is 1 (violating the first condition). (Negative numbers, imposing penalties). ,

[0119] ,because It also accepts the new location, although violating the first condition, but with lower total energy. Update temperature. The first detection path can be (0,0)->(1,0)->(2,0).

[0120] For example, assuming the current temperature T0 = 95, a new position is randomly generated from (1,0). Assuming If the new solution is worse, further calculation of the acceptance probability is needed. Accept the new position with probability P≈0.9895, update the new path to (0,0)->(1,0)->(2,0), and update the temperature. .

[0121] In static precision testing scenarios, the simulated annealing algorithm employs a global search, dynamic adjustment, and stepwise optimization approach. Furthermore, the first condition can be flexibly defined according to testing requirements to adapt to different testing objectives. Compared to manual quality inspection, which is prone to getting stuck in local optima and failing to fully cover complex networks, the simulated annealing algorithm can escape local optima and find globally optimal or near-global optimal test paths. It also generates test paths quickly, making it suitable for large-scale distributed voice wake-up testing systems. It has low labor costs and is not affected by uncertainties such as the experience and condition of quality inspectors. It effectively meets the testing requirements, node coverage requirements, environment simulation requirements, and performance optimization requirements in distributed voice wake-up detection paths, ensuring a more comprehensive, efficient, and adaptable testing process.

[0122] 202. The first device performs wake-up tests at N test locations on the first detection path.

[0123] For example, the first device moves along a first detection path, performing a voice wake-up test each time it reaches a test position. For instance, as... Figure 3 As shown, in a static wide-area testing scenario, the first device moves to test position 1. First, the distances d1 and d2 between test position 1 and device 1 and device 2 are calculated. At the same time, the orientation of the first device and the angle between device 1 and device 2 are calculated. If it is determined that the distance d1 between device 1 and the first device is the shortest, noise simulation device 1 and noise simulation device 2 are used to play speech data. Test data is obtained through visual signal processing device 1 and visual signal processing device 2, acoustic signal processing device 1 and acoustic signal processing device 2, and logs, etc. The test data is sent to the control platform. The control platform comprehensively determines the wake-up status of device 1 and device 2 based on the obtained test data. If only device 1 is woken up, the voice wake-up success rate result is recorded as "success". Otherwise, all other wake-up results are recorded as "failure". The control platform completes the test result recording for test position 1.

[0124] Assuming that the distance d1 between test position 1 and the first device and the distance d2 between test position 1 and device 2 are the same and the shortest, a further judgment principle of "same distance, based on orientation" needs to be adopted. That is, the angle A between the orientation of the first device and device 1, and the angle B between the orientation of the first device and device 2 need to be determined. For example, if the angle A between device 1 and the first device is determined to be the smallest, if only device 1 is woken up during the wake-up test, the voice wake-up success rate result is recorded as "success", and all other wake-up results are recorded as "failure". The test results are uploaded to the control terminal to complete the test result recording for test position 1.

[0125] Following the above operational logic, complete the testing tasks for all test locations (or perform multiple rounds of iterative testing according to testing requirements).

[0126] 203. The first device acquires wake-up test information.

[0127] For example, the wake-up test information is obtained by the first device performing wake-up tests on at least two second devices at the N test locations, and the wake-up test information is used to determine the wake-up test results.

[0128] For example, wake-up test information can be obtained by the following method: the first device receives wake-up test information from at least one visual signal processing device and at least one acoustic processing device within the first test environment.

[0129] For example, the first device receives visual information data generated by visual signal processing device 1 and visual signal processing device 2, and receives acoustic information data generated by acoustic signal processing device 1 and acoustic signal processing device 2. After completing the test operation at N target locations, it reports the obtained test information to the control terminal, which generates a test report. The test report details the distributed wake-up test experiment of device 1 and device 2 and the results of the distributed voice wake-up test.

[0130] The above scheme uses a random walk algorithm and a simulated annealing algorithm to determine the first detection path, enabling the first device to perform wake-up tests at N target positions along this path. This reduces manual labor costs and automates the recording of wake-up success rates, minimizing false detections and misrecordings. Furthermore, by introducing a scheme that uses different algorithms to generate detection paths for different test scenarios, it allows for scenario-based testing to meet various testing needs, adapting to distributed voice wake-up test scenarios of different scales, thereby improving detection efficiency.

[0131] This application provides another wake-up test method, which can be applied to... Figure 5 The test environment shown. Figure 5 The test environment shown is Figure 1 The test environment shown is similar and can be applied to both static broad testing and static fine testing scenarios. The difference lies in... Figure 5 The test environment shown also includes a control terminal to help operators manage, control, and record information about various experimental devices in the test scenario. In this scenario, the control terminal controls the first device to perform a wake-up test. It should be noted that... Figure 5 The layout of the equipment in the test environment shown, as well as the number and types of equipment included in the test scenario, are merely examples for understanding the technical solution of this application. This application includes, but is not limited to, the examples mentioned above.

[0132] Figure 6 The embodiments of this application are based on Figure 5 The test environment shown is a flowchart illustrating a distributed voice wake-up testing method. Figure 6 As shown, the wake-up test method includes the following steps:

[0133] 601. The control terminal obtains basic parameter information in the distributed voice wake-up test scenario.

[0134] For example, basic parameter information may include the first test environment (such as...) Figure 5 The test environment shown contains the location information of all devices within it. The first test environment is used for static wide-area testing and static fine-scale testing. Devices within the first test environment may include a control terminal, a first device, at least two second devices, at least one visual signal processing device and at least one acoustic signal processing device, and at least one noise simulation device. For specific methods of obtaining basic parameter information, please refer to [reference needed]. Figure 2 The method by which the first device obtains basic parameter information in the example shown will not be described again here.

[0135] 602. Based on basic parameter information and a first condition, the control terminal uses a first algorithm to determine the first detection path corresponding to the first device, and instructs the first detection path to the first device. The first detection path is related to N test positions, where N is an integer greater than or equal to 2. The first condition is determined based on test requirements.

[0136] For example, if distributed voice wake-up testing of multiple immovable second devices from multiple angles and locations is required, a static wide-range testing scenario can be adopted. The first algorithm can be a random walk algorithm. Based on the basic parameter information and the first condition, the control terminal uses a random walk algorithm to determine the first detection path corresponding to the first device. For specific determination methods, please refer to... Figure 2 The illustrated embodiment employs a random walk algorithm to obtain the first detection path corresponding to the first device. The first condition may include the number of steps, step size, and direction change probability set according to testing requirements, the access order of the N test locations, and minimizing the distance of the first detection path. For an explanation of the number of steps, step size, and direction change probability, please refer to... Figure 2 The example shown is described below.

[0137] For example, if it is necessary to conduct a distributed voice wake-up success rate test on multiple immovable second devices at several fixed angles and distances, a static precision test scenario can be adopted. The first algorithm can be a simulated annealing algorithm, which can be used to simulate the movement path of the first device between different positions. The first condition can include one or more of the following: N test positions determined according to test requirements; or, the access order of the N test positions; or, the distance between two adjacent test positions in the N test positions is less than or equal to a first value; or, the distance between each test position in the N test positions and at least one noise simulation device is less than or equal to a second value; or, the distance of the first detection path is minimized; or, each of at least two second devices uses the same distance to the nearest test position. The control terminal determines the first detection path corresponding to the first device based on the basic parameter information and the first condition using the simulated annealing algorithm. For specific determination methods, please refer to [reference needed]. Figure 2 The embodiment shown employs a simulated annealing algorithm to determine the first detection path corresponding to the first device.

[0138] 603. The control terminal controls the first device to perform wake-up tests on at least two second devices at N test positions in the first detection path.

[0139] For specific instructions on how to perform wake-up tests, please refer to [link / reference]. Figure 2 The method shown.

[0140] 604. The control terminal receives wake-up test information, determines the wake-up test result based on the wake-up test information, and generates a test report.

[0141] For example, the control terminal receives visual information data generated by visual signal processing device 1 and visual signal processing device 2, and receives acoustic information data generated by acoustic signal processing device 1 and acoustic signal processing device 2. After completing the test work at N test locations, a test report is generated. The test report details the distributed wake-up test experiment of device 1 and device 2 and the results of the distributed voice wake-up test.

[0142] The above scheme uses a random walk algorithm and a simulated annealing algorithm to determine the first detection path. The first device then performs wake-up tests at N test locations along this path. The wake-up test results are determined based on the wake-up test information, and a test report is generated. This reduces manual labor costs and automates the recording of wake-up success rates, minimizing the probability of false detections and misrecordings. Furthermore, by introducing a scheme that uses different algorithms to generate detection paths for different test scenarios, tests can be conducted in different scenarios to meet various testing needs, minimizing the false wake-up rate, false detections, and misrecordings while ensuring the shortest possible detection path. Compared to the method of managing distributed voice wake-up tests by the first device, the control terminal manages distributed voice wake-up tests with longer battery life and can execute test tasks more stably over extended periods.

[0143] Figure 7 This is a second detection environment built for performing distributed voice wake-up. Figure 7 The detection environment shown can detect multiple dynamic second devices, and this application does not limit the number of such second devices. For ease of understanding, an example of two second devices (device number three and device number four) will be used. Figure 7 As shown, the testing environment includes noise simulation device 1, noise simulation device 2, device 1, device 3, and device 4. For descriptions of the noise simulation devices, device 1, and device 2, please refer to [link to documentation]. Figure 1 The example shown will not be repeated here. Device 3 and Device 4 in the testing environment are of the same type and have remote log transmission capabilities, which remain enabled during the test. Device 1, Device 3, and Device 4 all have obstacle avoidance capabilities, so no collisions will occur during actual movement. Figure 7 The layout of the devices in the test environment shown, as well as the number and types of devices included in the test scenario, are merely examples for understanding the technical solution of this application. This application includes, but is not limited to, the examples above.

[0144] To more accurately determine the success rate of voice wake-up testing, at least two second devices (e.g., Device 3 and Device 4) need to use the same electronic map. Each second device must have a positioning device so that the first device can obtain the real-time coordinates of all second devices in the test environment, facilitating positional calculations and path planning. Since the second devices are dynamic and their positions are not fixed, fixed cameras and microphones cannot be used for data collection. Consequently, the second devices cannot use image and acoustic information to determine whether they have been voice-wake-up. To more accurately test the voice wake-up success rate of the second devices, a remote Android debug bridge (ADB) needs to be introduced. Log information from the second devices can be obtained via remote ADB and sent to the first device. It should be noted that second devices developed based on the Android system can directly enable the remote ADB function. The device under test (e.g., the second device) can send its log data to the control terminal by adding wireless network functionality via a wireless network USB adapter (WiFi-USB).

[0145] Figure 8 The embodiments of this application are based on Figure 7 The test environment shown is a flowchart illustrating a distributed voice wake-up testing method. Figure 8 As shown, it includes:

[0146] 800. The first device acquires basic information in the distributed voice wake-up test scenario.

[0147] For example, the basic parameter information may include the real-time location information of all devices within the second test environment, which is a test environment used to test at least two dynamic second devices, such as... Figure 7 The test environment is shown. Basic parameter information may include the real-time location information of the first device, the real-time location information of at least two second devices, and the real-time location information of at least one noise simulation device.

[0148] For example, Figure 7 After the test environment is successfully set up, the first device acquires the real-time location information (such as coordinate information) of each device in the test environment based on the positioning module on each device. The real-time location information can change dynamically as the device moves, so it can also be called dynamic location information, such as the dynamic location information of the first device, the dynamic location information of at least two second devices, and the dynamic location information of at least one noise simulation device. The first device marks the acquired dynamic location information of each device in the test environment on the test environment map.

[0149] 801. Based on basic parameter information and a first condition, the first device uses the A* path planning algorithm to determine the first detection path corresponding to the first device. The first detection path is related to N test positions, where N is an integer greater than or equal to 2.

[0150] For example, the first condition may include one or more of the following: the order in which test locations are accessed, the priority of access when path costs are the same, or the first detection path preferentially selecting points closer to the test locations. The priority of access when path costs are the same could be, for example, moving the test location laterally to a point of the third value or vertically to a point of the fourth value when path costs are the same. For instance, assuming N=4 (i.e., 4 test locations), the order in which test locations are accessed could be: test location 1, test location 2, test location 3, and test location 4. The first detection path preferentially selects points closer to the test locations, such as points within a radius of the fourth value centered on the test location, or points with a straight-line distance of the fifth value from the test location.

[0151] For example, the first detection path corresponding to the first device can be determined in the following manner:

[0152] like Figure 8A As shown, a grid map model is constructed based on the obstacle situation in the distributed voice wake-up test scenario, and the starting node of the first path is determined based on the grid map model. That is, the location of the first device and the target node. That is, the first position, which is a point that meets the test requirements and corresponds to test position 1, and all obstacles (noise simulation equipment and multiple movable second devices) are marked in the grid map model.

[0153] 1. The first detection path is determined using the A* path planning algorithm in the following manner.

[0154] First, create an Open table and a Close table for the A* path planning algorithm. Place the starting node of the first detection path into the Close table, and place nodes adjacent to the starting node of the first detection path that do not overlap with obstacles into the Open table. Calculate the path cost of the starting node and store it in the path cost table. Sort all nodes in the Open table according to their path costs, and select the node with the lowest cost as the next parent node, storing it in the Close table. Based on the path cost table, sequentially find the node with the lowest path cost from the starting node to the target node. The Close table stores nodes that have already been expanded as parent nodes, and the Open table stores the node from which the next parent node will be selected.

[0155] The path cost of the starting node is calculated using the cost function f(x) = g(x) + h(x), where f(x) is the cost estimate from the starting node to the target node, g(x) is the actual cost from the starting node to intermediate nodes, and h(x) is the estimated distance from intermediate nodes to the target node. Figure 8B As shown, the gray areas at the top and right represent the lowest-cost paths. The priority of path node access can be constrained by the first condition when the path costs are the same. For example, the first condition could be to prioritize horizontal movement (selecting the gray area on the right) or vertical movement (selecting the gray area at the top) when the path costs are the same.

[0156] Assuming a horizontal movement of one grid is 10 units and a diagonal movement of one grid is 14 units, the path cost of the starting node is calculated as follows: Figure 8C As shown, traversing the starting point That is, the eight nodes in the neighborhood of the grid where the first device 20 is located (shown as dark gray grids in the figure), and the path cost of each node in the neighborhood is calculated using a cost function (already marked in the grid), and the node with the minimum traversal path cost (14+70=84) is added to the Close table as its parent node and marked in black, as shown below. Figure 8D As shown.

[0157] Traverse the neighborhood nodes of the point with the minimum path cost (14+70=84), and calculate the path cost of each node in the neighborhood using the cost function. Add the point with the minimum path cost (24+60=84) as its parent node to the `close` list and mark it in black. Figure 8E As shown.

[0158] Each iteration follows this principle until the target node is reached. ,like Figure 8F The black area shown represents the first detection path of the first device 20 in the distributed voice wake-up test scenario.

[0159] For example, during the detection process, due to the presence of obstacles, there is no point with the lowest path cost in the neighborhood of the current point with the lowest path cost listed in open table 1. Therefore, the first detection path can be determined as follows:

[0160] like Figure 9 As shown, a grid map model is constructed based on the obstacle situation in the distributed voice wake-up test scenario, and the starting node of the first path is determined based on the grid map model. That is, the location of the first device and the target node. and obstacles.

[0161] Create an Open table 1 and a Close table 1 for the A* path planning algorithm. Place the starting node of the first detected path into the Close table 1, and place nodes adjacent to the starting node of the first detected path that do not overlap with obstacles into the Open table 1. Calculate the path cost of the starting node and store it in the path cost table 1. Sort all nodes in the Open table 1 according to their path costs, and select the node with the lowest path cost as the next parent node, storing it in the Close table 1. Based on the path cost table, sequentially search for nodes starting from the starting node (x... s ,y s Reaching the target node The node with the lowest path cost in the process. The Close table 1 stores nodes that have already been expanded as parent nodes, and the Open table 1 stores the node from which the next parent node will be selected.

[0162] 1. Traverse the starting node The system considers five neighboring nodes and calculates the path cost for each node in the neighborhood using a cost function (already written in each grid cell). In this round of traversal, the node with the lowest path cost is 10 + 130 = 144. This node is added to the close table 1 and marked in black. Figure 9A As shown.

[0163] 2. Traversal Figure 9A The neighborhood of the point with the minimum path cost (10+130=144) is also calculated using a cost function, with the path cost of each point in the neighborhood calculated similarly. Figure 9B As shown, the point with the lowest path cost in this round is 28 + 110 = 138. This point is placed in the close table 1 and marked in black.

[0164] 3. Each round of traversal follows this principle, and unless there are special cases, it will not be elaborated further. The results of several rounds of traversal are given below, such as... Figures 9C to 9E As shown.

[0165] 4. Figure 9E The point with the lowest path cost in the previous round selected is 62 + 70 = 132. However, after traversing the neighborhood of this point, it is found that there is no point with the lowest path cost in open table 1 in this neighborhood. Therefore, the point with the lowest cost selected from open table 1 is no longer the neighborhood of the point 62 + 70 = 132 (each round selects the smallest point from open table 1 and puts it into closed table 1, but the points with the lowest cost in the previous rounds just happen to be in the neighborhood of the point with the lowest cost in the previous round). Figure 9FAs shown, the minimum point selected from open table 1 in this round is 38 + 100 = 138. At this point, two minimum points (38 + 100) appear. Based on the first condition, the traversal order can be set in the algorithm (prioritizing the node traversed first). Here, we assume that the distance to each neighbor is calculated clockwise from the rightmost neighbor of this neighbor. Then, the point directly above 28 + 110 = 138 is the minimum node traversed later. To easily compare the difference between these two points, both points can be selected simultaneously and added to closed table 1, and their neighbors can be calculated simultaneously. This round of traversal updates the path cost in open table 1. The point (48 + 90) is the point whose distance in this round is less than the distance in the previous round, and the data is updated. After the update, the parent node of the point will also change to the minimum point from the previous round.

[0166] 5. Select the point with the minimum distance cost from the openlis after the previous traversal. This point is exactly the point updated in the previous round, which is 48 + 90 = 138. Figure 9G As shown.

[0167] 6. For example Figure 9H As shown, the point with the lowest cost in the previous round was 58 + 80 = 138. However, after traversing the neighborhood of this point, it was found that there was no point with the lowest cost in open table 1 within this neighborhood. Therefore, the point with the lowest cost selected from open table 1 is no longer the neighborhood of the point 58 + 80 = 138, but rather the point with the lowest cost distance in the entire open table 1, 24 + 120 = 144. In this round, I still selected two points (24 + 120). These two points are added to close table 1, and the neighborhoods of the two points are calculated simultaneously. Here, it is assumed that the distance of each neighborhood is calculated clockwise from the rightmost neighborhood of this point. The point with the lowest cost distance is traversed, and the path cost in open table 1 is updated, and the data is updated accordingly. The determination method is as described above and will not be repeated here.

[0168] 7. After traversal, the target node is finally found, such as... Figure 9I As shown. Based on the records of the parent node, the shortest path was found by backtracking from the target point to the starting point, as follows. Figure 9J As shown.

[0169] Starting node That is, starting from the first position 1, the target node is That is, taking the first position 2 as the target node, the detection path from the first position 1 to the first position 2 is obtained using the same method described above. Taking the starting node as... That is, starting from position 2, the target node is That is, taking the first position 3 as the target node, the detection path from the first position 2 to the first position 3 is obtained using the same method described above. Taking the starting node as... That is, starting from the first position 3, the target node is Taking the first position 4 as the target node, the detection path from the first position 3 to the first position 4 is obtained using the same method described above. Thus, the first detection path corresponding to the first detection device is obtained. This first detection path needs to pass through the first position 1 corresponding to the first test position, the first position 2 corresponding to the second test position, the first position 3 corresponding to the third test position, and the first position 4 corresponding to the fourth test position.

[0170] 802. The first device, based on the location information of the at least two second devices and the second condition, uses the A* path planning algorithm to determine the detection path corresponding to each of the at least two second devices. The detection path corresponding to each second device includes the N target locations. The first device then indicates the corresponding detection path to each of the at least two second devices.

[0171] For example, the second condition may include one or more of the following: the points that cover the test locations need to be selected preferentially (e.g., the detection path corresponding to each second device must pass through N test locations), the detection paths corresponding to each of at least two second devices intersect but do not overlap at the N test locations, and the access order of the N test locations (e.g., accessing them in the order of test location 1, test location 2, test location 3 and test location 4).

[0172] For example, at least two second devices may include device number three and device number four. The first device determines the detection paths corresponding to device number three and device number four respectively. For specific determination methods, please refer to the introduction of step 801.

[0173] It is important to note that when using the A* path planning algorithm to determine the detection paths for at least two second devices, path intersections must be avoided. Therefore, during device movement, the positional changes of other devices must be monitored in real time, and paths adjusted accordingly. If conflicts are unavoidable, dynamic adjustments should be made based on pre-defined priorities (e.g., device three waits). Alternatively, a spatiotemporal A* path planning method can be used, where device three avoids the time window in which device four occupies the same area. Furthermore, based on the first detection path generated by the A* path planning algorithm, the speed required for obstacle avoidance can be calculated and adjusted according to the real-time speed and direction of other devices.

[0174] 803. The first device receives wake-up test information from each of at least two second devices.

[0175] 804. The first device reports wake-up test information to the control terminal device.

[0176] further, Figure 8 The illustrated embodiments may also include the following steps 805 and / or 806:

[0177] 805. Based on the second basic parameter information and the first condition, the first detection path corresponding to the first device is optimized using the A* path planning algorithm, the second detection path is determined, and the wake-up test information determined by the first device on the second detection path is obtained.

[0178] 806. Based on the second basic parameter information and the second condition, the corresponding detection path for at least one second device is optimized using the A* path planning algorithm, the third detection path for each of the at least one second device is determined, the corresponding third detection path is indicated to each of the at least one second device, and the wake-up test information determined by each of the at least one second device on its corresponding third detection path is obtained.

[0179] For example, the second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0180] For example, the methods for optimizing the first detection path corresponding to the first device and optimizing the detection path corresponding to at least one second device in steps 805 and 806 can refer to the method shown in step 801, and will not be repeated here.

[0181] It should be noted that steps 801 and 802 can be executed in any order and may be executed simultaneously. Steps 805 and 806 can also be executed in any order and may be executed simultaneously. Steps 805 and 806 can be executed before or after step 804. If they are executed after step 804, the first device also needs to report the updated wake-up test information to the control terminal device.

[0182] like Figure 10 As shown, the first device plays wake-up data at each test position. Devices three and four send their respective detection information to the first device. After completing the test at all test positions, the first device sends the wake-up test information to the control terminal. The test ends, and the first device returns to its original position.

[0183] In dynamic testing scenarios, the A* path planning algorithm is used to generate detection paths, which can quickly realize real-time path planning and obstacle avoidance. It is suitable for large-scale distributed systems. Furthermore, the A* algorithm covers a wide area of ​​the network through heuristic search, which can avoid missed detections and is not affected by subjective factors (the state and experience of the inspector). Through global search, dynamic adjustment and step-by-step optimization, it can find high-quality test paths in complex networks.

[0184] Figure 11 A second detection environment was built for another type of distributed voice wake-up, used in scenarios requiring dynamic testing. Figure 11The detection environment shown is Figure 7 The detection environments shown are similar, the difference being that... Figure 11 The illustrated testing environment also includes a control terminal. The control terminal is used to control and manage the first device, the noise simulation device, and at least two second devices for wake-up testing. In this scenario, the control terminal controls the first device to perform the wake-up test. It should be noted that... Figure 11 The layout of the devices in the test environment shown, as well as the number and types of devices included in the test scenario, are merely examples for understanding the technical solution of this application. This application includes, but is not limited to, the examples above.

[0185] Figure 12 The embodiments of this application are based on Figure 11 The test environment shown is a flowchart illustrating a distributed voice wake-up testing method. Figure 12 As shown, it includes:

[0186] 1200. The control terminal obtains the basic parameter information in the distributed voice wake-up test scenario.

[0187] For example, the basic parameter information includes the dynamic location information of all devices within the second test environment. The second test environment is used to test the dynamic devices, and all devices within the second test environment use the same test environment map and their location information is updated in real time. Specifically, all devices within the second test environment may include a control terminal, a first device, at least two second devices, and at least one noise simulation device. For details on how to obtain this information, please refer to [reference needed]. Figure 8 The method by which the first device obtains basic parameter information in the example shown will not be described again here.

[0188] 1201. The control terminal obtains the basic parameter information and the first condition, uses the A* path planning algorithm to determine the first detection path corresponding to the first device, and indicates the first detection path corresponding to the first device to the first device. The first detection path is related to N test positions, where N is an integer greater than or equal to 2. The first condition is determined based on the test requirements.

[0189] For example, the first condition may include one or more of the following: the order in which test locations are accessed, the priority of access when path costs are the same, or the first detection path preferentially selecting points closer to the test locations. The priority of access when path costs are the same could be, for example, moving the test location laterally to a point of the third value or vertically to a point of the fourth value when path costs are the same. For instance, assuming N=4 (i.e., 4 test locations), the order in which test locations are accessed could be: test location 1, test location 2, test location 3, and test location 4. The first detection path preferentially selects points closer to the test locations, such as points within a radius of the fourth value centered on the test location, or points with a straight-line distance of the fifth value from the test location.

[0190] For example, the control terminal determines the first detection path corresponding to the first device using the A* path planning algorithm based on basic parameter information and the first condition. For specific determination methods, please refer to [reference needed]. Figure 8 The embodiment shown illustrates a method for obtaining the first detection path corresponding to the first device using the A* path planning algorithm. This will not be elaborated further here.

[0191] 1202. Based on the basic parameter information and the second condition, the control terminal uses the A* path planning algorithm to determine the detection path corresponding to each of the at least two second devices, and indicates the corresponding detection path to each of the at least two second devices. The detection path corresponding to each second device passes through N test positions.

[0192] For example, the second condition may include one or more of the following: prioritizing the selection of points passing through test locations (e.g., the detection path corresponding to each second device must pass through N test locations), ensuring that the detection paths corresponding to each of at least two second devices do not overlap at the N test locations, the access order of the N test locations, and accessing them according to a preset access order (e.g., accessing them in the order of test location 1, test location 2, test location 3, and test location 4). The method by which the control terminal determines the detection paths corresponding to at least two second devices can refer to the method by which the first device determines the first detection path in step 801, and will not be repeated here.

[0193] 1203. The control terminal controls the first device to perform wake-up tests on at least two second devices on the first detection path, and the control terminal controls each second device to perform wake-up tests at N test positions on its corresponding detection path.

[0194] 1204. The control terminal receives wake-up test information from the first device and at least two second devices, determines the wake-up test result based on the wake-up test information, and generates a test report.

[0195] further, Figure 12 The illustrated embodiment may also include the following steps 1205 and / or 1206. The execution order of steps 1205 and 1206 may be before or after step 1204, and there is no particular limitation on this. The execution order of steps 1205 and 1206 is not important, and they may be executed simultaneously.

[0196] 1205. Based on the second basic parameter information and the first condition, the control terminal uses the first algorithm to optimize the first detection path corresponding to the first device, determines the second detection path, and indicates the second detection path corresponding to the first device to the first device. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0197] 1206. Based on the second basic parameter information and the second condition, the control terminal uses the first algorithm to optimize the detection path corresponding to at least one second device, determines the third detection path corresponding to each of the at least one second device, and indicates the corresponding third detection path to each of the at least one second device. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0198] It should be noted that if steps 1205 and 1206 are executed after step 1204, the control terminal device also needs to receive wake-up test information from the first device and at least one second device, determine the wake-up test result based on the wake-up test information, and generate a test report.

[0199] The control terminal controls the first device to play wake-up data on the first detection path. The first device sends wake-up test information to the control terminal. Devices three and four respectively send their respective detection information to the control terminal. Based on the received wake-up test information, the control terminal 10 records the relative positional relationships of the first device and devices three and four, determines the detection results of devices three and four, and completes the testing for all test positions. The control terminal 10 generates a test report, which details the distributed wake-up experiment of devices three and four and their distributed wake-up rate. The test ends, and the first device returns to its original position.

[0200] In dynamic testing scenarios, the A* path planning algorithm is used to generate detection paths. This method is fast, suitable for large-scale distributed systems, and compared to manual detection, it is faster, more efficient, and lower in cost. Furthermore, the A* algorithm covers a wide area of ​​the network through heuristic search, avoiding missed detections and is unaffected by subjective factors (the tester's state and experience). Through global search, dynamic adjustment, and step-by-step optimization, it can find high-quality test paths in complex networks. The first condition can be flexibly defined according to testing needs to adapt to different testing objectives, effectively meeting the testing requirements, node coverage requirements, environment simulation requirements, and performance optimization requirements in distributed voice wake-up detection paths, ensuring a more comprehensive, efficient, and adaptable testing process. Compared to the method of managing distributed voice wake-up tests through a primary device, the control terminal manages distributed voice wake-up tests with longer battery life, allowing for more stable and long-term execution of test tasks.

[0201] This application also provides a distributed voice wake-up testing system, which can be applied to... Figure 1 The test environment shown is used for static precision testing scenarios and static wide-area testing scenarios. It includes a first device using the same test environment map, at least one noise simulation device, at least one visual signal processing device, at least one acoustic signal processing device, and at least two second devices. The test system includes:

[0202] The first device is used to acquire basic parameter information in the distributed voice wake-up test scenario. The basic parameter information includes the location information of all devices in the first test environment. Based on the basic parameter information and the first condition, the first algorithm is used to determine the first detection path corresponding to the first device. The first detection path is related to N test locations, where N is an integer greater than or equal to 2. The first condition is determined based on the test requirements.

[0203] At least one noise simulation device for simulating different noises;

[0204] The first device is used to perform a wake-up test on the first detection path corresponding to the first device;

[0205] At least one visual signal processing device and at least one acoustic signal processing device are used to acquire and report wake-up test information determined by performing a wake-up test in the first detection path;

[0206] The first device is used to receive wake-up test information, which is used to determine the wake-up test result.

[0207] For example, the first condition includes one or more of the following: the number of steps, step size, and direction change probability set according to the test requirements; or the access order of N test locations; or the minimum distance of the first detection path; and the first algorithm is a random walk algorithm.

[0208] For example, the first algorithm is a simulated annealing algorithm, and the first condition includes one or more of the following: N test locations determined according to test requirements; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each test location among the N test locations and at least one noise simulation device is less than or equal to a second value; or each of at least two second devices uses the same distance from the nearest test location.

[0209] For a detailed description and benefits of the system, please refer to [link / reference]. Figure 1 and Figure 2 The embodiments shown will not be described in detail here.

[0210] This application also provides a distributed voice wake-up testing system, which can be applied to... Figure 5 The test environment shown is used for static precision testing scenarios and static wide-area testing scenarios, including a map control terminal using the same test environment, a first device, at least one noise simulation device, at least one visual signal processing device, at least one acoustic signal processing device, and at least two second devices. The distributed voice wake-up test system includes:

[0211] The control terminal is used to acquire basic parameter information in the distributed voice wake-up test scenario. The basic parameter information includes the location information of all devices in the first test environment. Based on the basic parameter information and the first condition, the first algorithm is used to determine the first detection path corresponding to the first device, instruct the first device on the first detection path corresponding to the first device, and control the first device to perform a wake-up test on the first detection path corresponding to the first device. The first detection path is related to N test locations, where N is an integer greater than or equal to 2. The first condition is determined based on the test requirements.

[0212] At least one noise simulation device for simulating different noises;

[0213] At least one visual signal processing device and at least one acoustic signal processing device are used to acquire and report wake-up test information determined by the first device performing a wake-up test on its corresponding first detection path;

[0214] The control terminal is used to receive wake-up test information, determine the wake-up test results based on the wake-up test information, and generate a distributed voice wake-up test report.

[0215] For example, the first algorithm is a random walk algorithm, and the first condition includes one or more of the following: the number of steps, step size, and direction change probability set according to the test requirements; or the access order of N test locations; or minimizing the distance of the first detection path.

[0216] For example, the first algorithm is a simulated annealing algorithm, and the first condition includes one or more of the following: N test locations determined according to test requirements; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each test location among the N test locations and at least one noise simulation device is less than or equal to a second value; or each of at least two second devices uses the same distance from the nearest test location.

[0217] For a detailed description and benefits of the system, please refer to [link / reference]. Figure 5 and Figure 6 The embodiments shown will not be described in detail here.

[0218] This application also provides a distributed voice wake-up testing system, which can be applied to... Figure 7 The test environment shown is a scenario for dynamic testing, including a first device using the same test environment map, at least one noise simulation device, and at least two second devices. The test environment map is updated in real time, including:

[0219] The first device is used to acquire basic parameter information in the distributed voice wake-up test scenario. The basic parameter information includes the dynamic position information of all devices in the dynamic test scenario. Based on the basic parameter information and the first condition, A* path planning is used to determine the first detection path corresponding to the first device. The first detection path is related to N test positions, where N is an integer greater than or equal to 2. Wake-up tests are performed on at least two second devices on the first detection path to acquire wake-up test information. The wake-up test information is used to determine the wake-up test result. The first condition is determined based on test requirements. The first condition includes one or more of the following: prioritizing the selection of points close to the N test positions based on test requirements, the access order of the N test positions, or minimizing the distance of the first detection path.

[0220] The first device is further configured to, based on basic parameter information and a second condition, employ the A* path planning algorithm to determine the detection path corresponding to each of at least two second devices, and to indicate the detection path corresponding to each of the at least two second devices to each of the at least two second devices, wherein the detection path corresponding to each second device passes through N test locations; wherein the second condition includes one or more of the following: the detection paths corresponding to each of the at least two second devices do not overlap at the N test locations, the access order of the N test locations, the priority of path node access under the condition of the same path cost, and minimizing the distance of the detection path corresponding to each second device;

[0221] At least one noise simulation device for simulating different noises.

[0222] Each of at least two second devices is used to perform wake-up tests at N test locations on its corresponding detection path and send wake-up test information to the first device;

[0223] For example, the first device is further configured to optimize the first detection path corresponding to the first device using the A* path planning algorithm based on the second basic parameter information and the first condition, and determine the second detection path. The first device performs a wake-up test on the second detection path. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment; and / or,

[0224] The first device is also used to optimize the detection path corresponding to at least one second device based on the second basic parameter information and the second condition, using the A* path planning algorithm to determine the third detection path corresponding to each of the at least one second device, to indicate the corresponding third detection path to each of the at least one second device, and to control each of the at least one second device to perform a wake-up test on its corresponding third detection path; the second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0225] Each of the at least one second device is configured to perform a wake-up test on its corresponding third detection path and report wake-up test information to the first device.

[0226] For a detailed description and benefits of the system, please refer to [link / reference]. Figure 7 and Figure 8 The embodiments shown will not be described in detail here.

[0227] This application also provides a distributed voice wake-up testing system, which can be applied to... Figure 11 The test environment shown is a scenario for dynamic testing, including a control terminal using the same test environment map, a first device, at least one noise simulation device, and at least two second devices. The test environment map is updated in real time. This test system includes:

[0228] The control terminal is used to acquire basic parameter information in the distributed voice wake-up test scenario. The basic parameter information includes the dynamic position information of all devices in the dynamic test scenario. Based on the basic parameter information and the first condition, A* path planning is used to determine the first detection path corresponding to the first device. The control terminal indicates the first detection path corresponding to the first device to the first device. The first detection path is related to N test positions, where N is an integer greater than or equal to 2. The control terminal controls the first device to perform wake-up tests on at least two second devices on the first detection path. The first condition is determined based on test requirements and includes one or more of the following: prioritizing the selection of points close to the N test positions based on test requirements, the access order of the N test positions, and the priority of path node access when the path cost is the same.

[0229] The first device is used to perform a wake-up test on the first detection path corresponding to the first device and send wake-up test information to the control terminal.

[0230] The control terminal is also used to determine the detection path corresponding to each of the at least two second devices using the A* path planning algorithm based on basic parameter information and the first condition, and to indicate the detection path corresponding to each of the at least two second devices. The detection path corresponding to each second device passes through N test positions, and the control terminal controls each second device to perform wake-up tests at the N test positions on its corresponding detection path. The second condition includes one or more of the following: points that pass through the N test positions need to be selected preferentially, the detection paths corresponding to each of the at least two second devices do not overlap at the N test positions, the access order of the N test positions, or the access priority of path nodes when the path costs are the same.

[0231] Each of at least two second devices is used to perform wake-up tests at N test locations on the detection path corresponding to each second device and send wake-up test information to the control terminal.

[0232] The control terminal is used to determine the distributed voice wake-up test results based on the wake-up test information sent by the first device and the wake-up test information sent by each second device, and to generate a distributed voice wake-up test report.

[0233] At least one noise simulation device for simulating different noises.

[0234] For example, the control terminal: based on the second basic parameter information and the first condition, uses the A* path planning algorithm to optimize the first detection path corresponding to the first device, determines the second detection path, instructs the first device on the second detection path, and controls the first device to perform a wake-up test on the second detection path. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0235] For example, a first device is configured to perform a wake-up test on a second detection path and report the wake-up test information obtained on the second detection path to a control terminal.

[0236] For example, the control terminal is used to optimize the detection path corresponding to at least one second device using the A* path planning algorithm based on the second basic parameter information and the second condition, determine the third detection path corresponding to each of the at least one second device, indicate the corresponding third detection path to each of the at least one second device, and control each of the at least one second device to perform a wake-up test on its corresponding third detection path. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0237] For example, each of the at least one second device is configured to perform a wake-up test on its corresponding third detection path and report the wake-up test information obtained on the third detection path to the control terminal.

[0238] For example, the control terminal is used to determine the distributed voice wake-up test result and generate a distributed voice wake-up test report based on the wake-up test information obtained by the first device on the second detection path and the wake-up test information obtained by at least one second device on its corresponding third detection path.

[0239] For a detailed description and benefits of the system, please refer to [link / reference]. Figure 11 and Figure 12 The embodiments shown will not be described in detail here.

[0240] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0241] To facilitate better implementation of the above-described solutions in the embodiments of this application, related apparatus for implementing the above-described solutions is also provided below.

[0242] Please see Figure 13This is a schematic diagram of a communication device provided in an embodiment of this application. The communication device 1300 may include a transceiver module 1301 (sometimes also called a transceiver unit) and a processing module 1302 (sometimes also called a processing unit). The transceiver module can implement sending and receiving functions. When the transceiver module implements the sending function, it can be called a sending module (sometimes also called a sending unit), and when the transceiver module implements the receiving function, it can be called a receiving module (sometimes also called a receiving unit). The sending module and the receiving module can be the same functional module, which is called the transceiver module and can implement both sending and receiving functions; or, the sending module and the receiving module can be different functional modules, and the transceiver module is a collective term for these functional modules.

[0243] In some possible implementations, the communication device 1300 provided in the embodiments of this application further includes: a storage module (sometimes also called a storage unit) for storing any data, computer instructions and / or computer programs that may be involved in the embodiments of this application.

[0244] The transceiver module 1301, processing module 1302, and storage module in this application embodiment are used to enable the communication device 1300 to perform the functions of the terminal device in the above method embodiment, or to enable the communication device 1300 to perform the functions of the network device in the above method embodiment.

[0245] The following describes the use of communication device 1300 to implement the above method embodiments. Figure 2 The functions of the first device shown are explained, and the various modules in the communication device are described.

[0246] In some possible implementations, the communication device 1300 provided in this application embodiment includes a transceiver module 1301 for acquiring basic parameter information in the distributed voice wake-up test scenario; and a processing module 1302 for determining a first detection path corresponding to the first device using a random walk algorithm based on the basic parameter information and a first condition. The first detection path is associated with N test locations, where N is an integer greater than or equal to 2, and the first condition is determined based on test requirements. The transceiver module 1301 is also used to acquire wake-up test information, which is obtained by performing wake-up tests on at least two second devices along the first detection path. This wake-up test information is used to determine the wake-up test result. The first condition includes one or more of the following: the number of steps, step length, and direction change probability set according to test requirements; or the access order of the N test locations; or minimizing the distance of the first detection path.

[0247] In some possible implementations, the communication device 1300 provided in this application embodiment includes a transceiver module 1301 for acquiring basic parameter information in the distributed voice wake-up test scenario, and a processing module 1302 for determining a first detection path corresponding to a first device using a simulated annealing algorithm based on the basic parameter information and a first condition. The first detection path is associated with N test locations, where N is an integer greater than or equal to 2, and the first condition is determined based on test requirements. The transceiver module 1301 is also used to acquire wake-up test information, which is obtained by performing wake-up tests on at least two second devices along the first detection path. This wake-up test information is used to determine the wake-up test result. The first condition includes one or more of the following: the N test locations determined according to test requirements; or, the access order of the N test locations; or, the distance between two adjacent test locations among the N test locations is less than or equal to a first value; or, the distance between each test location among the N test locations and at least one noise simulation device is less than or equal to a second value; or, each of the at least two second devices uses the same distance to the nearest test location; or, the distance of the first detection path is minimized.

[0248] The following describes the use of communication device 1300 to implement the above method embodiments. Figure 8 The functions of the first device shown are explained, and the various modules in the communication device are described.

[0249] In some possible implementations, in the communication device 1300 provided in this application embodiment, the transceiver module 1301 is used to acquire basic parameter information in the distributed voice wake-up test scenario; the processing module 1302 is further used to determine a first detection path corresponding to the first device based on the basic parameter information and the first condition, using the A* path planning algorithm, wherein the first detection path is related to the N test locations. The first condition includes one or more of the following: prioritizing points closer to the N test locations based on test requirements; or, the access order of the N test locations; or, minimizing the distance of the first detection path; or, the priority of path node access under the condition of the same path cost.

[0250] For example, the processing module 1302 is further configured to, based on the basic parameter information and the second condition, use the A* path planning algorithm to determine a detection path corresponding to each of the at least two second devices, wherein the detection path corresponding to each second device passes through the N test positions, and to indicate the detection path corresponding to each of the at least two second devices to each of the second devices. The detection path corresponding to each second device is used by each second device to perform wake-up tests at the N test positions on its corresponding detection path. The second condition includes one or more of the following: the detection paths corresponding to each of the at least two second devices do not overlap at the N test positions, the access order of the N test positions, the need to prioritize points passing through the N test positions, and the priority of path node access when the path costs are the same.

[0251] The processing module 1302 is further configured to optimize the first detection path corresponding to the first device using the A* path planning algorithm based on the second basic parameter information and the first condition, determine the second detection path, perform a wake-up test on the second detection path, the second basic parameter information including the dynamic position information of the device whose position changes within the second test environment, and optimize the detection path corresponding to at least one second device using the A* path planning algorithm based on the second basic parameter information and the second condition, determine the third detection path, indicate the corresponding third detection path to each of the at least one second device, and control each of the at least one second device to perform a wake-up test on its corresponding third detection path, the second basic parameter information including the dynamic position information of the device whose position changes within the second test environment.

[0252] The following describes the use of communication device 1300 to implement the above method embodiments. Figure 12 The functions of the control terminal device are shown, and the various modules in the communication device are explained.

[0253] In some possible implementations, the communication device 1300 provided in this embodiment includes a transceiver module 1301 for acquiring basic parameter information in the distributed voice wake-up test scenario, and a processing module 1302 for determining a first detection path corresponding to a first device based on the basic parameter information and a first condition using a first algorithm, and indicating the first detection path to the first device. The first detection path is associated with N test positions, where N is an integer greater than or equal to 2, and the first condition is determined based on test requirements. The processing module 1302 controls the terminal to control the first device to perform wake-up tests on at least two second devices at the N test positions along the first detection path. The transceiver module 1301 receives wake-up test information, and the processing module 1302 determines the wake-up test result based on the wake-up test information and generates a test report.

[0254] For example, the processing module 1302 is further configured to determine the detection path corresponding to each of the at least two second devices using the A* path planning algorithm based on the basic parameter information and the second condition, and to indicate the corresponding detection path to each of the at least two second devices, wherein the detection path corresponding to each second device passes through N test positions. The second condition may include one or more of the following: points that pass through the N test positions need to be preferentially selected (e.g., the detection path corresponding to each of the at least two second devices needs to pass through N test positions), the detection paths corresponding to each of the at least two second devices do not overlap at the N test positions, the access order of the N test positions, and the priority of path node access when the path cost is the same.

[0255] The processing module 1302 is further configured to optimize the first detection path corresponding to the first device using the A* path planning algorithm based on the second basic parameter information and the first condition, determine the second detection path, indicate the second detection path to the first device, and control the first device to perform a wake-up test on the second detection path. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment. Based on the second basic parameter information and the second condition, the processing module 1302 optimizes the detection path corresponding to at least one second device using the A* path planning algorithm, indicates the corresponding third detection path to each of the at least one second device, and controls each of the at least one second device to perform a wake-up test on its corresponding third detection path. The second basic parameter information includes the dynamic position information of the device whose position changes within the second test environment.

[0256] The communication device 1300 is used to implement the function of controlling the terminal device in the above method embodiments. (See reference...) Figure 6 The illustrated embodiments and Figure 12 The example shown.

[0257] It should be particularly emphasized that the physical device corresponding to the transceiver module 1301 can be a transceiver, the physical device corresponding to the processing module 1302 can be a processor, and the physical device corresponding to the storage module can be a memory.

[0258] Figure 14 The number 1400 in this example represents another configuration of a communication device provided in an embodiment of this application. The communication device 1400 may be a first device or a control unit. Figure 14A simplified schematic diagram of a communication device is shown. The device includes parts 1401, 1402, and 1403. Part 1402, often referred to as a processor, is used by a first device or control terminal to execute the methods described in the above-described method embodiments. Part 1401 is primarily used to store computer program code and data. Part 1403, often referred to as a transceiver module, transceiver, transceiver circuit, or transceiver unit, etc. The device in part 1403 used to implement the receiving function can be considered a receiver, and the device used to implement the transmitting function can be considered a transmitter; that is, part 1403 includes a receiver 1432 and a transmitter 1431. The receiver can also be called a receiving module, receiver circuit, etc., and the transmitter can be called a transmitting module, transmitter, or transmitting circuit, etc.

[0259] Sections 1401 and 1402 may include one or more circuit boards, each of which may include one or more processors and one or more memories. The processors are used to read and execute programs from the memories to implement baseband processing functions and control the base station. If multiple circuit boards exist, they can be interconnected to enhance processing capabilities. As an alternative implementation, multiple circuit boards may share one or more processors, multiple circuit boards may share one or more memories, or multiple circuit boards may simultaneously share one or more processors.

[0260] For example, in one implementation, the transceiver module in section 1403 is used to execute the transceiver-related processes performed by the first device or control terminal in the aforementioned method embodiments. The processor in section 1402 is used to execute the processing-related processes performed by the first device or control terminal in the aforementioned method embodiments.

[0261] This application also provides a computer-readable storage medium storing program instructions that can be executed by a processor, the program instructions being used to implement the steps in any of the above wake-up test method embodiments.

[0262] The above solution uses a computer-readable storage medium to store data acquired by a visual signal processing device or an acoustic signal processing device, or to store detection data of a first device or at least two second devices to determine the wake-up status of at least two second devices, thereby reducing the probability of false detection and false recording, and thus meeting the needs of smart home appliance quality testing.

[0263] This application also provides a computer program product, including instructions that, when run on a computer, cause the computer to perform the methods described in any of the above embodiments.

[0264] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0265] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0266] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0267] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0268] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0269] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0270] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0271] Furthermore, an operating system runs on the aforementioned components. Examples include iOS, Android, and Windows operating systems. Applications can be installed and run on this operating system. Those skilled in the art will understand that, for the sake of convenience and brevity, explanations and beneficial effects of the relevant content in any of the communication devices provided above can be found in the corresponding method embodiments provided above, and will not be repeated here.

[0272] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or modules, and may be electrical, mechanical, or other forms.

[0273] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0274] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0275] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the essential contribution of the technical solution of this application, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the processes of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0276] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A wake-up test method, characterized by, An electronic device applied to a distributed voice wake-up test scene, comprising: obtaining basic parameter information in the distributed voice wake-up test scene; the basic parameter information comprises position information of all devices in a first test environment, the first test environment being a test environment for static wide test and static fine test; the static wide test refers to distributed voice wake-up test of a first device on a non-movable second device through multiple angles and multiple positions; the static fine test refers to distributed voice wake-up test of the non-movable second device through several fixed angles and distances; based on the basic parameter information and a first condition, a first detection path corresponding to the first device is determined by using a first algorithm, the first detection path being related to N test positions, the N being an integer greater than or equal to 2, and the first condition being determined based on test requirements; obtaining wake-up test information, the wake-up test information being obtained by performing wake-up test on at least two second devices on the first detection path, and the wake-up test information being used to determine a wake-up test result; the first detection path corresponding to the first device is determined by using the first algorithm based on the basic parameter information and the first condition, comprising: in a static wide test scene, a static device is tested by using a distributed voice wake-up mode; a first detection path corresponding to a first device is determined by using a random walk algorithm according to a first condition, the first detection path passing through N test positions; wherein the first condition comprises a step number, a step length and a direction change probability set according to test requirements, an access order of the N test positions, and distance minimization of the first detection path; alternatively, the first detection path corresponding to the first device is determined by using the first algorithm based on the basic parameter information and the first condition, comprising: in a static fine test scene, a plurality of second devices are tested by using a distributed voice wake-up mode; wherein the first algorithm is a simulated annealing algorithm, which is used to simulate a moving path of the first device between different positions; the first condition comprises one or more of the following: N test positions determined according to test requirements; or, an access order of the N test positions; or, a distance between two adjacent test positions in the N test positions is less than or equal to a first value; or, a distance between each test position in the N test positions and at least one noise simulation device is less than or equal to a second value; or, distance minimization of the first detection path; or, each second device in the at least two second devices and the nearest test position use the same distance.

2. The method of claim 1, wherein, the basic parameter information comprises dynamic position information of all devices in a second test environment, the second test environment being a test environment for testing dynamic devices in the distributed voice wake-up test scene, all devices in the second test environment using the same test environment map and updating position information in real time; wherein testing the dynamic devices refers to distributed voice wake-up test of the first device and at least two second devices moving within a certain range.

3. The method of claim 2, wherein, the first condition comprises one or more of the following: preferentially selecting a point close to the N test positions based on test requirements; or, an order of visiting the N test locations; or a priority of visiting nodes of the path in case of same path cost.

4. The method of claim 2, wherein, The first algorithm is an A* path planning algorithm, and the first detection path corresponding to the first device is determined based on the basic parameter information and the first condition by using the first algorithm, which includes: The first detection path corresponding to the first device is determined based on the basic parameter information and the first condition by using the A* path planning algorithm, and the first detection path is related to the N test locations.

5. The method according to any one of claims 2 to 4, characterized in that, The method further includes: The detection path corresponding to each of the at least two second devices is determined based on the basic parameter information and the second condition by using the A* path planning algorithm, and the detection path corresponding to each of the at least two second devices is indicated to each of the at least two second devices, the detection path corresponding to each of the at least two second devices passes through the N test locations, and the detection path corresponding to each of the at least two second devices is used for each of the at least two second devices to perform the wake-up test at the N test locations on the detection path corresponding to each of the at least two second devices.

6. The method of claim 5, wherein, The second condition includes one or more of the following: a point passing through the N test locations needs to be preferentially selected; or the detection path corresponding to each of the at least two second devices does not overlap at the N test locations; or an order of visiting the N test locations; or a priority of visiting nodes of the path in case of same path cost.

7. The method of claim 6, wherein, The method further includes: The first detection path corresponding to the first device is optimized based on second basic parameter information and the first condition by using the A* path planning algorithm to determine a second detection path corresponding to the first device, and the second basic parameter information includes dynamic position information of a device whose position in the second test environment changes; and / or The detection path corresponding to each of the at least one second device is optimized based on second basic parameter information and the second condition by using the A* path planning algorithm to determine a third detection path corresponding to each of the at least one second device, and the third detection path corresponding to each of the at least one second device is indicated to each of the at least one second device, and the second basic parameter information includes dynamic position information of a device whose position in the second test environment changes.

8. A distributed voice wake-up test system, characterized by, The application is applied to a static fine measurement test scene and a static wide measurement test scene, and includes a same test environment map first device, at least one noise simulation device, at least one visual signal processing device, at least one acoustic signal processing device, and at least two second devices, wherein The first device is configured to acquire basic parameter information in a distributed voice wake-up test scene, the basic parameter information including position information of all devices in a first test environment, and based on the basic parameter information and a first condition, a first detection path corresponding to the first device is determined by using a first algorithm, the first detection path being related to N test positions, N being an integer greater than or equal to 2, and the first condition being determined based on a test requirement; the first test environment is a test environment for static wide-range measurement and static precision measurement; the static wide-range measurement refers to distributed voice wake-up test of a second device that is not movable by the first device through multiple angles and multiple positions; and the static precision measurement refers to distributed voice wake-up test of the second device that is not movable through a few fixed angles and distances. The first device determines the first detection path corresponding to the first device based on the basic parameter information and the first condition by using the first algorithm, and a specific implementation manner includes: in a static wide-range measurement scene, voice wake-up test of a static device is performed by using a distributed voice wake-up manner; a random walk algorithm is used to determine the first detection path corresponding to the first device according to the first condition, and the first detection path passes through the N test positions; wherein the first condition includes a step number, a step length, a direction change probability, an access order of the N test positions, and distance minimization of the first detection path, which are set according to a test requirement. Alternatively, the first device determines the first detection path corresponding to the first device based on the basic parameter information and the first condition by using the first algorithm, and a specific implementation manner includes: in a static precision measurement scene, voice wake-up test of multiple second devices is performed by using a distributed voice wake-up manner; wherein the first algorithm is a simulated annealing algorithm, which is used to simulate a moving path of the first device between different positions; and the first condition includes one or more of the following: the N test positions, the access order of the N test positions, the distance between adjacent two test positions in the N test positions being less than or equal to a first value, the distance between each test position in the N test positions and at least one noise simulation device being less than or equal to a second value, distance minimization of the first detection path, and each second device in the at least two second devices and the nearest test position using the same distance. The at least one noise simulation device is configured to simulate different noises. The at least one visual signal processing device and the at least one acoustic signal processing device are configured to acquire wake-up test information obtained by performing wake-up test on the at least two second devices at the N test positions, and report the wake-up test information to the first device. The first device is configured to acquire the wake-up test information, and the wake-up test information is used to determine a wake-up test result.

9. A distributed voice wake-up test system, characterized by, The system is applied to a dynamic test scene, and includes a first device, at least one noise simulation device and at least two second devices using the same test environment map, the test environment map being updated in real time, and the system includes: The first device is configured to acquire basic parameter information in a distributed voice wake-up test scene, the basic parameter information including dynamic position information of all devices in a dynamic test scene; based on the basic parameter information and a first condition, determine a first detection path corresponding to the first device by using an A* path planning algorithm, the first detection path being related to N test positions, the N being an integer greater than or equal to 2, perform wake-up tests on at least two second devices on the first detection path, and acquire wake-up test information, wherein the first condition is determined based on a test requirement, and the first condition includes one or more of the following: preferentially selecting a point close to the N test positions, an access order of the N test positions, or a priority of path node access in the case of the same path cost based on the test requirement; the dynamic test refers to a distributed voice wake-up test performed by the first device and the at least two second devices moving within a certain range. The first device is configured to determine, based on the basic parameter information and a second condition, a detection path corresponding to each of the at least two second devices by using the A* path planning algorithm, indicate the detection path corresponding to each of the at least two second devices to each of the at least two second devices, control each of the at least two second devices to perform wake-up tests on the N test positions on the detection path corresponding to the each of the at least two second devices. Each of the at least two second devices is configured to perform wake-up tests on the N test positions on the corresponding detection path, and send wake-up test information to the first device. The second condition includes one or more of the following: preferentially selecting a point passing through the N test positions, the detection path corresponding to each of the at least two second devices not overlapping at the N test positions, an access order of the N test positions, or a priority of path node access in the case of the same path cost. The at least one noise simulation device is configured to simulate different noises.

10. The system of claim 9, wherein, The first device is further configured to optimize the first detection path corresponding to the first device based on second basic parameter information and the first condition by using a first algorithm, determine a second detection path, and perform wake-up tests on the second detection path, the second basic parameter information including dynamic position information of a device whose position changes in a second test environment. And / or, optimize the detection path corresponding to each of the at least one second device based on second basic parameter information and a second condition by using a first algorithm, determine a third detection path corresponding to each of the at least one second device, indicate the third detection path corresponding to each of the at least one second device to each of the at least one second device, and control each of the at least one second device to perform wake-up tests on the third detection path corresponding to the each of the at least one second device. Each of the at least one second device is configured to perform wake-up tests on the third detection path corresponding to the each of the at least one second device, and report wake-up test information to the first device.

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