A test method, system, and electronic device for testing the battery life of a smart wearable device under load.

Abstract

This application discloses a method, system, and electronic device for testing the battery life of a smart wearable device under load, relating to the field of device testing technology. The method includes: establishing a serial communication connection with the smart wearable device under test and sending a test script to the smart wearable device to control it into debug mode; when the smart wearable device is fully charged, sending test commands to a preset test device to enable the test device to simulate steps and heartbeats, and to control the smart wearable device to perform functional automated testing according to the test script; obtaining remaining battery power, heartbeat simulation power consumption, step simulation power consumption, and functional simulation power consumption from the smart wearable device via serial communication; and obtaining the ePOP battery life performance test results of the smart wearable device based on the simulated power consumption and predicted expected energy efficiency. This application can coordinate the control of multiple simulated loads and comprehensively collect data, improving testing accuracy and efficiency.

Description

Technical Field

[0001] This application relates to the field of equipment testing technology, and in particular to a test method, system, and electronic device for testing the load and battery life of a smart wearable device. Background Technology

[0002] In the research and development of smart wearable devices, load-bearing endurance testing of the embedded multi-chip package (ePOP) components is a crucial step in ensuring a good user experience. Existing testing methods often only assess endurance under a single load scenario (such as a single motion simulation or a single function operation), failing to comprehensively simulate the multiple concurrent loads encountered in actual user use. This singular testing mode cannot reflect the power consumption characteristics of the ePOP in complex real-world scenarios, and the data collection on power consumption and power consumption for each load during the test is insufficient. This results in a significant discrepancy between the final ePOP endurance performance evaluation and actual usage, failing to provide accurate data for product optimization. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a testing method, system, and electronic device for the load-bearing life of smart wearable devices, which can coordinately control multiple simulated loads and comprehensively collect data, thereby improving testing accuracy and efficiency.

[0004] Firstly, this application provides a method for testing the battery life of a smart wearable device under load, including:

[0005] A serial communication connection is recommended with the smart wearable device under test, and a test script is sent to the smart wearable device to control the smart wearable device to enter debug mode;

[0006] When the smart wearable device is fully charged, a test command is sent to a preset test device to enable the test device to simulate steps and heart rate on the smart wearable device, and to control the smart wearable device to perform functional automated testing according to the test script; wherein, the test device includes a step count simulator and a heart rate simulator;

[0007] The remaining battery power, heartbeat simulation power consumption, step count simulation power consumption, and function simulation power consumption are obtained from the smart wearable device via serial communication.

[0008] Based on the remaining battery power information, the heartbeat simulated power consumption, the step count simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency, the ePOP battery life performance test results of the smart wearable device are obtained.

[0009] The method for testing the load-bearing battery life of a smart wearable device according to the first aspect of this application has at least the following beneficial effects: First, a serial communication connection is established with the smart wearable device under test, and the smart wearable device is put into debug mode by sending a test script, thus building a reliable channel for subsequent test command transmission and data acquisition; when the smart wearable device is fully charged, two test operations are started simultaneously: on the one hand, test commands are sent to a preset test device to simulate steps and heartbeats on the smart wearable device; on the other hand, the smart wearable device is controlled to perform functional automated tests according to the test script; during the test, the remaining power information of the smart wearable device, as well as the power consumption data corresponding to the heartbeat simulation, step simulation, and functional simulation, are obtained in real time through serial communication; finally, the ePOP battery life performance test results of the smart wearable device are obtained by combining these real-time collected power consumption data, remaining power information, and predicted expected energy efficiency. By establishing serial communication to achieve collaborative control between the test equipment and the smart wearable device, it is possible to synchronously simulate the complex scenario of a user monitoring steps and heart rate while performing functional operations during exercise. This solves the problems of existing test methods where single-load testing cannot reflect real-world usage and incomplete data collection leads to biased evaluation results. By simultaneously acquiring power consumption data and remaining battery information under multiple loads and combining this with expected energy efficiency for comprehensive analysis, the test results are closer to actual usage scenarios, providing a precise basis for optimizing the battery life performance of embedded multi-chip packages. At the same time, the accuracy and efficiency of testing are improved through automated test processes and collaborative control mechanisms.

[0010] According to some embodiments of the first aspect of this application, when the smart wearable device is fully charged, sending a test command to a preset test device to enable the test device to simulate step counts and heart rate on the smart wearable device, and controlling the smart wearable device to perform functional automated testing according to the test script, includes:

[0011] Get the battery life test mode;

[0012] When the battery life test mode is the first mode.

[0013] When the smart wearable device is fully charged, a test command is sent to a preset test device and the smart wearable device is controlled to perform functional automated tests according to the test script, so that the test device simulates the number of steps and heart rate of the smart wearable device until the battery level of the smart wearable device drops to 0.

[0014] According to some embodiments of the first aspect of this application, when the smart wearable device is fully charged, sending a test command to a preset test device to enable the test device to simulate step counts and heart rate on the smart wearable device, and controlling the smart wearable device to perform functional automated testing according to the test script, includes:

[0015] Get the battery life test mode;

[0016] When the battery life test mode is the second mode.

[0017] When the smart wearable device is fully charged, a test command is sent to a preset test device so that the test device can simulate steps and heart rate on the smart wearable device.

[0018] After a preset first time period, the smart wearable device is controlled to perform functional automated testing according to the test script, and this continues for a preset second time period.

[0019] According to some embodiments of the first aspect of this application, the first time and the second time are calculated according to the following steps:

[0020] Construct a user behavior feature database; wherein, the user behavior feature database contains multiple sets of real users' exercise monitoring data, health monitoring data, and communication behavior data;

[0021] Based on the proportion of each data type in the user behavior feature database, a first timeframe for step count simulation and heart rate simulation is determined, and a second timeframe for functional automated testing is determined.

[0022] According to some embodiments of the first aspect of this application, sending test instructions to a preset test device to cause the test device to perform step count simulation and heart rate simulation on the smart wearable device includes:

[0023] Generate step simulation instructions according to the preset step simulation frequency;

[0024] Generate heartbeat simulation commands according to the preset heartbeat simulation frequency;

[0025] Based on the step simulation command and the heartbeat simulation command, a test command is generated;

[0026] Send a test command to a preset test device so that the test device can simulate the number of steps of the smart wearable device according to the step simulation command and simulate the heartbeat of the smart wearable device according to the heartbeat simulation command;

[0027] The smart wearable device is controlled to record the power consumption of the step simulation and the power consumption of the heartbeat simulation during step simulation and heartbeat simulation.

[0028] According to some embodiments of the first aspect of this application, the functional automated testing includes: voice call testing, video call testing, application switching testing, and video playback testing;

[0029] The control of the smart wearable device to perform functional automated testing according to the test script includes:

[0030] The smart wearable device is controlled to establish a connection with a preset virtual server according to the test script;

[0031] According to the test script, voice data packets are retrieved from the smart wearable device, and voice communication is established with the virtual server based on the voice data packets, and the power consumption of the voice communication simulation is recorded.

[0032] According to the test script, video data packets are retrieved from the smart wearable device, and video communication is established with the virtual server based on the video data packets, and the simulated power consumption of the video communication is recorded.

[0033] According to the test script, select several specified applications from the smart wearable device, and switch between the applications based on a preset loop order and loop interval, recording the simulated consumption of application switching.

[0034] According to the test script, a media player is invoked from the smart wearable device, and a preset test video file is played through the media player, recording the simulated video playback consumption;

[0035] Based on the simulated power consumption of voice communication, the simulated power consumption of video communication, the simulated power consumption of application switching, and the simulated power consumption of video playback, a functional simulated power consumption is generated.

[0036] According to some embodiments of the first aspect of this application, obtaining the ePOP battery life performance test results of the smart wearable device based on the remaining battery power information, the heartbeat simulated power consumption, the step count simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency includes:

[0037] The total simulated power consumption is obtained based on the simulated power consumption of heartbeat, simulated power consumption of steps, and simulated power consumption of function.

[0038] The simulated energy efficiency is obtained based on the total simulated power consumption and the remaining power information;

[0039] Based on the simulated energy efficiency and the expected energy efficiency, the ePOP battery life performance test results of the smart wearable device are obtained.

[0040] Secondly, this application also provides a testing system for the load-bearing battery life of smart wearable devices, comprising:

[0041] A communication unit is used to establish a serial communication connection with the smart wearable device under test and send test scripts to the smart wearable device to control the smart wearable device to enter debug mode.

[0042] The testing unit is configured to send test instructions to a preset testing device when the smart wearable device is fully charged, so that the testing device can simulate steps and heart rate on the smart wearable device, and control the smart wearable device to perform functional automated testing according to the test script; wherein, the testing device includes a step counting instrument and a heart rate simulation instrument;

[0043] The acquisition unit is used to acquire remaining battery information, heartbeat simulated power consumption, step count simulated power consumption and function simulated power consumption from the smart wearable device via serial communication.

[0044] The judgment unit is used to obtain the ePOP battery life performance test result of the smart wearable device based on the remaining battery power information, the heartbeat simulated power consumption, the step simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency.

[0045] Thirdly, this application also provides an electronic device, including:

[0046] At least one memory;

[0047] At least one processor;

[0048] At least one program;

[0049] The program is stored in the memory, and the processor executes at least one of the programs to implement the test method for the load-bearing battery life of a smart wearable device as described in any embodiment of the first aspect.

[0050] Fourthly, this application also provides a computer-readable storage medium storing computer-executable signals for performing a test method for the load endurance of a smart wearable device as described in any embodiment of the first aspect.

[0051] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0052] Additional aspects and advantages of this application will become apparent and readily understood in conjunction with the following description of the embodiments, in which:

[0053] Figure 1 A flowchart illustrating a method for testing the load and battery life of a smart wearable device according to some embodiments of this application;

[0054] Figure 2 This is a schematic diagram of a smart wearable device load and battery life testing system provided in some embodiments of this application. Detailed Implementation

[0055] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0056] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0057] In the description of this application, the use of "first" and "second" is for the purpose of distinguishing technical features only, and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0058] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0059] ePOP (Embedded Package on Package) is a highly integrated embedded storage solution designed for space-constrained and power-sensitive smart wearables, VR glasses, and other similar devices. It achieves integrated storage and runtime memory by vertically stacking eMMC (embedded multimedia card) and LPDDR (low-power double data rate memory) within the same package, significantly optimizing the balance between device size, power consumption, and performance.

[0060] In the research and development of smart wearable devices, load-bearing endurance testing of the embedded multi-chip package (ePOP) components is a crucial step in ensuring a good user experience. Existing testing methods often only assess endurance under a single load scenario (such as a single motion simulation or a single function operation), failing to comprehensively simulate the multiple concurrent loads encountered in actual user use. This singular testing mode cannot reflect the power consumption characteristics of the ePOP in complex real-world scenarios, and the data collection on power consumption and power consumption for each load during the test is insufficient. This results in a significant discrepancy between the final ePOP endurance performance evaluation and actual usage, failing to provide accurate data for product optimization.

[0061] Based on this, this application provides a test method, system, and electronic device for the load-bearing battery life of smart wearable devices to solve the aforementioned technical problems. The technical solutions provided in this application will be described in detail below.

[0062] Firstly, this application provides a method for testing the battery life of a smart wearable device under load. This method uses testing equipment to assist the smart wearable device in simulating some functions. Specifically, the method may include, but is not limited to, the following steps:

[0063] Step S110: Establish a serial communication connection with the smart wearable device under test and send a test script to the smart wearable device to control it to enter debug mode.

[0064] Step S120: When the smart wearable device is fully charged, send a test command to a preset test device so that the test device can simulate steps and heartbeats on the smart wearable device, and control the smart wearable device to perform functional automated tests according to the test script; wherein, the test device includes a step simulation instrument and a heartbeat simulation instrument.

[0065] Step S130: Obtain remaining battery power, heartbeat simulation power consumption, step count simulation power consumption, and function simulation power consumption from the smart wearable device via serial communication.

[0066] Step S140: Based on the remaining battery power information, heartbeat simulation power consumption, step simulation power consumption, function simulation power consumption, and predicted expected energy efficiency, obtain the ePOP battery life performance test results of the smart wearable device.

[0067] In steps S110 to S140, firstly, a serial communication connection is established with the smart wearable device under test. A test script is sent to put the smart wearable device into debug mode, establishing a reliable channel for subsequent command transmission and data acquisition. When the smart wearable device is fully charged, two test operations are initiated simultaneously: firstly, test commands are sent to a preset test device to simulate step counts and heartbeats; secondly, the smart wearable device is controlled to perform functional automated tests according to the test script. During the test, the remaining battery power information of the smart wearable device, as well as the power consumption data corresponding to the heartbeat simulation, step count simulation, and functional simulation, are acquired in real time via serial communication. Finally, by combining these real-time collected power consumption data, remaining battery power information, and predicted expected energy efficiency, a comprehensive analysis is conducted to obtain the ePOP battery life performance test results of the smart wearable device. By establishing serial communication to achieve collaborative control between the test equipment and the smart wearable device, it is possible to synchronously simulate the complex scenario of a user monitoring steps and heart rate while performing functional operations during exercise. This solves the problems of existing test methods where single-load testing cannot reflect real-world usage and incomplete data collection leads to biased evaluation results. By simultaneously acquiring power consumption data and remaining battery information under multiple loads and combining this with expected energy efficiency for comprehensive analysis, the test results are closer to actual usage scenarios, providing a precise basis for optimizing the battery life performance of embedded multi-chip packages. At the same time, the accuracy and efficiency of testing are improved through automated test processes and collaborative control mechanisms.

[0068] It is understood that step S120 may include, but is not limited to, the following steps:

[0069] Step S210: Obtain the battery life test mode.

[0070] Step S220: When the battery life test mode is the first mode, and the smart wearable device is fully charged, a test command is sent to the preset test device and the smart wearable device is controlled to perform functional automated tests according to the test script, so that the test device simulates the number of steps and the heartbeat of the smart wearable device until the battery of the smart wearable device drops to 0.

[0071] In steps S210 to S220, the battery life test mode is first obtained. When the mode is determined to be the first mode, with the smart wearable device fully charged, a test command is simultaneously sent to a preset test device to initiate step count simulation and heart rate simulation. At the same time, the smart wearable device is controlled to execute functional automated tests according to the test script. These two operations continue until the smart wearable device's battery is completely depleted. The first mode enables continuous testing under high-load scenarios, accurately simulating the complete process of the smart wearable device from full charge to battery depletion under extreme conditions of continuous motion monitoring and functional use. This obtains battery life data of the embedded multi-chip package under extreme load, providing direct evidence for evaluating its performance in high-intensity usage scenarios and further improving the comprehensiveness of the battery life test.

[0072] It is understood that step S120 may include, but is not limited to, the following steps:

[0073] Step S310: Obtain the battery life test mode;

[0074] Step S320: When the battery life test mode is the second mode, and the smart wearable device is fully charged, send a test command to the preset test device so that the test device can simulate the number of steps and the heartbeat of the smart wearable device.

[0075] Step S330: After the preset first time, control the smart wearable device to perform functional automated testing according to the test script, and continue for the preset second time.

[0076] In steps S310 to S330, the battery life test mode is first obtained. When the second mode is determined, with the smart wearable device fully charged, a test command is first sent to a preset test device to simulate step count and heart rate. This simulation process lasts for a preset first time. After the first time ends, the smart wearable device is controlled to execute functional automated tests according to the test script, and this test process lasts for a preset second time. The second mode realizes phased load testing, which can accurately simulate the daily scenarios of users performing exercise monitoring and function use at different times. It supplements the first mode's testing of extreme scenarios, making the battery life test of the smart wearable device more in line with actual usage habits. This provides a more detailed basis for evaluating the power consumption characteristics of embedded multi-chip packages in segmented usage scenarios, further improving the comprehensiveness and realism of the test.

[0077] It is understood that the first and second times are based on the following steps, including but not limited to:

[0078] Step S410: Construct a user behavior feature database; wherein, the user behavior feature database contains multiple sets of real users' exercise monitoring data, health monitoring data, and communication behavior data.

[0079] Step S420: Based on the proportion of each data type in the user behavior feature database, determine the first time for step simulation and heartbeat simulation, and the second time for functional automated testing.

[0080] In steps S410 to S420, a user behavior characteristic database is first constructed, containing multiple sets of real user activity monitoring data, health monitoring data, and communication behavior data. Then, the proportion of each data type in the database is analyzed, and based on these proportions, a first timeframe for step count simulation and heart rate simulation, and a second timeframe for functional automation testing are determined. For example, the user behavior characteristic database shows that most users perform an average of 3 hours of activity monitoring per day, which may include steps and heart rate, and 2 hours of communication or application operations, such as voice calls and video playback. By analyzing the proportion of these two types of data in the total usage time—60% for activity monitoring and 40% for functional operations—the testing method allocates the total test time according to this ratio. For example, the first timeframe is set to 6 hours (simulated activity monitoring), and the second timeframe is set to 4 hours (simulated functional operations), thus making the testing process closer to real-world usage scenarios. By calculating the test time based on real user behavior data, the testing process is made closer to actual user habits, significantly improving the authenticity and reliability of the test results and more accurately reflecting the embedded multi-chip package battery life performance of smart wearable devices in daily use.

[0081] It is understood that the steps of step simulation and heartbeat simulation in step S120 may include, but are not limited to, the following steps:

[0082] Step S510: Generate step simulation instructions according to the preset step simulation frequency.

[0083] Step S520: Generate a heartbeat simulation command according to the preset heartbeat simulation frequency.

[0084] Step S530: Generate test instructions based on the step simulation instructions and heartbeat simulation instructions.

[0085] Step S540: Send a test command to the preset test device so that the test device can simulate the number of steps of the smart wearable device according to the step simulation command and simulate the heartbeat of the smart wearable device according to the heartbeat simulation command.

[0086] Step S550: Control the smart wearable device to record the power consumption of step simulation and heartbeat simulation during step simulation and heartbeat simulation.

[0087] In steps S510 to S550, simulation instructions are precisely generated at a preset frequency, which realizes precise control of the step count and heart rate simulation process. At the same time, the corresponding power consumption is recorded simultaneously, which enables accurate acquisition of the power consumption characteristics of smart wearable devices in sports monitoring scenarios, providing more detailed and targeted data support for the evaluation of the battery life performance of embedded multi-chip packages.

[0088] It is understood that functional automated testing includes: voice call testing, video call testing, application switching testing, and video playback testing. The functional automated testing steps in step S120 may include, but are not limited to, the following steps:

[0089] Step S610: Control the smart wearable device to establish a connection with the preset virtual server according to the test script.

[0090] Step S620: According to the test script, obtain the voice data packet from the smart wearable device, establish voice communication with the virtual server based on the voice data packet, and record the simulated power consumption of the voice communication.

[0091] Step S630: According to the test script, retrieve the video data packet from the smart wearable device, establish video communication with the virtual server based on the video data packet, and record the simulated power consumption of the video communication.

[0092] Step S640: According to the test script, select several specified applications from the smart wearable device, and switch between the applications based on the preset loop order and loop interval, and record the simulated consumption of application switching.

[0093] Step S650: According to the test script, call the media player from the smart wearable device, play the preset test video file through the media player, and record the simulated consumption of video playback.

[0094] Step S660: Generate functional simulated power consumption based on the simulated power consumption of voice communication, simulated power consumption of video communication, simulated power consumption of application switching, and simulated power consumption of video playback.

[0095] In steps S610 to S660, the smart wearable device establishes a connection with a preset virtual server according to the test script. Then, it executes various functional tests sequentially according to the script. Specifically, it retrieves voice data packets from the device to establish voice communication with the virtual server and records the simulated power consumption of voice communication; it retrieves video data packets to establish video communication and records the simulated power consumption of video communication; it selects a specified application and switches it according to a preset loop order and interval, recording the simulated power consumption of application switching; it calls a media player to play a preset test video file and records the simulated power consumption of video playback; finally, it combines these recorded power consumption data to generate a functional simulated power consumption. By executing multiple functional automated tests in different scenarios and recording the corresponding power consumption, the core functional usage scenarios of the smart wearable device are comprehensively covered. This ensures that the generated functional simulated power consumption accurately reflects the energy consumption characteristics under different functional operations, thus providing a detailed and realistic basis for evaluating the battery life performance of ePOP in diverse functional uses, improving the comprehensiveness and accuracy of the test.

[0096] The following examples illustrate the specific execution process of voice call testing, video call testing, application switching testing, and video playback testing:

[0097] Voice call test: The test script controls the smart wearable device to establish a SIP connection with a virtual server, retrieves pre-recorded voice data packets (such as human dialogue clips) from the device's storage, sends the voice stream to the virtual server by simulating a real call process (such as ringing, answering, and call holding), and simultaneously receives simulated voice data returned by the server. During the test, the device's power consumption during voice encoding / decoding, network transmission, and audio output is monitored and recorded in real time.

[0098] Video call test: The test script activates the camera module of the smart wearable device, captures video streams according to preset parameters (e.g., 720p resolution, 30fps frame rate), and compresses the video streams using encoding algorithms such as H.264. The compressed video data packets are transmitted to a virtual server via the RTCP protocol, while simultaneously receiving simulated video streams from the server for decoding and display. During the test, the power consumption changes of the device throughout the entire process of video capture, encoding, network transmission, decoding, and screen display are recorded.

[0099] Application Switching Test: The test script selects 5-10 frequently used applications (such as health monitoring, messaging and social networking, music playback, etc.) from the device and automatically triggers application launch and switching operations according to a preset order (such as cyclic switching) and time interval (such as switching every 15 seconds). Each time a switch occurs, the time from clicking the application icon to the interface fully rendering is recorded, and the device's power consumption during memory management, CPU scheduling, and interface rendering is monitored simultaneously to assess the impact of multitasking on battery life.

[0100] Video playback test: The test script calls the media player of the smart wearable device and loads pre-stored test video files (containing different encoding formats, resolutions, and bitrates). During playback, it simulates real user operations, such as adjusting the playback progress every 5 minutes, randomly switching brightness levels (from 50% to 100%), and volume levels (from 30% to 80%). The test continuously records power consumption data for video decoding, cache management, screen backlight, and audio output.

[0101] It is understood that step S140 may include, but is not limited to, the following steps:

[0102] Step S710: Calculate the total analog power consumption based on the heartbeat analog power consumption, step analog power consumption, and function analog power consumption.

[0103] Step S720: Obtain the simulated energy efficiency based on the total simulated power consumption and remaining power information.

[0104] Step S730: Based on the simulated energy efficiency and expected energy efficiency, obtain the ePOP battery life performance test results of the smart wearable device.

[0105] In steps S710 to S730, the simulated power consumption of heartbeat, steps, and functions is first integrated and calculated to obtain the total simulated power consumption. Then, combined with the remaining battery information, the simulated energy efficiency is obtained through correlation analysis. Finally, the simulated energy efficiency is compared with the predicted expected energy efficiency to obtain the test results of the embedded multi-chip package battery life performance of the smart wearable device. When judging the test results, the simulated power consumption of heartbeat, steps, and functions is first summed to obtain the total simulated power consumption. Then, combined with the remaining battery information, the actual simulated energy efficiency is calculated, such as the ratio of power consumption to remaining battery power. After that, the calculated simulated energy efficiency is compared with the preset expected energy efficiency. If the simulated energy efficiency is close to or better than the expected energy efficiency, it means that the embedded multi-chip package battery life performance of the smart wearable device meets the standard. If the difference is large, it is judged as not meeting the standard. At the same time, the proportion of power consumption of each part can be used to see which type of function has excessive power consumption, providing direction for subsequent optimization.

[0106] Through phased power consumption integration, energy efficiency calculation and comparative analysis, a complete and logically coherent battery life performance evaluation system has been formed. It can accurately quantify the energy efficiency performance of embedded multi-chip packages under multi-load scenarios, and intuitively reflect the actual performance gap by comparing with the expected energy efficiency. This provides a clear quantitative basis for product power consumption optimization and enhances the reference value of test results.

[0107] Secondly, this application also provides a test system 800 for testing the battery life of a smart wearable device under load, comprising:

[0108] The communication unit 810 is used to establish a serial communication connection with the smart wearable device under test and send test scripts to the smart wearable device to control the smart wearable device to enter debug mode.

[0109] The test unit 820 is used to send test commands to a preset test device when the smart wearable device is fully charged, so that the test device can simulate steps and heartbeats on the smart wearable device, and control the smart wearable device to perform functional automated tests according to the test script; wherein, the test device includes a step count simulator and a heartbeat simulator;

[0110] The acquisition unit 830 is used to acquire remaining battery information, heartbeat simulated power consumption, step count simulated power consumption and function simulated power consumption from the smart wearable device via serial communication.

[0111] The judgment unit 840 is used to obtain the ePOP battery life performance test results of the smart wearable device based on the remaining power information, heartbeat simulated power consumption, step simulated power consumption, function simulated power consumption and predicted expected energy efficiency.

[0112] The specific implementation of the test system for the load-bearing life of the smart wearable device is basically the same as the specific implementation of the test method for the load-bearing life of the smart wearable device described above, and will not be repeated here.

[0113] Thirdly, this application also provides an electronic device, including: at least one memory, at least one processor, and at least one program, wherein the program is stored in the memory, and the processor executes one or more programs to implement the above-described test method for the load endurance of a smart wearable device.

[0114] In this electronic device, firstly, a serial communication connection is established with the smart wearable device under test. By sending a test script, the smart wearable device enters debug mode, establishing a reliable channel for subsequent command transmission and data acquisition. When the smart wearable device is fully charged, two test operations are initiated simultaneously: firstly, test commands are sent to a preset test device to simulate step count and heartbeat on the smart wearable device; secondly, the smart wearable device is controlled to perform functional automated tests according to the test script. During the test, the remaining battery information of the smart wearable device, as well as the power consumption data corresponding to the heartbeat simulation, step count simulation, and functional simulation, are acquired in real time through serial communication. Finally, by combining these real-time collected power consumption data, remaining battery information, and predicted expected energy efficiency, the ePOP battery life performance test results of the smart wearable device are comprehensively analyzed. By establishing serial communication to achieve collaborative control between the test equipment and the smart wearable device, it is possible to synchronously simulate the complex scenario of a user monitoring steps and heart rate while performing functional operations during exercise. This solves the problems of existing test methods where single-load testing cannot reflect real-world usage and incomplete data collection leads to biased evaluation results. By simultaneously acquiring power consumption data and remaining battery information under multiple loads and combining this with expected energy efficiency for comprehensive analysis, the test results are closer to actual usage scenarios, providing a precise basis for optimizing the battery life performance of embedded multi-chip packages. At the same time, the accuracy and efficiency of testing are improved through automated test processes and collaborative control mechanisms.

[0115] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data related to the test method for the battery life of the aforementioned smart wearable device. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processing module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] One or more signals are stored in memory, and when executed by one or more processors, the test method for the load-bearing battery life of the smart wearable device in any of the above method embodiments is executed.

[0117] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that is executed by one or more processors, enabling the one or more processors to perform the test method for the load and battery life of a smart wearable device in the above method embodiments.

[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 according to actual needs.

[0119] Based on the above description of the embodiments, those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable signals, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable signals, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0120] It should be understood that in this application, "at least one item" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, 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. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, "at least one of a, b, or c" can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0122] The units described above 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 according to actual needs.

[0123] 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.

[0124] 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 multiple instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method for testing the battery life of a smart wearable device under load, characterized in that, include: A serial communication connection is recommended with the smart wearable device under test, and a test script is sent to the smart wearable device to control the smart wearable device to enter debug mode; When the smart wearable device is fully charged, a test command is sent to a preset test device to enable the test device to simulate steps and heart rate on the smart wearable device, and to control the smart wearable device to perform functional automated testing according to the test script; wherein, the test device includes a step count simulator and a heart rate simulator; The remaining battery power, heartbeat simulation power consumption, step count simulation power consumption, and function simulation power consumption are obtained from the smart wearable device via serial communication. Based on the remaining battery power information, the heartbeat simulated power consumption, the step simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency, the ePOP battery life performance test results of the smart wearable device are obtained. The step of sending a test command to a preset test device when the smart wearable device is fully charged, so that the test device can simulate steps and heart rate on the smart wearable device, and control the smart wearable device to perform functional automated testing according to the test script, includes: Get the battery life test mode; When the battery life test mode is the second mode, when the smart wearable device is fully charged, a test command is sent to a preset test device to enable the test device to simulate steps and heart rate on the smart wearable device; after a preset first time, the smart wearable device is controlled to perform functional automated testing according to the test script, and this continues for a preset second time. The first time and the second time are calculated according to the following steps: Construct a user behavior feature database; wherein the user behavior feature database contains multiple sets of real users' exercise monitoring data, health monitoring data, and communication behavior data; based on the proportion of each data type in the user behavior feature database, determine the first time for step count simulation and heart rate simulation, and determine the second time for functional automation testing.

2. The test method for the load-bearing battery life of a smart wearable device according to claim 1, characterized in that, When the smart wearable device is fully charged, a test command is sent to a preset test device to enable the test device to simulate step counts and heart rate on the smart wearable device, and to control the smart wearable device to perform automated functional testing according to the test script, including: Get the battery life test mode; When the battery life test mode is the first mode. When the smart wearable device is fully charged, a test command is sent to a preset test device and the smart wearable device is controlled to perform functional automated tests according to the test script, so that the test device simulates the number of steps and heart rate of the smart wearable device until the battery level of the smart wearable device drops to 0.

3. The test method for the load-bearing battery life of a smart wearable device according to claim 1, characterized in that, Sending test instructions to a preset test device to enable the test device to simulate steps and heart rate on the smart wearable device includes: Generate step simulation instructions according to the preset step simulation frequency; Generate heartbeat simulation commands according to the preset heartbeat simulation frequency; Based on the step simulation command and the heartbeat simulation command, a test command is generated; Send a test command to a preset test device so that the test device can simulate the number of steps of the smart wearable device according to the step simulation command and simulate the heartbeat of the smart wearable device according to the heartbeat simulation command; The smart wearable device is controlled to record the power consumption of the step simulation and the power consumption of the heartbeat simulation during step simulation and heartbeat simulation.

4. The test method for the load-bearing battery life of a smart wearable device according to claim 1, characterized in that, The functional automated tests include: voice call testing, video call testing, application switching testing, and video playback testing; The control of the smart wearable device to perform functional automated testing according to the test script includes: The smart wearable device is controlled to establish a connection with a preset virtual server according to the test script; According to the test script, voice data packets are retrieved from the smart wearable device, and voice communication is established with the virtual server based on the voice data packets, and the power consumption of the voice communication simulation is recorded. According to the test script, video data packets are retrieved from the smart wearable device, and video communication is established with the virtual server based on the video data packets, and the simulated power consumption of the video communication is recorded. According to the test script, select several specified applications from the smart wearable device, and switch between the applications based on a preset loop order and loop interval, recording the simulated consumption of application switching. According to the test script, a media player is invoked from the smart wearable device, and a preset test video file is played through the media player, recording the simulated video playback consumption. Based on the simulated power consumption of voice communication, the simulated power consumption of video communication, the simulated power consumption of application switching, and the simulated power consumption of video playback, a functional simulated power consumption is generated.

5. The test method for the load-bearing battery life of a smart wearable device according to claim 1, characterized in that, The process of obtaining the ePOP battery life performance test results of the smart wearable device based on the remaining battery power information, the heartbeat simulated power consumption, the step count simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency includes: The total simulated power consumption is obtained based on the simulated power consumption of heartbeat, simulated power consumption of steps, and simulated power consumption of function. The simulated energy efficiency is obtained based on the total simulated power consumption and the remaining power information; Based on the simulated energy efficiency and the expected energy efficiency, the ePOP battery life performance test results of the smart wearable device are obtained.

6. A test system for the load-bearing battery life of a smart wearable device, characterized in that, include: A communication unit is used to establish a serial communication connection with the smart wearable device under test and send test scripts to the smart wearable device to control the smart wearable device to enter debug mode. The testing unit is configured to send test instructions to a preset testing device when the smart wearable device is fully charged, so that the testing device can simulate steps and heart rate on the smart wearable device, and control the smart wearable device to perform functional automated testing according to the test script; wherein, the testing device includes a step counting instrument and a heart rate simulation instrument; The acquisition unit is used to acquire remaining battery information, heartbeat simulated power consumption, step count simulated power consumption, and function simulated power consumption from the smart wearable device via serial communication. The judgment unit is used to obtain the ePOP battery life performance test result of the smart wearable device based on the remaining power information, the heartbeat simulated power consumption, the step simulated power consumption, the function simulated power consumption, and the predicted expected energy efficiency. The step of sending a test command to a preset test device when the smart wearable device is fully charged, so that the test device can simulate steps and heart rate on the smart wearable device, and control the smart wearable device to perform functional automated testing according to the test script, includes: Get the battery life test mode; When the battery life test mode is the second mode, when the smart wearable device is fully charged, a test command is sent to a preset test device to enable the test device to simulate steps and heart rate on the smart wearable device; after a preset first time, the smart wearable device is controlled to perform functional automated testing according to the test script, and this continues for a preset second time. The first time and the second time are calculated according to the following steps: Construct a user behavior feature database; wherein the user behavior feature database contains multiple sets of real users' exercise monitoring data, health monitoring data, and communication behavior data; based on the proportion of each data type in the user behavior feature database, determine the first time for step count simulation and heart rate simulation, and determine the second time for functional automation testing.

7. An electronic device, characterized in that, include: At least one memory; At least one processor; At least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement the test method for the load endurance of a smart wearable device as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable signals for performing the test method for the load endurance of a smart wearable device as described in any one of claims 1 to 5.

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