Integrated test platform and test method for intelligent wearable device

Abstract

The invention discloses an integrated test platform and test method for intelligent wearable equipment, and the platform comprises a control end, an openable test box body and a plurality of built-in test mechanisms, and is also provided with a temperature and humidity control panel, an atmospheric pressure valve and other components. The control end responds to a user operation issuing instruction, the test mechanism executes a pressure test and feeds back data, and the control end judges a threshold value. In addition, the platform also supports the mobile device to trigger the test with different instruction architectures. The test method is applied to a control end. The test mechanism is firstly triggered to obtain data and judge whether abnormity exists. And if an exception exists or a timestamp meets a condition, obtaining and analyzing a test log, fusing structured and unstructured data, and predicting and determining an exception type by utilizing a machine learning model after preprocessing and similarity rate clustering calculation. And data can be screened according to error names. According to the platform and the method, the intelligent wearable device can be comprehensively and efficiently tested and subjected to anomaly analysis, and the test accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of device testing, and particularly to an integrated testing platform and testing method for smart wearable devices. Background Art

[0002] With the rapid development of technology, the smart wearable device market has shown explosive growth. From smart watches to smart bracelets, these devices have been widely integrated into people's daily lives, undertaking various functions such as health monitoring, sports recording, and information reminder. In this booming situation, the market scale of smart wearable devices continues to expand, and product quality and performance have become the core competitiveness for enterprises to compete in the market.

[0003] In the research and development and production process of smart wearable devices, the testing link is crucial. Especially for the stability and performance of the device during long-term operation in complex environments, stress testing is an essential part. Existing technologies mainly rely on manual operation terminals for large-volume data stress testing, by manually setting the interval time and input thresholds, and performing fixed test actions.

[0004] In the above stress testing process, due to the differences in manual operations and large-scale long-term repeated stress testing, it is difficult to ensure the consistency and testing accuracy of the testing. Summary of the Invention

[0005] The present invention provides an integrated testing platform and testing method for smart wearable devices, which solves the technical problem that in the existing stress testing process, due to the differences in manual operations and large-scale long-term repeated stress testing, it is difficult to ensure the consistency and testing accuracy of the testing.

[0006] An integrated testing platform for smart wearable devices provided by the present invention includes a control terminal, an openable and closable testing box body, and a plurality of testing mechanisms accommodated in the testing box body;

[0007] The control terminal is used to issue control instructions to each of the testing mechanisms in response to user operations, and perform threshold judgment on the received test data to determine whether there is a testing anomaly;

[0008] Each of the testing mechanisms is respectively communicatively connected to the control terminal, and is used to respond to the control instruction, perform stress testing on the smart wearable device, and obtain the corresponding test data and return it to the control terminal.

[0009] Optionally, a temperature and humidity control panel and a temperature and humidity adjustment module are further installed on the testing box body;

[0010] The temperature and humidity control panel is communicatively connected to the control terminal, and is used to respond to the temperature and humidity adjustment instruction issued by the control terminal, and call the temperature and humidity adjustment module to adjust the temperature and humidity in the testing box body.

[0011] Optionally, an atmospheric pressure valve is provided at the top of the test box body, which is used to adjust the air pressure in the test box body in response to the air pressure adjustment instruction issued by the control end.

[0012] Optionally, the test mechanism includes a test base, a fixed column, a limiting fixture, a sliding roller, a fixed bracket, a telescopic rod, a support column and a test belt;

[0013] The test base is located at the bottom of the test box body, and the limiting fixtures are distributed and installed on the test base for limiting the intelligent wearable device;

[0014] The fixed bracket is installed on the test base, and one end is sleeved on the top end of the telescopic rod to limit the telescopic movement of the telescopic rod in the vertical direction;

[0015] The fixed column is sleeved on the bottom end of the telescopic rod;

[0016] The sliding roller is further installed on the fixed bracket, and the support rod is fixedly connected to the sliding roller;

[0017] The test belt bypasses the sliding roller and sequentially passes through the support column and the fixed column, so as to drive the test belt to apply pressure to the intelligent wearable device through the telescopic rod.

[0018] Optionally, a power mechanism is further provided on the fixed bracket for driving the telescopic rod to expand and contract.

[0019] Optionally, it further includes a mobile device with a different instruction architecture from the control end;

[0020] The mobile device is communicatively connected to the test mechanism for issuing a mobile control instruction to the test mechanism to trigger the test mechanism to perform a pressure test.

[0021] The present invention further provides a test method, which is applied to the control end of the integrated test platform as described in any one of the above, and the method includes:

[0022] Responding to a user operation, generating a control instruction and issuing it to at least one test mechanism of the integrated test platform to trigger each test mechanism to perform a pressure test on the intelligent wearable device and obtain test data;

[0023] When receiving the test data returned by each test mechanism, determining whether each test data is within a preset pressure test threshold range;

[0024] If all the test data are within the pressure test threshold range, it is determined that there is no test abnormality;

[0025] If any of the said test data is not within the stress test threshold range, there is a test anomaly.

[0026] Optionally, the method further includes:

[0027] When it is determined that there is a test anomaly, or when the timestamp of the test data meets the preset duration condition, obtain the test log and parse it to obtain structured data and unstructured data;

[0028] Perform data fusion on the structured data and the unstructured data to obtain fused data;

[0029] Perform data preprocessing on the fused data to obtain preprocessed data;

[0030] Calculate the data average similarity rate of the preprocessed data and perform clustering to obtain multiple clustering clusters;

[0031] According to the data characteristics and target tasks of each clustering cluster, respectively select the corresponding machine learning model for data prediction to determine the corresponding anomaly type.

[0032] Optionally, the performing data preprocessing on the fused data to obtain preprocessed data includes:

[0033] Perform data cleaning on the fused data to obtain intermediate data;

[0034] Successively perform duplicate removal, missing value processing, outlier elimination, and normalization operations on the intermediate data to obtain preprocessed data.

[0035] Optionally, after performing the step of obtaining the test log and parsing it to obtain structured data and unstructured data, the method further includes:

[0036] Filter the structured data and the unstructured data according to a variety of preset error names to obtain multiple groups of new structured data and new unstructured data.

[0037] From the above technical solutions, it can be seen that the present invention has the following advantages:

[0038] The present invention provides an integrated test platform for intelligent wearable devices, including a control end, an openable test box body, and a plurality of test mechanisms accommodated in the test box body; the control end is used to issue control instructions to each test mechanism in response to user operations, and perform threshold judgment on the received test data to determine whether there is a test anomaly; each test mechanism is respectively communicatively connected to the control end, and is used to respond to the control instructions, perform a stress test on the intelligent wearable device, and obtain the corresponding test data and return it to the control end. By accommodating multiple test mechanisms in the test box body at the same time and realizing the automatic control of the test process, the test efficiency and test accuracy are effectively improved.

[0039] Meanwhile, in response to user operations through the control terminal, control instructions are generated and sent to at least one test mechanism of the integrated test platform to trigger each test mechanism to perform a pressure test on the smart wearable device and obtain test data; when the test data returned by each test mechanism is received, it is determined whether each test data is within a preset pressure test threshold range; if all the test data are within the pressure test threshold range, it is determined that there is no test anomaly; if any test data is not within the pressure test threshold range, there is a test anomaly. Thus, it effectively guarantees real-time anomaly judgment during the pressure test and improves the pressure test efficiency. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a schematic structural diagram of an integrated test platform for a smart wearable device provided by an embodiment of the present invention;

[0042] Figure 2 It is a schematic test flow diagram of an integrated test platform for a smart wearable device provided by an embodiment of the present invention;

[0043] Figure 3 It is a step flow diagram of a test method in an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of the data analysis process of a test log provided by an embodiment of the present invention;

[0045] Figure 5 It is a test framework diagram of the integrated test platform in an embodiment of the present invention.

[0046] Reference Numerals: 1, atmospheric pressure valve; 2, temperature and humidity control panel; 3, test base; 4, fixed column; 5, limiting fixture; 6, smart wearable device; 7, sliding roller; 8, fixed bracket; 9, telescopic rod; 10, support column; 11, test belt. Detailed Embodiments

[0047] The embodiments of the present invention provide an integrated test platform and a test method for a smart wearable device, which are used to solve the technical problem that in the existing pressure test process, due to the differences in manual operations and large-scale long-term repeated pressure tests, it is difficult to ensure the consistency and test accuracy of the tests.

[0048] To make the objectives, features, and advantages of the present invention more apparent and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0049] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of an integrated test platform for an intelligent wearable device provided by an embodiment of the present invention.

[0050] An integrated test platform for an intelligent wearable device provided by the present invention includes a control end, an openable test box body, and a plurality of test mechanisms accommodated in the test box body;

[0051] The control end is used to issue control instructions to each test mechanism in response to user operations, and perform threshold judgment on the received test data to determine whether there is a test anomaly;

[0052] Each test mechanism is respectively communicatively connected to the control end, and is used to respond to the control instruction, perform a pressure test on the intelligent wearable device 6, and obtain the corresponding test data and return it to the control end.

[0053] In this embodiment, the integrated test platform mainly consists of a control end, an openable test box body, and a plurality of test mechanisms. The control end can be a computer or a dedicated control device, installed with corresponding control software, and the user can input test parameters and instructions through the operation interface of the control end. The test box body adopts an openable design, which is convenient for putting the intelligent wearable device 6 to be tested in and taking it out, and at the same time can provide a relatively closed and stable environment for the test. A plurality of test mechanisms are arranged inside the test box body, and each test mechanism is responsible for different types of pressure tests.

[0054] Specifically, after the smart wearable device 6 to be tested is moved into the position of the limiting fixture 5 of the test mechanism by the robotic arm, at this time, the operation interface at the control end receives information such as the test type, test parameters (such as pressure magnitude, temperature range, humidity value, etc.), and test time input by the user, generates corresponding control instructions, and sends them to each test mechanism through wired or wireless communication. After receiving the control instructions, the test structure conducts a pressure test on the smart wearable device 6 and collects test data in real time, such as pressure values, temperature values, humidity values, response time of the device, working current, etc., and transmits this data back to the control end. After receiving the test data, the control end compares it with a preset threshold. If the test data exceeds the threshold range, the control end determines that the test is abnormal, issues an alarm on the operation interface, and records the abnormal data and relevant information for subsequent analysis and processing. If the test data is within the threshold range, it is determined that the test is normal, and the remaining test tasks are continued to be completed.

[0055] The smart wearable device 6 is a device that integrates technologies such as sensors, wireless communication, and data processing. By wearing it on the human body, it can perceive, collect, analyze, and process the physiological data, motion state, environmental information, etc. of the human body, and provide corresponding services and functions. Such devices usually have the characteristics of miniaturization, portability, and intelligence, and can integrate multiple functions, such as health monitoring (heart rate, blood pressure, sleep, etc.), motion tracking (steps, distance, motion mode, etc.), information reminder (messages, calls, alarms, etc.), mobile payment, positioning and navigation, etc. Common smart wearable devices 6 include smart watches, smart bracelets, smart glasses, smart earphones, smart clothing, smart shoes, etc.

[0056] It should be noted that the test mechanism can perform but is not limited to the following pressure test tasks: mechanical pressure test, temperature-pressure test, and humidity-pressure test. Among them, the mechanical pressure test is used to apply mechanical pressure to parts such as the shell and buttons of the smart wearable device 6 to simulate situations such as squeezing and pressing that the user may encounter in daily use. This mechanism can use an electric push rod or a hydraulic device to apply pressure, and the magnitude of the applied pressure is monitored in real time through a sensor and the data is transmitted to the control end. The temperature-pressure test can simulate different temperature environments and test the performance of the smart wearable device 6 under high and low temperature conditions. The humidity-pressure test is used to test the reliability of the smart wearable device 6 in different humidity environments. It can adjust the humidity in the test chamber through a humidifier and a dehumidifier, and use a humidity sensor to monitor the humidity change in real time and feedback the test data to the control end. To perform the temperature-pressure test and the humidity-pressure test, temperature and humidity adjustment modules and an atmospheric pressure valve 1 and other devices can be further integrated on the test chamber.

[0057] Optionally, a temperature and humidity control panel 2 and a temperature and humidity adjustment module are also installed on the test chamber;

[0058] The temperature and humidity control panel 2 is communicatively connected to the control terminal and is used to respond to the temperature and humidity adjustment instructions issued by the control terminal and call the temperature and humidity adjustment module to regulate the temperature and humidity in the test chamber.

[0059] As Figure 1 shown, a communicatively connected temperature and humidity control panel 2 and a temperature and humidity adjustment module can also be installed in the test chamber. A communication link is established between the temperature and humidity control panel 2 and the control terminal to facilitate remote control of the temperature and humidity environment in the test chamber. After the temperature and humidity control panel 2 receives the temperature and humidity adjustment instructions issued by the control terminal, it parses the instructions. For example, if the instructions require adjusting the temperature in the test chamber to 30 °C and the humidity to 60% RH, the control panel identifies key information such as the target temperature and humidity values. Subsequently, the control panel calls the connected temperature and humidity adjustment module to adjust the temperature and / or humidity in the test chamber through the temperature and humidity adjustment module, providing the required temperature and humidity environment for the pressure test of the smart wearable device 6.

[0060] Among them, the temperature and humidity adjustment module can be composed of a variety of components. For temperature regulation, it may include heating elements such as resistance wires and refrigeration elements such as semiconductor refrigeration chips and small compressor refrigeration systems. When heating is required, the heating element is powered on to generate heat and increase the temperature inside the chamber; when cooling is required, the refrigeration element starts to work. In terms of humidity regulation, an ultrasonic humidifier and a condensation dehumidifier can be set. When increasing humidity is required, the humidifier converts water into water mist and releases it into the chamber; when reducing humidity is required, the dehumidifier collects the water in the chamber through the condensation principle to reduce the humidity. During the whole process, the temperature and humidity sensor continuously monitors the temperature and humidity data in the chamber and feeds it back to the temperature and humidity control panel 2 so that it can perform real-time regulation on the temperature and humidity adjustment module according to the actual situation.

[0061] Furthermore, an atmospheric pressure valve 1 is provided at the top of the test chamber and is used to respond to the air pressure adjustment instructions issued by the control terminal to adjust the air pressure in the test chamber.

[0062] In this embodiment, the atmospheric pressure valve 1 can receive the air pressure adjustment instruction sent from the control end through the integrated communication module, and determine the air pressure value and the change direction that need to be adjusted. Specifically, if the received air pressure adjustment instruction is a pressure increase instruction, the air intake channel is opened through the atmospheric pressure valve 1. Under the action of the pressure difference, external air enters the test chamber through the air intake pipe. As the air entering the chamber increases, the air pressure in the chamber gradually rises. The atmospheric pressure valve 1 monitors the change of the air pressure in the chamber in real time through the built-in pressure sensor and feeds the data back to the control end. When the air pressure reaches the target value set by the control end, the atmospheric pressure valve 1 closes the air intake channel and stops the air intake, thereby stabilizing the air pressure in the chamber. If a pressure reduction instruction is received, the atmospheric pressure valve 1 opens the exhaust channel. The air in the test chamber is discharged to the external environment through the exhaust pipe, resulting in a decrease in the air pressure in the chamber. Similarly, the pressure sensor continuously monitors the air pressure change and feeds it back to the control end. When the air pressure drops to the target value, the atmospheric pressure valve 1 closes the exhaust channel to maintain the stability of the air pressure in the chamber. Thus, it simulates the air pressure environment when the smart wearable device 6 is used in different altitude maps, so as to test the sealing performance, battery performance, and stability of electronic components of the device under different air pressure conditions while conducting the pressure test.

[0063] Optionally, the test mechanism includes a test base 3, fixing columns 4, limiting jigs 5, sliding rollers 7, fixing brackets 8, telescopic rods 9, support columns 10, and a test belt 11;

[0064] The test base 3 is located at the bottom of the test chamber, and the limiting jigs 5 are distributed and installed on the test base 3 for limiting the smart wearable device 6;

[0065] The fixing bracket 8 is installed on the test base 3, and one end is sleeved on the top of the telescopic rod 9 to limit the telescopic rod 9 to expand and contract in the vertical direction;

[0066] The fixing column 4 is sleeved on the bottom end of the telescopic rod 9;

[0067] The fixing bracket 8 is also installed with a sliding roller 7, and the support rod is fixedly connected to the sliding roller 7;

[0068] The test belt 11 bypasses the sliding roller 7 and sequentially passes through the support column 10 and the fixing column 4, so as to drive the test belt 11 to apply pressure to the smart wearable device 6 through the telescopic rod 9.

[0069] As Figure 1As shown in the figure, the testing mechanism includes a testing base 3, fixed columns 4, limiting jigs 5, sliding rollers 7, fixed brackets 8, telescopic rods 9, support columns 10, and a testing belt 11. The testing base 3 serves as the basic support component of the entire testing mechanism. It is located at the bottom of the testing box, providing an installation platform for other components to ensure the stability of the testing process. The limiting jigs 5 are distributed and installed on the testing base 3 to limit the intelligent wearable device 6, preventing the device from moving or shaking during the testing process and ensuring the accuracy and reliability of the testing. For example, when conducting a pressure test on a watch, the limiting jig 5 can firmly fix the watch on the testing base 3, avoiding affecting the test results due to the offset of the watch's position. The limiting jig 5 is an elastic mechanism that can adapt to intelligent wearable devices 6 of different sizes.

[0070] Regarding the fixed bracket 8, it is installed on the testing base 3 and includes a fixed vertical column, a movable crossbar, and a driving mechanism. The crossbar is movably connected to one side of the fixed vertical column and can move up and down through a motor. The driving mechanism is installed at the free end of the crossbar and sleeved on the telescopic rod 9 to limit the telescopic movement of the telescopic rod 9 in the vertical direction, ensuring that the direction of the applied pressure is vertically downward and guaranteeing the accuracy of the test pressure. The telescopic rod 9 is a key component for applying pressure and can be driven by the power mechanism carried by the driving mechanism. In addition, a fixed column 4 is sleeved at the bottom end of the telescopic rod 9. Through the cooperation of the fixed column 4 and the testing belt 11, when the telescopic rod 9 expands and contracts, the pressure is transmitted to the intelligent wearable device 6 through the testing belt 11.

[0071] A sliding roller 7 is also installed at the position of the crossbar of the fixed bracket 8. The roller surface of the sliding roller 7 is connected to the support column 10. When the sliding roller 7 rotates upward, there is no contact between the support column 10 and the testing belt 11, and the testing belt 11 can be continuously placed. When the sliding roller 7 rotates downward, the testing belt 11 is limited by the support column 10 to ensure the position of the testing belt 11 during the pressure test process. At the same time, the fixed column 4 is sleeved on the telescopic rod 9 and has clamps or rollers on both sides for fixing. The testing belt 11 bypasses the sliding roller 7 and passes through the clamp positions on both sides of the support column 10 and the fixed column 4 in sequence. When the telescopic rod 9 expands and contracts downward, the testing belt 11 applies pressure to the intelligent wearable device 6; when the telescopic rod 9 contracts upward, the pressure on the device by the testing belt 11 decreases or is released. The testing belt 11 can be selected with different materials and widths according to different testing requirements to simulate different pressure application methods.

[0072] In addition, the testing belt 11 can be made of an elastic material to avoid damaging the intelligent wearable device 6 during the pressure test process.

[0073] Among them, a power mechanism is also provided on the fixed bracket 8 to drive the telescopic rod 9 to expand and contract.

[0074] In this embodiment, the power mechanism can be a device such as a motor or a cylinder that can drive the telescopic rod 9 to perform telescopic motion.

[0075] Optionally, the integrated test platform further includes a mobile device with a different instruction architecture from the control terminal;

[0076] The mobile device is communicatively connected to the test mechanism and is used to send mobile control instructions to the test mechanism to trigger the test mechanism to perform a pressure test.

[0077] In this embodiment, as Figure 2 shown, in addition to the control terminal, the integrated test platform can also be controlled by a mobile device with a different instruction architecture. Since there are differences between the mobile device and the control terminal in terms of instruction format, encoding method, transmission protocol, etc., the mobile control instructions can be converted through a protocol conversion component to achieve compatibility between the mobile device and the control terminal.

[0078] Specifically, the operator performs corresponding operations on the mobile device, such as opening a specific test application, setting test parameters (such as pressure magnitude, test time, etc.), and then clicking the start test button. The mobile device generates mobile control instructions based on these operations and sends them to the test mechanism. After receiving the mobile control instructions, the test mechanism parses the instructions. According to the parameter requirements in the instructions, the test mechanism starts the corresponding pressure test process. For example, the telescopic rod 9 starts to expand and contract, driving the test belt 11 to apply a preset pressure to the smart wearable device 6 and lasting for the corresponding time. During the test, the test mechanism can feedback test data (such as the actually applied pressure value, the response of the device, etc.) to the mobile device. The mobile device can display and analyze these data in real time, facilitating the operator to understand the test progress and results at any time. Among them, the instruction architecture of the mobile device may be more concise and easy to operate, suitable for quickly starting simple tests; while the instruction architecture of the control terminal may be more complex and have more powerful functions, suitable for large-scale and high-precision test settings and management. The two complement each other, improving the applicability and functionality of the entire integrated test platform.

[0079] In the embodiment of the present application, an integrated test platform for a smart wearable device is provided, including a control terminal, an openable test box body, and a plurality of test mechanisms accommodated in the test box body; the control terminal is used to respond to user operations and send control instructions to each test mechanism, and perform threshold judgment on the received test data to determine whether there is a test anomaly; each test mechanism is communicatively connected to the control terminal respectively and is used to respond to the control instructions, perform a pressure test on the smart wearable device, and obtain the corresponding test data and return it to the control terminal. By accommodating multiple test mechanisms in the test box body at the same time and realizing the automatic control of the test process, the test efficiency and test accuracy are effectively improved.

[0080] Please refer toFigure 3 , Figure 3 is a flowchart of the steps of a testing method in an embodiment of the present invention.

[0081] The embodiment of the present application provides a testing method, which is applied to the control end of the integrated testing platform in any of the above embodiments. The method includes the following steps:

[0082] Step 301, in response to a user operation, generate a control instruction and send it to at least one testing mechanism of the integrated testing platform to trigger each testing mechanism to perform a pressure test on the smart wearable device and obtain test data;

[0083] In this embodiment, after placing at least one smart wearable device on the integrated testing platform through a robotic arm, the control end responds to the user's operation on the operation interface. For example, the user sets parameters for the pressure test (such as pressure magnitude, test duration, etc.) and clicks the start test button. A corresponding control instruction is generated according to the user operation and sent to the testing mechanism on the integrated testing platform where the smart wearable device is placed.

[0084] After the testing mechanism receives the control instruction, it starts the corresponding testing process, applies pressure to the smart wearable device, and in the process of testing, real-time collects test data, such as the actual pressure value received by the device, the deformation data of the device, the working state parameters, etc., and returns the test data to the control end.

[0085] It should be noted that in addition to setting the pressure test parameters, environmental parameters such as temperature, humidity, and air pressure can also be pre-configured according to the usage scenario of the smart wearable device. For example, for an outdoor sports smart watch, simulate the environmental conditions of high altitude, low temperature, and low air pressure for testing. In addition, an information entry link for the smart wearable device can be added, such as model, batch number, production date, etc. Establish a device information database to facilitate subsequent query and statistical analysis, and can also associate test data to provide support for product quality traceability.

[0086] Step 302, when receiving the test data returned by each testing mechanism, determine whether each test data is within a preset pressure test threshold range;

[0087] In this embodiment, after the control end receives the test data, each test data is compared with the pre-set pressure test threshold range. The pressure test threshold range can be determined according to the design standard of the smart wearable device, industry specifications, etc., and the difference between the upper and lower limits of the range is 5%.

[0088] Step 303, if all the test data are within the pressure test threshold range, it is determined that there is no test anomaly;

[0089] Step 304, if any of the test data is not within the pressure test threshold range, there is a test anomaly.

[0090] In this embodiment, if all test data are within the corresponding stress test threshold ranges, it indicates that the smart wearable device performs normally in the current stress test scenario. The system determines that there is no abnormality in this test, which means that the device can meet the design requirements under this stress condition and there are no problems in terms of functions, structures, etc.

[0091] However, if the test data returned by any one test mechanism exceeds the corresponding stress test threshold range, it indicates that an abnormal situation has occurred during the test. For example, the pressure value feedback by the mechanical stress test mechanism exceeds the upper limit that the device can withstand, or there are functional failures in the device during the stress test, etc. The system determines that there is a test abnormality, and further analysis of the abnormal reasons may be required later, such as device quality problems, test process mistakes, etc.

[0092] In addition, a camera can be installed in the test box to perform real-time video monitoring on the test process. This can not only visually view the state of the device during the stress test, such as whether there are appearance damages, component looseness, etc., but also provide a visual basis for subsequent abnormal analysis.

[0093] Please refer to Figure 4 , in an example of the present application, the method further includes the following steps S11 - S15:

[0094] S11. When it is determined that there is a test abnormality, or the timestamp of the test data meets the preset duration condition, obtain the test log and parse it to obtain structured data and unstructured data;

[0095] In this embodiment, if it is determined that there is a test abnormality, that is, a BUG may be generated, or the timestamp of the test data meets the preset duration condition, at this time, obtain the test log and parse it to extract structured data and unstructured data.

[0096] It should be noted that the preset duration condition means that the timestamp is the same as the timestamp boundary corresponding to any acquisition period. The test log can come from multi-source heterogeneous systems and there can be various different log formats, such as JSON, CSV, custom text format, etc. Select the corresponding parsing method for logs of different formats for parsing to extract structured data and unstructured data. For example, use means such as regular expressions and log parsing libraries to parse the test log. For structured data such as test time, test pressure value, and device number, they can be directly extracted from the log and organized into a table form. For unstructured data such as error descriptions and device status information, natural language processing technology is used for preliminary processing. The specific approach is to first extract key information, such as extracting key factors causing abnormalities from error descriptions, such as "connection failure", "sensor failure", etc.

[0097] In addition, the real-time stream data and batch data in the test logs can be processed synchronously. Specifically, for the real-time stream data, a stream processing framework (such as Apache Flink) is used for real-time analysis and processing. For example, real-time monitoring of device anomalies in sensor data. The batch data is processed regularly using a batch processing framework (such as Apache Hive). During the processing, the interaction and synchronization of the real-time stream data and batch data are achieved through a shared data storage (such as a Hive table). For example, the abnormal information in the real-time stream data can be combined with the historical data of batch processing for more in-depth fault analysis.

[0098] In an example of the present application, after executing S11, the method further includes:

[0099] Filter the structured data and unstructured data according to a preset variety of error names to obtain multiple groups of new structured data and new unstructured data.

[0100] To reduce the subsequent data processing volume, the structured data and unstructured data can be initially filtered according to a variety of different error names, and at the same time, the structured data and unstructured data are classified into corresponding error types to obtain multiple groups of new structured data and new unstructured data.

[0101] S12. Perform data fusion on the structured data and unstructured data to obtain fused data;

[0102] In this embodiment, the structured data usually exists in the form of a table, with clear fields and data types, such as the data in a relational database. The unstructured data has no predefined structure, and common ones include text files, images, audio, videos, etc. During the fusion, for the structured data, it can be integrated according to a unified data model through a data integration tool or by writing code. For the unstructured data, data parsing and feature extraction need to be performed first. Taking text data as an example, natural language processing (NLP) techniques, such as word segmentation, part-of-speech tagging, named entity recognition, etc., can be used to convert the text into a structured feature vector. Then, the structured data and the processed features of the unstructured data are combined to form a unified data set for subsequent analysis.

[0103] S13. Perform data preprocessing on the fused data to obtain preprocessed data;

[0104] In an example of the present application, S13 may include the following sub-steps:

[0105] Perform data cleaning on the fused data to obtain intermediate data;

[0106] Perform deduplication, missing value handling, outlier removal, and normalization operations on the intermediate data in sequence to obtain preprocessed data.

[0107] In this embodiment, by checking the fused data, the incorrect, invalid, and duplicate data therein are removed. For example, when checking the test time field, if there are format errors or obviously unreasonable time values, they are corrected or deleted; for the test data with duplicate records, only one record is retained. For the fields with missing values in the data, appropriate methods are selected for processing according to the nature of the fields and the characteristics of the data. If it is a numerical field, such as the test pressure value, methods such as mean and median can be used for filling; if it is a text field, like the device model number, if the missing values are few, the model numbers of other similar devices can be referred to for filling. Through specific algorithms, such as the box plot method, the outliers in the data are identified and removed. Taking the test pressure value as an example, a box plot of the pressure value is drawn, and the data points that deviate significantly from the normal range are determined as outliers and removed to ensure the accuracy and reliability of the data. The data of different magnitudes are normalized so that they are in a unified numerical interval, which is convenient for subsequent analysis and comparison. For example, the data of different magnitudes such as the test pressure value and the device power consumption are uniformly scaled to the interval [0, 1] to eliminate the influence of data magnitude differences on the analysis results.

[0108] S14. Calculate the data average similarity rate of the preprocessed data and perform clustering to obtain multiple clustering clusters;

[0109] In this embodiment, exploratory analysis is first performed to statistically calculate the data mean, and then clustering analysis is performed through K-Means to obtain multiple clustering clusters. For example, a specific similarity measurement algorithm, such as the cosine similarity algorithm, is used to calculate the similarity degree between each data point in the preprocessed data, and then the data average similarity rate is obtained. This average similarity rate reflects the overall similarity characteristics of the data. Based on the calculated average similarity rate, a clustering algorithm, such as the K-means clustering algorithm, is used to divide the data into multiple clustering clusters. The data within each clustering cluster have high similarity, while the data between different clustering clusters have large differences. Through clustering, the data with similar characteristics can be aggregated together, which provides convenience for subsequent targeted analysis.

[0110] Specifically, for each preprocessed data, taking the time data vector X m (i) = {x(i), x(i + 1), …, x(i + m - 1)} of dimension m as an example, where 1 ≤ i ≤ N - m + 1, the data average similarity rate is:

[0111]

[0112] Among them, for Xm(i), given the similarity r, Am(r) represents the data average similarity rate of dimension m.

[0113] S15. According to the data characteristics and target tasks of each clustering cluster, respectively select the corresponding machine learning model for data prediction to determine the corresponding abnormal types.

[0114] In this embodiment, automated analysis can be performed through AutoML to respectively select the corresponding machine learning model for data prediction according to the data characteristics and target tasks of each clustering cluster, and determine the abnormal types corresponding to each clustering cluster.

[0115] Specifically, an automated machine learning platform is used for automated analysis. The preprocessed data is input into the AutoML platform, and the platform automatically selects a suitable machine learning algorithm, adjusts the model parameters, and performs model training and evaluation. Among them, the model can select a Gaussian process regression model, etc., and optimize the algorithm structure through model training to reduce resource consumption.

[0116] Please refer to Figure 5 , Figure 5 , which is a test framework diagram of the integrated test platform in the embodiment of the present invention.

[0117] In this embodiment, taking the control end as the data platform and the PC-host computer as an example, the data of multiple wearable devices is collected by the transmission device and then transmitted to the test tool set for preliminary processing. During this process, the PC-host computer can operate and control the test tool set. The test platform overall manages the entire test process. At the same time, the data processed by the test tool set will be transmitted to the data platform. The data platform first receives the data through the data acquisition module, and then successively performs operations such as multi-platform parsing, BUG parsing, LOG parsing, and data analysis to deeply mine the data value. The PC-host computer can also interact with the data platform to facilitate viewing analysis results, etc., so as to complete the complete test process from device data collection to analysis.

[0118] In the embodiment of the present application, by responding to a user operation at the control end, generating a control instruction and sending it to at least one test mechanism of the integrated test platform to trigger each test mechanism to perform a stress test on the smart wearable device and obtain test data; when receiving the test data returned by each test mechanism, determine whether each test data is within a preset stress test threshold range; if all test data are within the stress test threshold range, it is determined that there is no test abnormality; if any test data is not within the stress test threshold range, there is a test abnormality. Thus, it effectively guarantees real-time abnormality judgment during the stress test and improves the stress test efficiency.

[0119] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or modules can be in electrical, mechanical or other forms.

[0120] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated test platform for intelligent wearable devices, characterized in that It includes a control terminal, an openable and closable test box body, and a plurality of test mechanisms accommodated in the test box body; The control terminal is used to issue control instructions to each of the test mechanisms in response to user operations, and perform threshold judgment on the received test data to determine whether there is a test anomaly; Each of the test mechanisms is respectively communicatively connected to the control terminal, and is used to respond to the control instruction, perform a pressure test on the smart wearable device, and obtain corresponding test data and return it to the control terminal.

2. The integrated test platform according to claim 1, wherein A temperature and humidity control panel and a temperature and humidity adjustment module are also installed on the test box body; The temperature and humidity control panel is communicatively connected to the control terminal, and is used to respond to the temperature and humidity adjustment instruction issued by the control terminal, and call the temperature and humidity adjustment module to adjust the temperature and humidity in the test box body.

3. The integrated test platform according to claim 1 or 2, characterized in that An atmospheric pressure valve is provided at the top of the test box body, and is used to respond to the air pressure adjustment instruction issued by the control terminal to adjust the air pressure in the test box body.

4. The integrated test platform according to claim 1, characterized in that The test mechanism includes a test base, a fixing column, a limiting fixture, a sliding roller, a fixing bracket, a telescopic rod, a support column, and a test belt; The test base is located at the bottom of the test box body, and the limiting fixtures are distributed and installed on the test base for limiting the smart wearable device; The fixing bracket is installed on the test base, and one end is sleeved on the top end of the telescopic rod to limit the telescopic rod to expand and contract in the vertical direction; The fixing column is sleeved at the bottom end of the telescopic rod; The sliding roller is also installed on the fixing bracket, and the support rod is fixedly connected to the sliding roller; The test belt bypasses the sliding roller and sequentially passes through the support column and the fixing column, so as to drive the test belt to apply pressure to the smart wearable device through the telescopic rod.

5. The integrated test platform according to claim 4, characterized in that A power mechanism is also provided on the fixing bracket for driving the telescopic rod to expand and contract.

6. The integrated test platform according to claim 1, wherein It further includes a mobile device using a different instruction architecture from the control terminal; The mobile device is communicatively connected to the test mechanism and is used to issue a mobile control instruction to the test mechanism to trigger the test mechanism to perform a pressure test.

7. A testing method, characterized in that, Applied to the control terminal of the integrated test platform according to any one of claims 1-6, the method includes: Responding to user operations, generating control instructions and issuing them to at least one test mechanism of the integrated test platform to trigger each test mechanism to perform a pressure test on the smart wearable device and obtain test data; When receiving the test data returned by each test mechanism, judging whether each test data is within a preset pressure test threshold range; If all the test data are within the pressure test threshold range, it is determined that there is no test anomaly; If any one of the test data is not within the pressure test threshold range, there is a test anomaly.

8. The test method according to claim 7, wherein The method further includes: When it is determined that there is a test anomaly, or the timestamp of the test data meets the preset duration condition, obtaining and parsing the test log to obtain structured data and unstructured data; Performing data fusion on the structured data and the unstructured data to obtain fusion data; Performing data preprocessing on the fusion data to obtain preprocessed data; Calculate the average data similarity rate of the preprocessed data and perform clustering to obtain multiple clustering clusters; According to the data characteristics and target tasks of each clustering cluster, select corresponding machine learning models for data prediction respectively to determine the corresponding abnormal types.

9. The test method according to claim 8, wherein The data preprocessing of the fusion data to obtain preprocessed data includes: Perform data cleaning on the fusion data to obtain intermediate data; Successively perform deduplication operations, missing value processing, outlier removal, and normalization operations on the intermediate data to obtain preprocessed data.

10. The test method according to claim 8, characterized in that, After performing the step of obtaining the test log and parsing it to obtain structured data and unstructured data, the method further includes: Screen the structured data and the unstructured data according to a variety of preset error names to obtain multiple groups of new structured data and new unstructured data.

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