Automatic testing system and method applied to defrosting and noise reduction of refrigerator and storage medium

Abstract

The invention discloses an automatic testing system and method for refrigerator defrosting and noise reduction application and a storage medium, relates to the technical field of automatic testing, and solves the problems that in the prior art, related logs are manually checked and part of testing tools are combined to complete testing work together, a large amount of manpower and material resources are wasted, and the testing efficiency is high. And the efficiency of the automatic test method is low. Inputting the task parameters, the equipment parameters and the environment data into a case generation model to obtain a test case; generating a control instruction according to the test case; generating a feedback result according to the control instruction; according to the method, the alarm signal is generated according to the feedback result, refrigerator defrosting and noise reduction testing is achieved through an automatic process, the test case and the expected result are dynamically adjusted according to different testing devices and environment changes of the devices in the testing process, and the efficiency of automatic testing and the accuracy of result generation are improved.

Description

Technical Field

[0001] The present application belongs to the field of automated testing technology, and specifically relates to an automated testing system, method, and storage medium for refrigerator defrosting and noise reduction applications. Background Art

[0002] Against the backdrop of the continuous evolution of contemporary home appliance technology and consumers' ever-increasing pursuit of quality of life, refrigerator performance optimization has become a key research topic as an indispensable storage device in the home. Among these, defrosting and noise reduction technologies are key factors affecting the user experience and operational efficiency of refrigerators. During long-term operation, traditional refrigerators are prone to frost on the evaporator surface, which not only hinders the efficiency of heat exchange and increases energy consumption, but may also affect the preservation of food. At the same time, noise generated by compressor operation and fan operation becomes increasingly prominent in quiet home environments, disrupting users' normal lives. Therefore, conducting application testing and research on refrigerator defrosting and noise reduction is of vital practical significance for improving overall refrigerator performance and meeting consumers' dual demands for efficient energy saving and a quiet and comfortable life.

[0003] Existing refrigerator defrost noise reduction application tests often rely on manual review of relevant logs and the combination of some test tools to complete the test work, resulting in a large waste of manpower and material resources, and a long time and low efficiency, resulting in low efficiency of the automated testing method; therefore, the automated testing method for refrigerator defrost noise reduction applications still needs further improvement. Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an automated testing system, method and storage medium for refrigerator defrosting and noise reduction applications, which is used to solve the technical problem that the prior art often uses manual review of relevant logs and combination of some testing tools to complete the testing work, resulting in a large amount of waste of manpower and material resources, and leading to low efficiency of the automated testing method.

[0005] To achieve the above objectives, the first aspect of the present application provides an automated testing method for refrigerator defrosting noise reduction applications, comprising:

[0006] Obtain task parameters, environmental data, and equipment parameters;

[0007] Inputting task parameters, device parameters, and environmental data into a use case generation model to obtain a test case; the use case generation model is constructed using a machine learning model;

[0008] Generate control instructions based on test cases;

[0009] Generate feedback results according to control instructions;

[0010] Generate an alarm signal based on the feedback results.

[0011] Through the above steps, this application builds a set of automated processes to realize the automated execution of refrigerator defrost tests and noise reduction tests; in this process, the characteristic differences of different test equipment and the dynamic changes of the test environment with the process are fully considered, and the test cases and expected results are flexibly adjusted in a targeted manner. In this way, the work efficiency of automated testing is significantly improved, and the accuracy of test result generation is greatly enhanced.

[0012] Furthermore, the use case generation model is constructed through a machine learning model, including:

[0013] Obtain several historical task parameters, historical equipment parameters, historical environment data, and historical test cases;

[0014] Divide a number of historical task parameters, historical equipment parameters, historical environment data, and historical test cases into training data, verification data, and test data; perform data preprocessing on the training data, verification data, and test data to obtain a training set, a verification set, and a test set;

[0015] Select a machine learning model as the base model;

[0016] Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model;

[0017] By verifying the pre-trained model on the test set, we finally obtain the input task parameters, device parameters, and environmental data, and output the use case generation model for the test case.

[0018] Furthermore, generating control instructions according to the test case includes:

[0019] Get test cases;

[0020] Parse the test case to obtain several test steps and test parameters;

[0021] Encapsulate the test parameters corresponding to several test steps to obtain an HTTP request body, and select the cloud API interface based on the HTTP request body;

[0022] The cloud API interface generates control instructions corresponding to the device through the MQTT protocol.

[0023] Furthermore, generating a feedback result according to the control instruction includes:

[0024] Get control instructions;

[0025] Obtaining a protocol data packet by performing parsing and conversion operations on the control instructions;

[0026] The protocol data packet is transmitted to the microcontroller corresponding to the device through the serial communication protocol;

[0027] The microcontroller generates a trigger command by parsing the protocol data packet;

[0028] Determine whether the trigger command is completed;

[0029] Yes, obtain the device operating status and generate feedback results based on the device operating status;

[0030] No, do nothing.

[0031] Furthermore, generating feedback results according to the device operating status includes:

[0032] Get the device operating status;

[0033] Convert the equipment operation status into event status;

[0034] Encapsulate the event status to obtain a feedback data packet;

[0035] Parse and encapsulate the feedback data packet to obtain an MQTT message;

[0036] Distribute MQTT messages according to matching rules to obtain feedback results.

[0037] Furthermore, generating an alarm signal according to the feedback result includes:

[0038] Obtain feedback results, test cases, and environment change data;

[0039] Extract expected results from test cases;

[0040] Adjust expected results based on environmental change data and expected results generation;

[0041] Determine whether the feedback results exceed the expected results of the adjustment;

[0042] If yes, a test failure warning signal will be generated;

[0043] No, generate a test pass prompt.

[0044] This application compares the expected adjustment results with the feedback results. If the feedback results are consistent with the expected adjustment results, the automated test is considered to have passed; if they are inconsistent, the automated test is considered to have failed, and a warning signal is issued to facilitate subsequent investigation by relevant personnel. The expected results that are more in line with the actual situation of the test equipment are used for comparison, thereby improving the accuracy of the comparison results.

[0045] Furthermore, the step of generating and adjusting the expected result based on the environmental change data and the expected result includes:

[0046] Obtain data on environmental changes and expected outcomes;

[0047] The expected results and environmental change data are input into the result correction model to obtain adjusted expected results; the result correction model is constructed through an artificial intelligence model.

[0048] Furthermore, the result correction model is constructed by an artificial intelligence model, including:

[0049] Obtain certain historical expected results, historical environmental change data, and historical adjusted expected results;

[0050] Dividing a number of historical expected results, historical environmental change data, and historical adjusted expected results into training data, verification data, and test data; performing data preprocessing on the training data, verification data, and test data to obtain a training set, a verification set, and a test set;

[0051] Select an artificial intelligence model as the base model;

[0052] Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model;

[0053] By verifying the pre-trained model on the test set, we finally get the input expected results and environmental change data, and output the result correction model that adjusts the expected results.

[0054] A second aspect of the present invention provides an automated test system for refrigerator defrosting noise reduction applications, comprising: a data acquisition module, a data analysis module, and an early warning module; the data acquisition module is connected to the data analysis module; the data analysis module is connected to the early warning module;

[0055] The data acquisition module acquires task parameters, environmental data and equipment parameters through data acquisition equipment;

[0056] The data analysis module inputs task parameters, device parameters, and environmental data into a use case generation model to obtain a test case; the use case generation model is constructed using a machine learning model; a control instruction is generated based on the test case; a feedback result is generated based on the control instruction; and an alarm signal is generated based on the feedback result;

[0057] The early warning module: provides prompts according to the alarm signal.

[0058] Another aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an automated testing system for refrigerator defrosting and noise reduction applications according to the second aspect of the present invention.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] 1. This application realizes refrigerator defrosting and noise reduction tests through an automated process by generating test cases based on task parameters, environmental data and equipment parameters; generating control instructions based on the test cases; generating feedback results based on the control instructions; and generating alarm signals based on the feedback results. In addition, the application dynamically adjusts the test cases and expected results according to different test equipment and environmental changes of the equipment during the test process, thereby improving the efficiency of automated testing and the accuracy of result generation.

[0061] 2. This application obtains the task parameters, device parameters and environmental data corresponding to the device to be tested, and obtains the test cases corresponding to the test devices based on the pre-trained use case generation model. It can generate test cases for multiple devices at the same time, and conform to the actual situation of each device, providing solid data support for improving the accuracy and efficiency of subsequent test results.

[0062] 3. This application takes into account that the dynamic changes in the environment during device testing will cause a large deviation between the expected results and the feedback results corresponding to the test equipment. Therefore, a pre-trained result correction model is used to correct the expected results to obtain adjusted expected results, and then compared with the feedback results in the subsequent comparison process, so that the accuracy of the comparison can be improved, thereby improving the accuracy of the automated test. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0064] Figure 1 This is a flow chart of an automated testing method for refrigerator defrosting and noise reduction applications in this application;

[0065] Figure 2 Generate a flow chart for the alarm signal of this application;

[0066] Figure 3 This is a schematic diagram of the principle of an automated testing system for refrigerator defrosting and noise reduction applications in this application. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the embodiments described are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0068] See also Figure 1 The first embodiment of the present application provides an automated testing method for refrigerator defrosting noise reduction applications, comprising:

[0069] Obtain task parameters, environmental data, and device parameters; task parameters refer to the parameters required for testing the device, including turning on or off noise reduction, turning on or off defrost, etc.; environmental data refers to the environmental conditions of the device under test; and device parameters refer to the parameters of the device under test.

[0070] Input task parameters, device parameters, and environmental data into the use case generation model to generate test cases. The use case generation model is constructed using a machine learning model. A test case is a pre-planned test plan for the device under test, which includes expected results, etc.

[0071] Generate control instructions based on test cases; control instructions refer to control instructions issued to the device under test;

[0072] Generate feedback results according to control instructions; feedback results refer to test results obtained after the test is completed;

[0073] Generate an alarm signal based on the feedback result, including a test failure alarm signal, etc.

[0074] The use case generation model in this embodiment is constructed using a machine learning model, including:

[0075] Obtain several historical task parameters, historical equipment parameters, historical environment data, and historical test cases;

[0076] Several historical task parameters, historical equipment parameters, historical environment data, and historical test cases are divided into training data, verification data, and test data; data preprocessing is performed on the training data, verification data, and test data to obtain training sets, verification sets, and test sets; the ratio of the training set, verification set, and test set is set to 7:2:1;

[0077] Select a machine learning model as the base model; machine learning models include the FT-Transformer model, etc.

[0078] Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model;

[0079] By verifying the pre-trained model on the test set, we finally obtain the input task parameters, device parameters, and environmental data, and output the use case generation model for the test case.

[0080] Through the above steps, this embodiment proposes a test case generation scheme. By collecting multi-dimensional information such as task parameters, device parameters, and environmental data of the device to be tested, and using a pre-trained use case generation model for in-depth analysis and processing, the scheme accurately generates test cases that are highly adapted to the test equipment. This scheme has powerful parallel processing capabilities and can simultaneously generate test cases that meet the actual characteristics of multiple devices. It provides a solid and reliable data foundation for improving the accuracy of results and execution efficiency in subsequent testing processes, and has important theoretical value and practical significance in the field of testing technology.

[0081] In this embodiment, generating control instructions according to test cases includes:

[0082] Get test cases;

[0083] The test case is parsed to obtain several test steps and test parameters. The test case in this embodiment is stored in a YAML file to facilitate CI task reading and parameterized execution. When parsing the test case, it is parsed through the CI script.

[0084] Encapsulate the test parameters corresponding to several test steps to obtain an HTTP request body, and select the cloud API interface based on the HTTP request body. Encapsulation means organizing the test parameters to be passed into a request body according to the format required by the API documentation, such as JSON format or form data. The HTTP request body includes parameters such as the request method, request header, and timeout period.

[0085] The cloud API interface generates control instructions corresponding to the device through the MQTT protocol.

[0086] In this embodiment, generating a feedback result according to the control instruction includes:

[0087] Get control instructions;

[0088] A protocol data packet is obtained by parsing and converting the control instruction. The parsing and conversion operations refer to extracting the core parameters of the control instruction according to a preset communication protocol, such as a custom frame format or JSON parsing rules, and converting them into a protocol data packet recognizable by the serial port. Common formats include fixed frame structures and ASCII encoded instructions.

[0089] The protocol data packet is transmitted to the microcontroller corresponding to the device through the serial communication protocol; the serial communication protocol includes UART or RS-485;

[0090] The microcontroller generates a trigger command by parsing the protocol data packet; the trigger command refers to the trigger operation on the device;

[0091] Determine whether the trigger command is completed;

[0092] Yes, obtain the device operating status and generate feedback results based on the device operating status;

[0093] No, do nothing.

[0094] In this embodiment, the feedback result is generated according to the device operation status, including:

[0095] Get the device operating status; the device operating status refers to the corresponding parameters that need to be tested on the test equipment;

[0096] Convert the equipment operation status into event status;

[0097] Encapsulate the event status to obtain a feedback data packet; the encapsulation operation includes JSON or binary format;

[0098] Parse and encapsulate the feedback data packet to obtain an MQTT message. An MQTT message is the basic unit for transmitting data in the MQTT protocol and includes parameters such as topic, payload, quality of service, and retained messages.

[0099] The MQTT message is distributed according to the matching rules to obtain the feedback result; the feedback result can be received by multiple recipients.

[0100] See also Figure 2 In this embodiment, generating an alarm signal according to the feedback result includes:

[0101] Obtain feedback results, test cases, and environmental change data; environmental change data refers to data on changes in environmental parameters during the test process;

[0102] Extract expected results from test cases;

[0103] Generate adjusted expected results based on environmental change data and expected results; the adjusted expected results are expected results adjusted under the environmental change data;

[0104] Determine whether the feedback results exceed the expected results of the adjustment; that is, whether the feedback results exceed all situations that can be included in the expected results of the adjustment;

[0105] If yes, a test failure warning signal will be generated;

[0106] No, generate a test pass prompt.

[0107] Through the above steps, this embodiment constructs a set of automated test result judgment mechanisms. During the test operation, the expected results after dynamic adjustment are accurately compared with the actual feedback results. If the feedback results are consistent with the adjusted expected results, the automated test is judged to have passed. Otherwise, the test is judged to have failed and a warning signal is immediately triggered. Based on the real-time characteristics and operating status of the test equipment, the expected results that are more in line with the actual situation can be selected for comparison, which effectively improves the accuracy of the comparison results and provides a solid guarantee for the reliable control of test quality.

[0108] In this embodiment, the adjusted expected result is generated based on the environmental change data and the expected result, including:

[0109] Obtain data on environmental changes and expected outcomes;

[0110] The expected results and environmental change data are input into the result correction model to obtain adjusted expected results; the result correction model is constructed through an artificial intelligence model.

[0111] The result correction model in this embodiment is constructed using an artificial intelligence model, including:

[0112] Obtain certain historical expected results, historical environmental change data, and historical adjusted expected results;

[0113] Several historical expected results, historical environmental change data, and historical adjusted expected results are divided into training data, verification data, and test data; data preprocessing is performed on the training data, verification data, and test data to obtain training sets, verification sets, and test sets; the ratio of the training set, verification set, and test set is set to 7:2:1;

[0114] Select an artificial intelligence model as the basic model; artificial intelligence models include BP models, etc.

[0115] Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model;

[0116] By verifying the pre-trained model on the test set, we finally get the input expected results and environmental change data, and output the result correction model that adjusts the expected results.

[0117] In the field of equipment automation testing, the significant deviation between the expected results and feedback results of test equipment caused by dynamic changes in the environment has always been a key factor affecting test accuracy. This embodiment conducts research on this issue. By constructing and applying a pre-trained result correction model, the original expected results are accurately corrected to generate adjusted expected results that are more in line with the actual test environment. In the subsequent testing process, the adjusted expected results are used as a comparison benchmark and compared with the actual feedback results, which effectively weakens the interference caused by dynamic changes in the environment and significantly improves the accuracy of the comparison results, thereby fundamentally improving the overall accuracy of equipment automation testing and providing strong support for technological development in related fields.

[0118] See also Figure 3 The second embodiment of the present application provides an automated test system for refrigerator defrosting noise reduction applications, comprising: a data acquisition module, a data analysis module, and an early warning module; the data acquisition module is communicatively connected to the data analysis module; the data analysis module is communicatively connected to the early warning module;

[0119] Data acquisition module: obtains task parameters, environmental data and equipment parameters through data acquisition equipment; data acquisition equipment includes several sensors, etc.

[0120] Data analysis module: Input task parameters, equipment parameters, and environmental data into the use case generation model to obtain test cases; the use case generation model is constructed through a machine learning model; control instructions are generated based on the test cases; feedback results are generated based on the control instructions; and alarm signals are generated based on the feedback results.

[0121] Early warning module: Prompts based on alarm signals.

[0122] Another embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements an automated testing system for refrigerator defrosting and noise reduction according to the second aspect of the present invention.

[0123] The working principle of this application is as follows: by obtaining task parameters, environmental data and equipment parameters; inputting task parameters, equipment parameters and environmental data into a use case generation model to obtain a test case; generating control instructions according to the test case; generating feedback results according to the control instructions; generating an alarm signal according to the feedback results, and implementing the refrigerator defrosting and noise reduction test through an automated process, and dynamically adjusting the test cases and expected results according to different test equipment and environmental changes of the equipment during the test process, thereby improving the efficiency of automated testing and the accuracy of result generation, and avoiding the problem that the existing technology often uses manual review of relevant logs and combination of some test tools to complete the testing work, resulting in a waste of a lot of manpower and material resources, and leading to the low efficiency of the automated testing method.

[0124] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. An automated testing method for refrigerator defrosting noise reduction, characterized in that: include: Obtain task parameters, environmental data, and equipment parameters; Inputting task parameters, equipment parameters and environmental data into the use case generation model to obtain test cases; The use case generation model is constructed through a machine learning model; Generate control instructions based on test cases; Generate feedback results according to control instructions; Generate an alarm signal based on the feedback results.

2. The automated test method for refrigerator defrosting noise reduction according to claim 1, characterized in that: The use case generation model is constructed using a machine learning model, including: Obtain several historical task parameters, historical equipment parameters, historical environment data, and historical test cases; Divide a number of historical task parameters, historical equipment parameters, historical environment data, and historical test cases into training data, verification data, and test data; perform data preprocessing on the training data, verification data, and test data to obtain a training set, a verification set, and a test set; Select a machine learning model as the base model; Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain the input task parameters, device parameters, and environmental data, and output the use case generation model for the test case.

3. The automated test method for refrigerator defrosting noise reduction according to claim 1, characterized in that: Generating control instructions according to the test case includes: Get test cases; Parse the test case to obtain several test steps and test parameters; Encapsulate the test parameters corresponding to several test steps to obtain an HTTP request body, and select the cloud API interface based on the HTTP request body; The cloud API interface generates control instructions corresponding to the device through the MQTT protocol.

4. The automated test method for refrigerator defrosting noise reduction according to claim 1, characterized in that: Generating a feedback result according to the control instruction includes: Get control instructions; Obtaining a protocol data packet by performing parsing and conversion operations on the control instructions; The protocol data packet is transmitted to the microcontroller corresponding to the device through the serial communication protocol; The microcontroller generates a trigger command by parsing the protocol data packet; Determine whether the trigger command is completed; Yes, obtain the device operating status and generate feedback results based on the device operating status; No, do nothing.

5. The automated test method for refrigerator defrosting noise reduction according to claim 4, characterized in that: Generating feedback results according to the device operating status includes: Get the device operating status; Convert the equipment operation status into event status; Encapsulate the event status to obtain a feedback data packet; Parse and encapsulate the feedback data packet to obtain an MQTT message; Distribute MQTT messages according to matching rules to obtain feedback results.

6. The automated test method for refrigerator defrosting noise reduction according to claim 1, characterized in that: Generating an alarm signal according to the feedback result includes: Obtain feedback results, test cases, and environment change data; Extract expected results from test cases; Adjust expected results based on environmental change data and expected results generation; Determine whether the feedback results exceed the expected results of the adjustment; If yes, a test failure warning signal will be generated; No, generate a test pass prompt.

7. The automated test method for refrigerator defrosting noise reduction according to claim 6, characterized in that: Generating and adjusting the expected result based on the environmental change data and the expected result includes: Obtain data on environmental changes and expected outcomes; The expected results and environmental change data are input into the result correction model to obtain adjusted expected results; the result correction model is constructed through an artificial intelligence model.

8. The automated test method for refrigerator defrosting noise reduction according to claim 7, characterized in that: The result correction model is constructed through an artificial intelligence model, including: Obtain certain historical expected results, historical environmental change data, and historical adjusted expected results; Dividing a number of historical expected results, historical environmental change data, and historical adjusted expected results into training data, verification data, and test data; performing data preprocessing on the training data, verification data, and test data to obtain a training set, a verification set, and a test set; Select an artificial intelligence model as the base model; Train the basic model through each training set, and adjust the learning rate and other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input expected results and environmental change data, and output the result correction model that adjusts the expected results.

9. An automated test system for refrigerator defrost noise reduction, applied to the automated test method for refrigerator defrost noise reduction according to any one of claims 1 to 8, characterized in that: include: Data collection module, data analysis module and early warning module; The data acquisition module is connected to the data analysis module; the data analysis module is connected to the early warning module; The data acquisition module acquires task parameters, environmental data and equipment parameters through data acquisition equipment; The data analysis module inputs task parameters, device parameters and environmental data into the use case generation model to obtain test cases; The use case generation model is constructed through a machine learning model; a control instruction is generated according to the test case; a feedback result is generated according to the control instruction; and an alarm signal is generated according to the feedback result; The early warning module: provides prompts according to the alarm signal.

10. A computer-readable storage medium, applied to the automated test system for refrigerator defrosting and noise reduction according to claim 9, characterized in that: The computer-readable storage medium stores a computer program.

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