Refrigerating machine service life acceleration experiment method and device, computer equipment and storage medium

Abstract

The invention relates to a refrigerator life acceleration experiment method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring basic experiment data sets of a plurality of target devices, and preprocessing each basic experiment data set to obtain an optimized experiment data set; classifying target equipment based on the optimization experiment data set to obtain a plurality of categories of the target equipment; and a preset number of experimental devices are extracted from all kinds of target devices, a life acceleration experiment is carried out on the experimental devices based on life acceleration experimental variables, and the life acceleration experimental variables at least comprise the experimental environment temperature and the refrigerator rotating speed. The service life condition of batch equipment can be comprehensively reflected, the experiment process is faster, and good timeliness is achieved.

Description

Technical Field

[0001] The present application relates to the field of instrument control technology, and in particular to a refrigerator life acceleration test method, device, computer equipment, and storage medium. Background Art

[0002] The long life of a Stirling refrigerator is the key to its application. The existing accelerated life test method for Stirling refrigerators mainly verifies the life of Stirling refrigerators by sampling batches of machines. However, if there are many machines produced in the same batch and only a few are sampled for sampling, the sampling results will be highly random and cannot fully reflect the life of the batch of refrigerators. Summary of the invention

[0003] Based on this, it is necessary to provide a refrigerator life acceleration test method, device, computer equipment, computer readable storage medium and computer program product that can more comprehensively reflect the life of batch refrigerators in response to the above technical problems.

[0004] In a first aspect, the present application provides a refrigerator life acceleration test method, comprising: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0005] In one embodiment, preprocessing each of the basic experimental data sets to obtain an optimized experimental data set includes: Performing anomaly detection on the basic experimental data set to obtain abnormal samples in the basic experimental data set; Eliminate and fill the abnormal samples to obtain a corrected experimental data set; The modified experimental data set is subjected to a standard transformation to obtain the optimized experimental data set.

[0006] In one embodiment, performing anomaly detection on the basic experimental data set to obtain anomaly samples in the basic experimental data set includes: Perform anomaly detection on the basic experimental data set based on the box plot to obtain outlier data in the basic experimental data set; The outlier data is determined as the abnormal sample. In one embodiment, removing and filling the abnormal samples to obtain a corrected experimental data set includes: Eliminate the abnormal samples to obtain a valid sample data set; Obtaining a valid sample feature set based on the valid sample data set; A feature value is randomly extracted from the valid sample feature set, and the position of the abnormal sample is filled based on the feature value.

[0007] In one embodiment, the target device is classified based on the optimization experiment data set to obtain multiple categories of the target device, including: Dividing the optimization experimental data set into several classes based on a clustering algorithm; The target devices corresponding to the same class data are determined as target devices of one category.

[0008] In one embodiment, the performing of the life acceleration experiment on the experimental device based on the life acceleration experiment variable includes: Setting the experimental environment temperature to a preset temperature, wherein the preset temperature is higher than the environment temperature when the target device is operating normally; Setting the speed of the refrigerator to the maximum speed of the target device; The life acceleration test is performed on the test equipment based on the current life acceleration test variables and test cycle.

[0009] In a second aspect, the present application also provides a refrigerator life acceleration test device, comprising: A preprocessing module is used to obtain basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; A classification module, used to classify the target device based on the optimization experiment data set to obtain multiple categories of the target device; The experimental module is used to extract a preset number of experimental devices from target devices of each category, and perform life acceleration experiments on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables include at least the experimental environment temperature and the refrigerator speed.

[0010] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0011] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0012] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0013] The above-mentioned refrigerator life accelerated test method, device, computer equipment, computer readable storage medium and computer program product can reflect the life of refrigerators in the batch as comprehensively as possible by classifying refrigerators in the same batch and then sampling the classified refrigerators separately. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A diagram showing an application environment of a refrigerator life acceleration test method in one embodiment; Figure 2 A schematic flow chart of a sub-step of a refrigerator life acceleration test method in one embodiment; Figure 3 A schematic diagram of an abnormality detection method in one embodiment; Figure 4 A structural block diagram of a refrigerator life acceleration test device in one embodiment; Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] In the production line of Stirling refrigerators, even machines produced in the same batch can have different performances due to slight differences in the machines themselves. When spot-checking the performance of batch machines, only a few machines are often selected from a large number of machines, which leads to a large randomness in the spot check. For example, if a lifespan prediction is to be made, and the machines that are picked happen to have a short lifespan, it does not mean that the lifespan of refrigerators in this batch is short. Based on this, the present application proposes to classify refrigerators based on experimental data of refrigerators, and then spot-check machines of different categories to more comprehensively reflect the performance of the batch of machines.

[0018] In an exemplary embodiment, Figure 1 As shown, a refrigerator life acceleration test method is provided, including the following steps 102 to 103.

[0019] Step 101, obtaining basic experimental data sets of multiple target devices, and preprocessing each of the basic experimental data sets to obtain an optimized experimental data set; First, obtain some experimental data for each target device in the batch. In order to speed up the experimental process and improve the test efficiency, priority is given to obtaining data that is easy to obtain and can be quickly obtained. For example, data such as incoming materials, production, and room temperature running-in test current, speed data, and coke temperature data can be selected. The basic experimental data set may have data anomalies or loss, so the basic experimental data set needs to be preprocessed to facilitate subsequent classification.

[0020] Step 102, classifying the target devices based on the optimization experiment data set to obtain multiple categories of the target devices; In this step, the target devices produced in batches are classified and multiple target devices are divided into several categories for subsequent random inspection.

[0021] Step 103 , extracting a preset number of experimental devices from the target devices of each category, and performing an accelerated life test on the experimental devices based on accelerated life test variables, wherein the accelerated life test variables at least include an experimental environment temperature and a refrigerator speed.

[0022] A preset number of target devices are extracted from each category and determined as experimental devices. Then, the life acceleration experiment is performed on the experimental devices based on the life acceleration experiment variables. For example, in order to speed up the testing process of the experimental devices, the life acceleration experiment variables will put the experimental devices in a more stringent environment and state. The specific experimental variables will be discussed later.

[0023] The above-mentioned accelerated life test method for refrigerators proposes a method for classifying machines based on test data and experimentally verifying the classified machines. For accelerated experiments, it is proposed to add life accelerated test variables to life test samples to accelerate the failure of life test machines to quickly obtain experimental data, which can quickly complete the iteration of new products and has good economy and timeliness.

[0024] In an exemplary embodiment, Figure 2 As shown, step 101 includes steps 201 to 203. Among them: Step 201, performing anomaly detection on the basic experimental data set to obtain abnormal samples in the basic experimental data set; In an exemplary embodiment, step 201 includes: performing anomaly detection on the basic experimental data set based on a box plot to obtain outlier data in the basic experimental data set; and determining the outlier data as the abnormal sample.

[0025] Specifically, a box plot is also called a box-whisker plot, box-line plot, or box diagram. It can be used to reflect the center position and spread of one or more groups of continuous quantitative data. It is named because of its box-like shape. Figure 3 , after a set of data is arranged in order from small to large, the number of the four equal parts of the set of data is called quartiles. The first quartile (Q1), the second quartile (Q2, also called the "median") and the third quartile (Q3) are respectively equal to the 25th, 50th and 75th percentile numbers of all the values ​​in the sample arranged from small to large. The difference between the third quartile and the first quartile is also called the interquartile range (IQR).

[0026] In the box plot, there is a line in the middle of the box, which represents the median of the data. The upper and lower bases of the box are the upper quartile (Q3) and lower quartile (Q1) of the data, which means that the box contains 50% of the data. Therefore, the height of the box reflects the degree of fluctuation of the data to a certain extent. The upper and lower bounds of the box plot represent the maximum and minimum values ​​of the group of data. The points outside the box, that is, the outliers, can represent abnormal values ​​in the data.

[0027] Step 202, removing and filling the abnormal samples to obtain a corrected experimental data set; In an exemplary embodiment, step 202 includes: removing the abnormal samples to obtain a valid sample data set; obtaining a valid sample feature set based on the valid sample data set; randomly extracting feature values ​​from the valid sample feature set, and filling the position of the abnormal sample based on the feature values.

[0028] Once the outliers in the data, i.e., the abnormal samples in the data set, are identified, the abnormal samples can be removed. However, the effective sample data set after removal has vacancies caused by the removal of outliers, and the missing values ​​need to be filled. At this time, a feature set is formed based on the effective sample data set, and the rows in the formed feature set represent each sample, and the columns represent a certain feature. When there is a missing value in a sample, a feature value is randomly extracted from the column where the missing value is located to fill the missing value.

[0029] Step 203: performing a standardization transformation on the modified experimental data set to obtain the optimized experimental data set.

[0030] In practical application problems, different variables often have different dimensions and magnitudes. For example, the value range of one variable is 0 to 1, while the value range of another variable is 100 to 10,000. If these data are used directly for machine learning, the features with a large value range will have a greater impact on the model, which may cause the model to fail to correctly learn the information of other features. Through data standardization, the value ranges of all features can be unified to the same scale, so that the model can fairly consider the information of each feature.

[0031] Therefore, the present application transforms the modified experimental data set by standardization to obtain an optimized experimental data set with a mean of 0 and a variance of 1.

[0032] Specifically, if the modified experimental data set is , based on formula 1, the modified experimental data set is transformed, formula 1: in, represents the i-th sample in the modified experimental data set, represents the i-th sample in the transformed optimized experimental data set, Indicates the average value of each sample in the modified experimental data set, and n indicates the number of samples in the modified experimental data set. s is calculated based on formula 2: After transformation, we get the optimized experimental data set , Represents the nth sample in the optimization experiment dataset.

[0033] In an exemplary embodiment, step 102 includes: The optimization experiment data set is divided into several classes based on a clustering algorithm; and the target devices corresponding to the same class of data are determined as target devices of one category.

[0034] When multiple data need to be classified, clustering algorithm is a better solution. Commonly used clustering algorithms include K-Means clustering algorithm, DBSCAN clustering algorithm, hierarchical clustering, etc. In this embodiment, the classification process of this embodiment is introduced by taking K-Means clustering algorithm as an example.

[0035] The core idea of ​​K-Means clustering is to distinguish each data by measuring the distribution of the data in the optimization experiment data set in the geometric space. That is, the samples in the original optimization experiment data set are re-divided into the most similar K classes.

[0036] Step 301: Given an optimized experimental data set, the size of the data set is n, and the number of clusters is set to K. Then, K random samples in the sample can be used as initial cluster centers.

[0037] Step 302, take samples from the optimization experiment data set in turn, calculate the distances between all samples and the K cluster centers initially selected, and divide the n samples into the nearest center points according to the distance results. For example, in this embodiment, the Euclidean distance is used for measurement. The Euclidean distance can be found in the following formula, Formula 3: in represents the cluster center of the kth cluster, represents the currently taken sample, i represents the i-th feature in the sample, specifically, Representation sample The i-th feature of Representation sample The i-th feature of Indicates the sample currently taken out With cluster center The Euclidean distance of .

[0038] Step 303, calculate the average value of each category of the above classified samples, and use the mean point obtained by the calculation result as the new cluster center of each category.

[0039] Step 304, compare the current center with the K cluster centers calculated last time to see if they are the same. If they are the same, the K-Means algorithm ends. Otherwise, repeat steps 302 to 303 until the cluster center no longer changes or the change is less than a specified threshold, and the K-Means algorithm ends.

[0040] The K-Means algorithm can ensure that the samples of each class in the clustering result are relatively similar, while the differences between different classes are relatively large. Determining the target devices corresponding to the same class data as the target devices of one category can achieve a better classification effect on batch devices. When extracting later, 1 or 2 refrigerators can be extracted from each category for life verification.

[0041] In an exemplary embodiment, step 103 includes: The experimental environment temperature is set to a preset temperature, wherein the preset temperature is higher than the ambient temperature of the target device during normal operation; the refrigerator speed is set to the maximum speed of the target device; and the experimental device is subjected to an accelerated life test based on current accelerated life test variables and test cycles.

[0042] As mentioned above, in order to accelerate the life test, it is necessary to use more stringent experimental variables than the normal working state to accelerate the aging of the experimental equipment. In this embodiment, two life acceleration experimental variables are given, namely, the ambient temperature and the refrigerator speed. The higher the ambient temperature, the faster the refrigerator ages; the faster the refrigerator speed, the faster the refrigerator wears out. Therefore, by increasing the ambient temperature and the refrigerator speed, the aging of the refrigerator can be effectively accelerated, speeding up the experimental process.

[0043] In a specific embodiment, the experimental temperature control box can be stably set to a constant temperature of 71°C to increase the ambient temperature of the refrigerator life experiment and accelerate the aging of the machine; by setting the refrigerator electronic control program, the refrigerator speed is adjusted to the maximum speed to accelerate the friction efficiency of the internal parts of the refrigerator. It should be noted that for the sake of control variables, the same accelerated test variables are used for refrigerators of different categories in step 102 to ensure that the final experimental results can effectively reflect the relationship between the life and category of the refrigerator. After testing, after using the above-mentioned life acceleration test variables, the original life test cycle can be shortened by 3-5 times, which has good timeliness.

[0044] In summary, the refrigerator life accelerated test method provided in the present application can effectively classify refrigerators through easily obtained data such as incoming materials, production, and normal temperature running-in test current, speed data, coke temperature data, etc., with high accuracy, and provides a good basis for the selection of refrigerator life samples; for accelerated experiments, it is proposed to accelerate the failure of the life test machine by adding life accelerated test variables to the life test samples, and the experimental data can be obtained quickly, which can quickly complete the iteration of new products and has good economy and timeliness in actual production.

[0045] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0046] Based on the same inventive concept, the embodiment of the present application also provides a refrigerator life acceleration test device for implementing the refrigerator life acceleration test method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more refrigerator life acceleration test device embodiments provided below can refer to the limitations of the refrigerator life acceleration test method above, and will not be repeated here.

[0047] In an exemplary embodiment, Figure 4 As shown, a refrigerator life acceleration test device 400 is provided, comprising: a pre-processing module 401, a classification module 402 and an experimental module 403, wherein: A preprocessing module 401 is used to obtain basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; A classification module 402, configured to classify the target device based on the optimization experiment data set to obtain multiple categories of the target device; The experimental module 403 is used to extract a preset number of experimental devices from the target devices of each category, and perform an accelerated life experiment on the experimental devices based on accelerated life experiment variables, wherein the accelerated life experiment variables include at least the experimental environment temperature and the refrigerator speed.

[0048] In an exemplary embodiment, the preprocessing module 401 is further used to: Performing anomaly detection on the basic experimental data set to obtain abnormal samples in the basic experimental data set; Eliminate and fill the abnormal samples to obtain a corrected experimental data set; The modified experimental data set is subjected to a standard transformation to obtain the optimized experimental data set.

[0049] In an exemplary embodiment, the preprocessing module 401 is further used to: Perform anomaly detection on the basic experimental data set based on the box plot to obtain outlier data in the basic experimental data set; The outlier data is determined as the abnormal sample.

[0050] In an exemplary embodiment, the preprocessing module 401 is further used to: Eliminate the abnormal samples to obtain a valid sample data set; Obtaining a valid sample feature set based on the valid sample data set; A feature value is randomly extracted from the valid sample feature set, and the position of the abnormal sample is filled based on the feature value.

[0051] In an exemplary embodiment, the classification module 402 is further configured to: Dividing the optimization experimental data set into several classes based on a clustering algorithm; The target devices corresponding to the same class data are determined as target devices of one category.

[0052] In an exemplary embodiment, the experiment module 403 is further used to: Setting the experimental environment temperature to a preset temperature, wherein the preset temperature is higher than the environment temperature when the target device is operating normally; Setting the speed of the refrigerator to the maximum speed of the target device; The life acceleration test is performed on the test equipment based on the current life acceleration test variables and test cycle.

[0053] Each module in the above-mentioned refrigerator life acceleration test device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0054] The refrigerator life acceleration test device provided in the present application can effectively classify refrigerators through easily obtained data such as incoming materials, production, and normal temperature running-in test current, speed data, coke temperature data, etc., with high accuracy, and provides a good basis for the selection of refrigerator life samples; for accelerated experiments, it is proposed to accelerate the failure of the life test machine by adding life acceleration test variables to the life test samples, quickly obtain experimental data, and quickly complete the iteration of new products, which has good economy and timeliness in actual production.

[0055] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for accelerating the life of a refrigerator is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0056] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0057] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0058] The computer equipment provided in the present application can effectively classify refrigerators through easily obtained data such as incoming materials, production, and normal temperature running-in test current, speed data, coke temperature data, etc., with high accuracy, providing a good basis for the selection of refrigerator life samples; for accelerated experiments, it is proposed to add life acceleration experiment variables to the life experiment samples to accelerate the failure of the life experiment machine, quickly obtain experimental data, and quickly complete the iteration of new products, which has good economy and timeliness in actual production.

[0059] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0060] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

[0061] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0062] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0063] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A refrigerator life acceleration test method, characterized in that: The method comprises: Acquire basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; Classifying the target device based on the optimized experimental data set to obtain multiple categories of the target device; A preset number of experimental devices are respectively extracted from target devices of each category, and life acceleration experiments are performed on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables at least include experimental environment temperature and refrigerator speed.

2. The refrigerator life acceleration test method according to claim 1, characterized in that: Preprocessing each of the basic experimental data sets to obtain an optimized experimental data set includes: Performing anomaly detection on the basic experimental data set to obtain abnormal samples in the basic experimental data set; Eliminate and fill the abnormal samples to obtain a corrected experimental data set; The modified experimental data set is subjected to a standard transformation to obtain the optimized experimental data set.

3. The refrigerator life acceleration test method according to claim 2, characterized in that: The performing anomaly detection on the basic experimental data set to obtain anomaly samples in the basic experimental data set includes: Perform anomaly detection on the basic experimental data set based on the box plot to obtain outlier data in the basic experimental data set; The outlier data is determined as the abnormal sample.

4. The refrigerator life acceleration test method according to claim 3, characterized in that: The abnormal samples are removed and filled to obtain a corrected experimental data set, including: Eliminate the abnormal samples to obtain a valid sample data set; Obtaining a valid sample feature set based on the valid sample data set; A feature value is randomly extracted from the valid sample feature set, and the position of the abnormal sample is filled based on the feature value.

5. The refrigerator life acceleration test method according to claim 1, characterized in that: The target device is classified based on the optimization experiment data set to obtain multiple categories of the target device, including: Dividing the optimization experimental data set into several classes based on a clustering algorithm; The target devices corresponding to the same class data are determined as target devices of one category.

6. The refrigerator life acceleration test method according to claim 1, characterized in that: The method of performing an accelerated life test on the experimental device based on the accelerated life test variables includes: Setting the experimental environment temperature to a preset temperature, wherein the preset temperature is higher than the environment temperature when the target device is operating normally; Setting the speed of the refrigerator to the maximum speed of the target device; The life acceleration test is performed on the test equipment based on the current life acceleration test variables and test cycle.

7. A refrigerator life acceleration test device, characterized in that: The device comprises: A preprocessing module is used to obtain basic experimental data sets of multiple target devices, and preprocess each of the basic experimental data sets to obtain an optimized experimental data set; A classification module, used to classify the target device based on the optimization experiment data set to obtain multiple categories of the target device; The experimental module is used to extract a preset number of experimental devices from target devices of each category, and perform life acceleration experiments on the experimental devices based on life acceleration experiment variables, wherein the life acceleration experiment variables include at least the experimental environment temperature and the refrigerator speed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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