Method for detecting degree of freshness of article in refrigeration device, refrigeration device and computer program product
By directly processing refrigerator image data using a multimodal pre-trained large model to obtain item features and location information, this technology solves the accuracy and cost problems in detecting the freshness of items inside refrigerators in existing technologies, and achieves efficient and robust freshness detection.
Patent Information
- Application Number
- CN202411171579.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-23
- Publication Date
- 2026-03-03
AI Technical Summary
In existing technologies, spectral methods and odor detection methods suffer from high costs or low accuracy when detecting the freshness of items in refrigerators. Deep learning models require a large number of image samples and manual annotations for detecting the freshness of items, which limits their application.
By using a multimodal pre-trained large model to directly process image data, the feature and location information of items can be obtained. The storage time of items can be determined by feature similarity and location deviation, and the freshness can be evaluated, eliminating the need for model training and manual annotation.
It achieves low-tech and high-accuracy detection of the freshness of items in the refrigerator, improves detection efficiency and robustness, and adapts to changes in different item categories.
Smart Images
Figure CN121600296A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of household appliances, and more particularly to a method for detecting the freshness of articles in a refrigeration device, a refrigeration device, and a computer program product for at least assisting in implementing the method according to this application. Background Technology
[0002] In existing technologies, common methods for detecting the freshness of items inside refrigerators include spectroscopy, odor detection, and machine vision.
[0003] Spectroscopy has high accuracy, but the high cost of the required spectrometers limits its widespread application in the home appliance industry.
[0004] Odor detection methods, such as using VOC gas sensors to detect the composition and concentration of volatile organic compounds in the refrigerator's storage space to identify the freshness of items, have a low accuracy rate when multiple categories of items are stored in the refrigerator. This is due to the interaction between the different gases produced by different items.
[0005] Machine vision methods typically use deep learning models to process images of items captured by cameras installed inside refrigerators to detect the freshness of goods. However, because images of items inside refrigerators are easily affected by factors such as lighting, shooting angle, and occlusion, traditional image processing algorithms have poor robustness in detecting the freshness of items under different refrigerator internal environmental conditions. Furthermore, most current deep learning models use fully supervised training methods to learn freshness detection for different categories of items. This requires collecting a large number of image samples for each item category, manually labeling these samples, and retraining the model for each new item category. These factors significantly limit the application of deep learning models for freshness detection.
[0006] Therefore, how to detect the freshness of items in a refrigerator with low technical cost and high accuracy has become a technical challenge that needs to be solved. Summary of the Invention
[0007] The purpose of embodiments of this application is to provide a method for detecting the freshness of articles in a refrigeration device, a refrigeration device, and a computer program product for at least assisting in implementing the method according to this application, so as to at least solve some of the problems in the prior art.
[0008] According to a first aspect of this application, embodiments of this application provide a method for detecting the freshness of articles in a refrigeration device, the method comprising:
[0009] - Step S1: Based on the pre-given item category information and image data of the items to be detected in the refrigeration equipment at various times, obtain the feature information of each item belonging to each item category at each time; and
[0010] - Step S2: Determine the storage time of each item by comparing the feature information of each item belonging to the same item category at each time. The storage time is used to identify the freshness of the item.
[0011] The concept of this application also includes: utilizing a multimodal pre-trained large model to directly process image data of each frame of an item without any model adjustments, in order to obtain time-related feature information of each item. By analyzing the feature information, the storage time of each item can be determined, and then the freshness of each item can be evaluated based on the storage time. This scheme fully utilizes the generalization ability of the multimodal pre-trained large model for image object detection, not only possessing the high robustness of deep learning, but also eliminating the technically costly steps of data collection, manual annotation, and model training required by commonly used fully supervised training methods for deep neural networks, thereby effectively improving the efficiency of detecting the freshness of different categories of items in refrigeration equipment.
[0012] According to an optional embodiment of this application, the method may further include step S11: obtaining the location information of each item belonging to each item category at each time based on pre-given item category information and image data of the item to be detected in the refrigeration equipment at each time. Thus, the location information of each item and its associated time information can be extracted from each frame of image data, thereby laying the foundation for determining the storage time and location changes of the items.
[0013] According to another optional embodiment of this application, the item category information and image data of the items at various times can be input into a multimodal pre-trained large model. This allows for the determination of the location information and / or image information of each item belonging to each item category at each time, and the determination of feature information of each item at each time based on the image information of each item at each time. This fully utilizes the generalization ability of the multimodal pre-trained large model for image object detection, quickly and accurately determining the location information and / or image information of each item at each time. In particular, the item category information input into the multimodal pre-trained large model can be changed according to actual needs without requiring any training or fine-tuning of the model.
[0014] According to another optional embodiment of this application, feature information of each item at each time step can be obtained using a convolutional neural network based on the image information of each item at each time step. Here, the efficient processing capability of the convolutional neural network for image classification tasks is fully utilized to quickly and accurately extract the feature information of each item from the image information.
[0015] According to another optional embodiment of this application, step S2 may include:
[0016] - Step S21: Calculate the similarity between the first feature information of each item belonging to the same item category at the first time and other feature information at one or more other times, wherein the other times are different from the first time;
[0017] - Step S22: Determine whether the similarity exceeds a preset similarity threshold;
[0018] - Step S23: If the similarity exceeds a preset similarity threshold, then the item associated with the first feature information obtained based on image data at the first time point and the item associated with the other feature information obtained based on image data at other times points are marked as the same item, and the storage time of the item is determined based on the first time point and the other times points, the storage time being used to identify the freshness of the item; and
[0019] - Step S24: If the similarity does not exceed the preset similarity threshold, then the item associated with the first feature information obtained based on the image data at the first moment is marked as an item that has been taken out.
[0020] Here, steps S21 to S24 can be executed iteratively until all items belonging to the same item category, obtained based on the image data from the first moment, have been marked. In this way, the storage time of items can be accurately and efficiently determined by utilizing the similarity of the feature information of items at different times, and the retrieved items can be quickly filtered out.
[0021] According to another optional embodiment of this application, step S2 may further include:
[0022] - Step S25: After all items belonging to the same item category obtained based on the image data at the first moment have been marked, determine whether there are any unmarked items among the items belonging to the same item category obtained based on image data at other moments; and
[0023] - Step S26: If there is an unmarked item, mark the item as a newly placed item and store the other moments associated with the item.
[0024] Here, newly placed items can be quickly and accurately identified, and their characteristic information and / or location information, along with other associated times, can be stored. These other times are marked as the initial times of the newly placed items. Based on subsequently acquired image data about the items, the storage time of the items can be updated, and the freshness of the items can be assessed using the negative correlation between the storage time of the item category and its freshness.
[0025] According to another optional embodiment of this application, the feature information of the item may include a first feature value vector of the item at a first time moment and other feature value vectors of the item at one or more other times moment moment, wherein the other times moment moment moment moment is different from the first time moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment momentary ...
[0026] According to another optional embodiment of this application, step S2 may further include:
[0027] - Step S27: After calculating the storage time of the item, determine whether the positional deviation between the first position information of the item at a first moment and other position information at one or more other moments exceeds a preset positional deviation threshold; and
[0028] - Step S28: If the deviation exceeds a preset position deviation threshold, the item is marked as a moved item.
[0029] This method allows for the quick and timely marking of items whose positions have changed, avoiding interference with item identification due to changes in item position, and laying the foundation for the execution of various functions of refrigeration equipment (such as food preservation function, food position indication, etc.).
[0030] According to a second aspect of this application, a refrigeration device is provided, which may include the following components:
[0031] - An image acquisition unit, configured to acquire image data of the item to be detected in the refrigeration device at various times; and
[0032] - A control unit configured to implement the method according to this application.
[0033] According to another optional embodiment of this application, the refrigeration device is, for example, a refrigerator.
[0034] According to a third aspect of this application, a computer program product, such as a computer-readable program carrier, is provided, comprising or storing computer program instructions that, when executed by a processor, at least assist in implementing the steps of the method described in this application. Attached Figure Description
[0035] The principles, features, and advantages of this application will be better understood below with reference to the accompanying drawings. The drawings include:
[0036] Figure 1 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to an exemplary embodiment of this application is provided.
[0037] Figure 2 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is provided.
[0038] Figure 3 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is provided.
[0039] Figure 4 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is provided.
[0040] Figure 5 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application; and
[0041] Figure 6 A schematic block diagram of a refrigeration device according to an exemplary embodiment of this application is shown.
[0042] List of reference numerals
[0043] 1 Refrigeration equipment
[0044] 11 Image Acquisition Unit
[0045] 12 control units Detailed Implementation
[0046] To make the technical problems to be solved, the technical solutions, and the beneficial technical effects of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and several exemplary embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit the scope of protection of this application.
[0047] Figure 1 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to an exemplary embodiment of this application is shown. The following exemplary embodiments describe the method according to this application in more detail.
[0048] like Figure 1 As shown, the method may include steps S1 and S2. In step S1, feature information of each item belonging to each item category at each time is obtained based on pre-given item category information and image data of the items to be detected in the refrigeration device 1 at each time. In the current embodiment of this application, the category information of the items to be detected in the refrigeration device 1 is pre-given. For example, a user can pre-given N item categories that they use daily, wherein the number of item categories N is unlimited, and the number of items in each item category is also unlimited. Image data of the items to be detected in the refrigeration device 1 at each time can be obtained by the image acquisition unit 11 of the refrigeration device 1. The image acquisition unit 11 is configured, for example, as cameras arranged at multiple locations in the storage space of the refrigeration device 1, which can acquire image data of each item in the storage space from various angles without blind spots.
[0049] Here, pre-given item category information and image data of the items at various times can be input into a loaded multimodal pre-trained large model, thereby obtaining image information of each item belonging to each item category at each time. The multimodal pre-trained large model specifically refers to a deep neural network model based on a transformer structure. It has learned the semantic correspondence between different modalities of data—such as image data and text data—using self-supervised training based on a large amount of unlabeled sample data, and can use text data to guide image processing algorithms to perform image processing tasks such as item classification, item recognition, and item detection. For example, the multimodal pre-trained large model includes, but is not limited to, the SAM model (Segment Anything Model), the DINO (Distillation with no labels) model, and the ClipSeg (Contrastive Language-Image Pre-training Segmentation) model. These multimodal pre-trained large models can directly process the image data of each frame of the item without any model adjustments.
[0050] Specifically, on the one hand, the information of each item category can be sequentially input into the multimodal pre-trained large model. The prompt word encoder of the multimodal pre-trained large model can obtain text features about the item categories based on the input item category information, that is, the names of items in each category, such as vegetables like potatoes, carrots, and green vegetables, and fruits like apples, cherries, and pears. On the other hand, the image data of each frame of items can be input into the multimodal pre-trained large model. The image encoder of the multimodal pre-trained large model can convert the image data of each frame into image features. Since the multimodal pre-trained large model has learned the semantic correspondence between image data and text data, the decoder of the multimodal pre-trained large model can use text features and image features to extract image information about each item in each item category from each frame of image data. At the same time, the image information is associated with the time information of the corresponding frame of image data.
[0051] Next, feature information of each item at each time step can be obtained based on image information of each item at each time step, for example, using a convolutional neural network. In this application, "feature information" refers to information related to the shape features of an item—such as its size, geometry, and color—that can be extracted from image data about the item. Based on this feature information, two different items can be distinguished. Here, convolutional neural networks specifically refer to image classification models based on convolutional neural networks, such as the VGG (Visual Geometry Group) model, the GoogleLeNet model, the RestNet model, and the EfficientNet model, which have been trained on specific datasets. Here, the efficient processing capability of convolutional neural networks for image classification tasks is fully utilized to quickly and accurately obtain feature information of each item based on image information. The feature information of an item obtained using a convolutional neural network can include a first feature value vector of the item at a first time step (assumed to be the starting time) and other feature value vectors of the item at one or more other times, wherein the other times are different from the first time step. It should be noted that since each image information is associated with the time information of the corresponding frame of image data, each feature value vector is also associated with corresponding time information.
[0052] like Figure 2The flowchart shown is a process flow diagram of a method for detecting the freshness of items in a refrigeration device according to another exemplary embodiment of this application. The method may further include step S11. In step S11, the position information of each item belonging to each item category at each time is obtained based on pre-given item category information and image data of the item to be detected in the refrigeration device 1 at each time. Here, the item category information and the image data of the item at each time can be input into a loaded multimodal pre-trained large model, thereby obtaining the position information of each item belonging to each item category at each time. Specifically, the prompt word encoder of the multimodal pre-trained large model can obtain text features about the item category based on the input item category information; the image encoder of the multimodal pre-trained large model can convert the image data of each frame of the item into image features; the decoder of the multimodal pre-trained large model can use the text features and image features to extract the position information of each item of each item category at each time in each frame of image data, that is, the information about the position of each item in the storage space of the refrigeration device 1, and the position information is associated with the time information of the corresponding frame of image data.
[0053] In step S2, the storage time of each item can be determined by comparing the feature information of each item belonging to the same item category at each time point. The storage time is used to identify the freshness of the item. Since each feature value vector in the item's feature information is associated with corresponding time information, the storage time of the item can be determined based on this feature information. Storage time is negatively correlated with the freshness of the item; that is, the longer the storage time, the lower the freshness of the item. Therefore, the freshness of the item can be assessed by comparing its storage time with empirical data of the item category to which it belongs. It is understood that the negative correlation coefficient between the freshness of items and storage time varies for different item categories. For example, the freshness of potatoes decreases relatively slowly with increasing storage time, while the freshness of leafy greens decreases relatively quickly with increasing storage time.
[0054] Next, combined Figure 3 The flowchart illustrating step S2 of a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is shown in detail. The following only describes the steps related to... Figure 2 The differences between the embodiments shown are omitted for brevity, and the same steps will not be repeated.
[0055] like Figure 3As shown, step S2 may include steps S21 to S24. In step S21, the similarity between the first feature information of each item belonging to the same item category at a first time point and other feature information at one or more other times points is calculated, wherein the other times points are different from the first time point. Here, the feature information is represented in the form of feature value vectors. Therefore, when analyzing the similarity between two feature value vectors—for example, a first feature value vector and other feature value vectors—the similarity between the first feature information of the item and other feature information can be characterized by the cosine distance between the first feature value vector of the item and other feature value vectors.
[0056] In step S22, it is determined whether the similarity exceeds a preset similarity threshold. The embodiments of this application are based on the following assumption: there are certain differences between items belonging to the same item category, that is, no two items are completely identical. Therefore, the similarity of the feature information of each item can be used to distinguish items of the same item category.
[0057] If the similarity exceeds a preset similarity threshold, that is, the cosine distance between the first feature value vector of the item and other feature value vectors is less than a preset distance threshold, then in step S23, the item belonging to the first feature information obtained based on the image data at the first time and the item belonging to the other feature information obtained based on the image data at other times are marked as the same item, and the storage time of the item is determined based on the first time and the other times, the storage time being used to identify the freshness of the item.
[0058] If the similarity does not exceed the preset similarity threshold, that is, the cosine distance between the first feature value vector of the item and other feature value vectors is greater than or equal to the preset distance threshold, it means that at other times it is impossible to match a feature value vector with a high similarity to the first feature value vector. Therefore, the item is no longer in the storage space of the refrigeration device 1 at other times. In step S24, the item that belongs to the first feature information obtained based on the image data at the first time is marked as an item that has been taken out.
[0059] Next, steps S21 to S24 are executed repeatedly until all items belonging to the same item category obtained based on the image data at the first moment have been marked, thereby completing the item marking process for that item category and obtaining the storage time of the items still existing in the storage space of the refrigeration equipment 1.
[0060] According to the configuration scheme of this application, a multimodal pre-trained large model is used to directly process the image data of each frame of the item to obtain the time-related feature information of each item. By analyzing the feature information, the storage time of each item can be determined, and then the freshness of each item can be evaluated based on the storage time. In particular, the item category information input into the multimodal pre-trained large model can be changed according to actual needs, such as adding new item categories, deleting existing item categories, or replacing existing item categories with new ones, without requiring any training or fine-tuning of the model. This scheme fully utilizes the generalization ability of the multimodal pre-trained large model for image object detection, not only possessing the advantages of high robustness of deep learning, but also eliminating the technically costly steps of data collection, manual annotation, and model training required by commonly used fully supervised training methods for deep neural networks, thereby effectively improving the detection efficiency of the freshness of different item categories in refrigeration equipment.
[0061] Figure 4 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is shown. The following only describes the method in relation to... Figure 3 The differences between the embodiments shown are omitted for brevity, and the same steps will not be repeated.
[0062] like Figure 4 As shown, step S2 may further include steps S25 and S26. After all items belonging to the same item category acquired based on the image data at the first moment have been marked, i.e., after exiting the loop from S21 to S24, step S25 can determine whether there are any unmarked items among the items belonging to the same item category acquired based on image data at other moments. If there are unmarked items, i.e., the item did not exist at the first moment, then in step S26, the item is marked as a newly added item, and the other moments associated with the item are stored. Here, the feature information and / or location information of the newly added item, as well as the associated other moments, can be stored, and the other moments are regarded as the initial moments of the newly added item. Based on the subsequently acquired image data about the item, the storage time of the item can be determined, thereby identifying the freshness of the item. In this way, newly added items can be quickly and accurately identified, and the storage time of the item can be updated in a timely manner.
[0063] Figure 5 A flowchart illustrating a method for detecting the freshness of articles in a refrigeration device according to another exemplary embodiment of this application is shown. The following only describes the method in relation to... Figure 3 The differences between the embodiments shown are omitted for brevity, and the same steps will not be repeated.
[0064] like Figure 5As shown, step S2 may further include steps S27 and S28. After calculating the storage time of the item, in step S27, it can be determined whether the positional deviation between the first position information of the item at a first moment and other position information at one or more other moments exceeds a preset positional deviation threshold. If the deviation exceeds the preset positional deviation threshold, the item is marked as a moved item in step S28, that is, the item's position in the storage space of the refrigeration device 1 has moved; if the deviation does not exceed the preset positional deviation threshold, step S28 is skipped directly. In this way, items whose positions have changed can be marked quickly and timely, avoiding interference from item position changes on item identification, thereby laying the foundation for the execution of various functions of the refrigeration device 1, such as adjusting the operating parameters of the food preservation function according to the positional changes of the item, indicating the current position of the item according to user needs, etc.
[0065] In addition, it should be noted that the step numbers described herein do not necessarily represent the order of steps, but are merely a reference numeral. The order may be changed depending on the specific circumstances, as long as the technical objective of this application can be achieved.
[0066] Figure 6 A schematic block diagram of a refrigeration device according to an exemplary embodiment of this application is shown.
[0067] like Figure 6 As shown, the refrigeration device 1 may include the following components:
[0068] - Image acquisition unit 11, which is configured to acquire image data of the item to be detected in the refrigeration device 1 at various times, the image acquisition unit 11 being configured, for example, as a camera arranged at multiple locations in the storage space of the refrigeration device 1;
[0069] - Control unit 12, which is configured to implement the method described in accordance with this application.
[0070] Optionally, the refrigeration device 1 is particularly a refrigerator.
[0071] It should be understood that in this document, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance, nor should they be construed as implicitly specifying the number of technical features indicated.
[0072] If an embodiment includes an "and / or" association between a first feature and a second feature, it should be interpreted as follows: according to one implementation, the embodiment has not only the first feature but also the second feature; according to another implementation, the embodiment has either only the first feature or only the second feature.
[0073] Although specific embodiments have been described above, these embodiments are not intended to limit the scope of this application, even when only a single embodiment is described with respect to a particular feature. The feature examples provided in this application are intended for illustrative purposes and not for limitation, unless otherwise stated. In practice, multiple features may be combined with each other as needed and where technically feasible. Various substitutions, modifications, and alterations are also conceived without departing from the spirit and scope of this application.
Claims
1. A method for detecting the freshness of articles in a refrigeration device (1), the method comprising: Step S1: Based on the pre-given item category information and the image data of the items to be detected in the refrigeration equipment (1) at each time, obtain the feature information of each item belonging to each item category at each time. and Step S2: Determine the storage time of each item by comparing the feature information of each item belonging to the same item category at each time. The storage time is used to identify the freshness of the item.
2. The method according to claim 1, wherein, The method further includes: Step S11: Based on the pre-given item category information and the image data of the item to be detected in the refrigeration equipment (1) at each time, obtain the location information of each item belonging to each item category at each time.
3. The method according to claim 1 or 2, wherein, The item category information and image data of the item at each time moment are input into a multimodal pre-trained large model, thereby obtaining the location information and / or image information of each item belonging to each item category at each time moment, and obtaining the feature information of each item at each time moment based on the image information of each item at each time moment.
4. The method according to claim 3, wherein, Based on the image information of each item at each time, the feature information of each item at each time is obtained by using a convolutional neural network.
5. The method according to claim 2, wherein, Step S2 includes: Step S21: Calculate the similarity between the first feature information of each item belonging to the same item category at the first time and other feature information at one or more other times, wherein the other times are different from the first time; Step S22: Determine whether the similarity exceeds a preset similarity threshold; Step S23: If the similarity exceeds a preset similarity threshold, then the item associated with the first feature information obtained based on image data at the first time point and the item associated with the other feature information obtained based on image data at other times points are marked as the same item, and the storage time of the item is determined based on the first time point and the other times points, the storage time being used to identify the freshness of the item; and Step S24: If the similarity does not exceed the preset similarity threshold, then the item associated with the first feature information obtained based on the image data at the first moment is marked as an item that has been taken out.
6. The method according to claim 5, wherein, Steps S21 to S24 are executed repeatedly until all items belonging to the same item category obtained based on the image data at the first moment have been marked.
7. The method according to claim 6, wherein, Step S2 further includes: Step S25: After all items belonging to the same item category acquired based on the image data at the first moment have been marked, determine whether there are any unmarked items among the items belonging to the same item category acquired based on image data at other moments; and Step S26: If there is an unmarked item, mark the item as a newly placed item and store the other time associated with the item.
8. The method according to claim 5, wherein, The feature information of the item includes a first feature value vector of the item at a first time moment and other feature value vectors of the item at one or more other times moment moment, wherein the other times moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment moment 1 feature value vector of the item at a first time moment ...
9. The method according to claim 8, wherein, The cosine distance between the first feature vector of the item and other feature vectors represents the similarity between the first feature information of the item and other feature information. If the cosine distance is less than a preset distance threshold, the similarity exceeds the preset similarity threshold; if the cosine distance is greater than or equal to the preset distance threshold, the similarity does not exceed the preset similarity threshold.
10. The method according to claim 5, wherein, Step S2 further includes: Step S27: After calculating the storage time of the item, determine whether the positional deviation between the first position information of the item at a first moment and other position information at one or more other moments exceeds a preset positional deviation threshold; and Step S28: If the deviation exceeds a preset position deviation threshold, the item is marked as a moved item.
11. A refrigeration device (1), the refrigeration device (1) comprising the following components: An image acquisition unit (11) is configured to acquire image data of the item to be detected in the refrigeration device (1) at various times; and Control unit (12) configured to implement the method according to any one of claims 1 to 10.
12. The refrigeration equipment (1) according to claim 11, wherein, The refrigeration equipment (1) is a refrigerator.
13. A computer program product, such as a computer-readable program carrier, comprising or storing computer program instructions that, when executed by a processor, at least auxiliaryly implement the steps of the method according to any one of claims 1 to 10.