A control method, system, electronic device and medium for plastic foam processing

Abstract

The present invention provides a control method, system, electronic device and medium for plastic foam processing, which relates to the technical field of plastic foam processing. The method comprises: using a heating information processing model based on a heating video of the plastic foam in a starting time period of a foaming machine to determine a color change sequence data of the plastic foam in the starting time period, a swelling degree change sequence data of the plastic foam in the starting time period, and a surface smoothness change sequence data of the plastic foam in the starting time period; using a variational autoencoder to determine a predicted video clip of a subsequent heating process of the plastic foam; using a time determination model based on the heating video of the plastic foam in the starting time period of the foaming machine and a predicted video clip of the subsequent heating process of the plastic foam to determine a stop time of heating the plastic foam; and controlling the foaming machine to stop heating based on the stop time of the heating of the plastic foam. The method can accurately determine the stop time of heating the plastic foam.

Description

Technical Field

[0001] The invention relates to the technical field of plastic foam processing, and in particular to a control method, system, electronic equipment and medium for plastic foam processing. Background Art

[0002] Plastic foam material is a lightweight material widely used in the fields of construction, industry, and home appliances. The heating and foaming treatment of plastic foam materials is a key link in the production of plastic foam materials, and its quality is directly related to the performance and application effect of the final product. However, in the heating and foaming process of plastic foam materials, determining the stop time of plastic foam heating is a challenging technical problem. Heating the plastic foam for too long or too short a time will bring a series of problems. If the heating time is too long, the plastic foam may expand excessively, resulting in a loose structure, decreased strength, and even deformation and rupture. On the contrary, if the heating time is too short, the plastic foam may not achieve the expected expansion effect, the density is too high, and the lightweight characteristics of the plastic foam material are lost. The traditional method of determining the stop time of plastic foam heating mainly relies on the operator's experience and manual operation, which is subject to great subjectivity and uncertainty.

[0003] Therefore, how to accurately determine the stopping time of heating plastic foam is a problem that needs to be solved urgently. Summary of the invention

[0004] The main technical problem solved by the present invention is how to accurately determine the stopping time of heating the plastic foam.

[0005] According to a first aspect, the present invention provides a control method for plastic foam processing, comprising: obtaining a heating video of the plastic foam in a starting time period of a foaming machine; determining color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; determining a predicted video clip of a subsequent heating process of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine, color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder; determining a stop time of heating of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam using a time determination model; and controlling the foaming machine to stop heating based on the stop time of heating of the plastic foam.

[0006] Furthermore, the heating information processing model is a gated recurrent unit, and the time determination model is a gated recurrent unit.

[0007] Furthermore, the heating video of the plastic foam in the initial time period of the foaming machine is captured by a high-definition camera arranged on the foaming machine.

[0008] Furthermore, the input of the variational autoencoder is the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period, and the output of the variational autoencoder is a predicted video clip of the subsequent heating process of the plastic foam.

[0009] Furthermore, the method also includes: inputting the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period into the judgment model to judge whether the heating process of the plastic foam in the starting time period of the foaming machine is normal.

[0010] According to a second aspect, the present invention provides a control system for plastic foam processing, comprising:

[0011] An acquisition module is used to acquire a heating video of the plastic foam in the starting time period of the foaming machine; a heating information processing module is used to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; a prediction module is used to determine a predicted video segment of the subsequent heating process of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder; a stop time determination module is used to determine the stop time of the plastic foam heating based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video segment of the subsequent heating process of the plastic foam using a time determination model; a control module is used to control the foaming machine to stop heating based on the stop time of the plastic foam heating.

[0012] Furthermore, the heating information processing model is a gated recurrent unit, and the time determination model is a gated recurrent unit.

[0013] Furthermore, the heating video of the plastic foam in the initial time period of the foaming machine is captured by a high-definition camera arranged on the foaming machine.

[0014] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method as described above, the method comprising: obtaining a heating video of the plastic foam in the starting time period of the foaming machine; determining the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; determining a predicted video segment of a subsequent heating process of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder; determining the stop time of the heating of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video segment of the subsequent heating process of the plastic foam using a time determination model; and controlling the foaming machine to stop heating based on the stop time of the heating of the plastic foam.

[0015] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned control method for plastic foam processing, the method comprising: obtaining a heating video of the plastic foam in the starting time period of the foaming machine; determining, based on the heating video of the plastic foam in the starting time period of the foaming machine, a heating information processing model, color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period; determining, based on the heating video of the plastic foam in the starting time period of the foaming machine, color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period, a variational autoencoder is used to determine a predicted video segment of a subsequent heating process of the plastic foam; determining, based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video segment of the subsequent heating process of the plastic foam, a time determination model is used to determine the stop time of heating of the plastic foam; and controlling the foaming machine to stop heating based on the stop time of heating of the plastic foam.

[0016] The present invention provides a control method, system, electronic device and medium for plastic foam processing, the method comprising acquiring a heating video of the plastic foam in a starting time period of a foaming machine; determining color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; determining a predicted video clip of a subsequent heating process of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine, color change sequence data of the plastic foam in the starting time period, expansion degree change sequence data of the plastic foam in the starting time period, and surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder; determining a stop time of heating the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam using a time determination model; and controlling the foaming machine to stop heating based on the stop time of heating of the plastic foam. The method can accurately determine the stop time of heating the plastic foam. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of a control method for plastic foam processing provided by an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of a control system for plastic foam processing provided by an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of an electronic device provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0022] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0023] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in the present invention include direct and indirect connections (couplings) unless otherwise specified.

[0024] In an embodiment of the present invention, there is provided Figure 1 A control method for plastic foam processing is shown, and the control method for plastic foam processing includes steps S1 to S5:

[0025] Step S1, obtaining a heating video of the plastic foam in the starting time period of the foaming machine.

[0026] For example, the plastic foam material may be a polypropylene plastic foam material.

[0027] The heating video of the plastic foam in the starting time period of the foaming machine can be captured by a high-definition camera arranged on the foaming machine. A high-definition camera can be installed and located on the top of the foaming machine. In the starting time period, the camera is started to record the heating process of the plastic foam. The heating video of the plastic foam in the starting time period of the foaming machine includes information such as the formation, color change, and expansion of the plastic foam.

[0028] The start time period is a preset time period. As an example, the start time period can be one of the first minute, first three minutes, first five minutes, and first ten minutes before the plastic foam starts to be heated in the foaming machine.

[0029] Step S2, based on the heating video of the plastic foam in the starting time period of the foaming machine, use the heating information processing model to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period.

[0030] The heating information processing model is a gated loop unit. The input of the heating information processing model is the heating video of the plastic foam in the starting time period of the foaming machine, and the output of the heating information processing model is the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period.

[0031] The color change sequence data is a data sequence that records the color change of the plastic foam in the initial time period. The color change sequence data can be used to analyze the color characteristic changes of the plastic foam during the heating process.

[0032] The expansion degree change sequence data is a data sequence that records the expansion degree change of the plastic foam within the initial time period. The expansion degree change sequence data can be used to analyze the change of the expansion characteristics of the plastic foam.

[0033] The surface smoothness change sequence data is a data sequence that records the surface smoothness change of the plastic foam in the initial time period. The surface smoothness change sequence data can be used to analyze the change in the surface smoothness characteristics of the plastic foam.

[0034] The Gated Recurrent Unit (GRU) is used to process sequence data and time series information. The Gated Recurrent Unit consists of three components: a memory unit, an update gate, and a reset gate. The process of heating plastic foam in a foaming machine is a dynamic process that requires consideration of the impact of information from past time points on the current state. The Gated Recurrent Unit can better capture long-term dependencies in sequence data through a gating mechanism, thereby more accurately determining the sequence data of changes in plastic foam at the beginning of the time period.

[0035] In some embodiments, the heating information processing model includes a segmentation time point determination layer, a video segmentation layer, and a data determination layer. The segmentation time point determination layer, the video segmentation layer, and the data determination layer all include a gated loop unit. The input of the segmentation time point determination layer is the heating video of the plastic foam in the starting time period of the foaming machine, and the output of the segmentation time point determination layer is a plurality of segmentation time points. The input of the video segmentation layer is the heating video of the plastic foam in the starting time period of the foaming machine and the plurality of segmentation time points. The output of the video segmentation layer is a plurality of heating video clips of the plastic foam in the starting time period of the foaming machine. The input of the data determination layer is a plurality of heating video clips of the plastic foam in the starting time period of the foaming machine. The output of the data determination layer is the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period.

[0036] By dividing the model into different layers, each layer can focus on performing a specific task. For example, the segmentation time point determination layer focuses on identifying key time points in the video, the video segmentation layer is responsible for segmenting the video based on these time points, and the data determination layer extracts feature data such as color, dilation, and surface smoothness. This modular approach allows each layer to be optimized specifically for its task, improving the efficiency and performance of the overall model.

[0037] Breaking down the task into multiple steps, each of which builds on the previous step, can gradually refine and refine information. This approach helps improve the accuracy of the final output data because each layer can focus on improving the accuracy of its specific task, thereby improving the reliability of the entire model.

[0038] Step S3, based on the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period, a predicted video segment of the subsequent heating process of the plastic foam is determined using a variational autoencoder.

[0039] The predicted video clip of the subsequent heating process of the plastic foam is a video clip of the subsequent heating process of the plastic foam predicted by the variational autoencoder output.

[0040] The input of the variational autoencoder is the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period. The output of the variational autoencoder is a predicted video clip of the subsequent heating process of the plastic foam.

[0041] Variational Autoencoder (VAE) is a generative model that can learn the potential representation of data and generate new samples from it. The variational autoencoder includes an encoder and a decoder. The encoder is used to learn the distribution of data and find the potential structure of the data. The decoder is used to generate new samples with similar features from the potential structure learned by the encoder. The trained variational autoencoder model can take as input the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period, so as to generate a predicted video clip of the subsequent heating process of the plastic foam.

[0042] Variational autoencoders can process sequence data, such as features that change over time in video frames. By taking the heating video of the plastic foam at the beginning of the foaming machine and the time series data of the plastic foam as input, the variational autoencoder can learn the changing rules in the time dimension and use it to predict the predicted video clips of the subsequent heating process of the plastic foam at future time points. The encoder of the variational autoencoder maps the complex input data into a distribution in a latent space that captures the key features of the data. In this way, the variational autoencoder is able to extract important information about the heating process of the plastic foam and represent it as a set of latent variables. The decoder of the variational autoencoder can sample points from the latent space and generate new samples that are similar to the original data. In the application of plastic foam heating, the variational autoencoder can generate predicted video clips of the plastic foam in the subsequent heating process based on the learned distribution.

[0043] In some embodiments, the method further includes: inputting the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period into a judgment model to judge whether the heating process of the plastic foam in the starting time period of the foaming machine is normal.

[0044] The judgment model is a deep neural network model. The input of the judgment model is the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period. The output of the judgment model is that the heating process of the plastic foam in the starting time period of the foaming machine is normal or the heating process of the plastic foam in the starting time period of the foaming machine is abnormal. The deep neural network model includes deep neural networks (DNN). The deep neural network can include multiple processing layers, each processing layer is composed of multiple neurons, and each neuron performs matrix transformation on the data.

[0045] The color change of plastic foam can reflect the changes in its chemical composition and physical state. In the normal heating process, the color change should be smooth and continuous. If the color change is abnormal, such as sudden or discontinuous, it may indicate that there is a problem in the heating process, such as improper temperature control or wrong raw material ratio.

[0046] The expansion degree of plastic foam is an important indicator of its physical changes during heating. The normal heating process will cause the plastic foam to expand to a certain extent. If the expansion degree exceeds the normal range or the rate of change is abnormal, it may mean that there is a problem with the heating process, such as overheating or insufficient reaction of the raw materials.

[0047] The surface smoothness of plastic foam reflects the uniformity and stability of its surface structure. During the normal heating process, the surface of plastic foam should gradually become smooth. If the surface smoothness is abnormal, such as unevenness or local roughness, it may indicate problems such as uneven heating or uneven mixing of raw materials during the heating process. The deep neural network model is able to process these complex sequence data and determine whether the heating process of the plastic foam is normal by learning the complex relationships and patterns between color changes, expansion degree and surface smoothness. The multi-layer structure and large number of neurons of the deep neural network model enable it to capture the subtle differences and deep features in the data, thereby making accurate judgments.

[0048] Step S4, using a time determination model to determine the stop time of the plastic foam heating based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam.

[0049] The time determination model is a gated loop unit. The input of the time determination model is the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam. The output of the time determination model is the stop time of the plastic foam heating.

[0050] The stop time of the plastic foam heating is the time at which the heating of the plastic foam is stopped.

[0051] The variational autoencoder learns the underlying patterns of the data through its encoder and decoder structures. This generates more accurate prediction video clips. These underlying structures may not be easy to observe directly from the original data, but they are crucial for predicting the final state of the plastic foam. The variational autoencoder can generate a possible future state, i.e., a prediction video clip, based on the determined plastic foam heating process. This generation ability is very useful for predicting the behavior and changes of plastic foam during the heating process. The plastic foam heating process is essentially a dynamic, time-varying process. The variational autoencoder is particularly suitable for processing this type of time series data because it can learn the dynamic characteristics of data changing over time and predict future development trends. By using the predicted video clips generated by the variational autoencoder as the input of the time determination model, the detailed information in the predicted video clips and the key information in the plastic foam heating process can be better utilized to generate more accurate prediction results and provide strong support for determining the stop time of plastic foam heating. This method is more efficient and reliable than directly using the original data for prediction.

[0052] The heating process of plastic foam in a foaming machine can be represented as a time series, in which each frame of video contains the state information of the plastic foam during the heating process. These video sequence data contain rich time-related information, such as temperature changes, changes in the volume of plastic foam, etc. The gated recurrent unit has the ability to process sequence data and can automatically capture patterns and regularities in time series. Based on the video sequence data of the plastic foam heating process, the gated recurrent unit can learn the feature representation of different stages in the plastic foam heating process. On the trained gated recurrent unit model, the stop time of the plastic foam heating can be predicted by inputting the heating video of the plastic foam at the beginning of the foaming machine and the predicted video clips of the subsequent heating process. During the learning process, the model captures the characteristics of each stage of the heating process, so that it can accurately predict the best time to stop heating to achieve the desired heating effect.

[0053] Step S5, controlling the foaming machine to stop heating based on the stop time of heating the plastic foam.

[0054] After determining the stop time of heating the plastic foam, the foaming machine is controlled to stop heating when the stop time of heating the plastic foam is reached.

[0055] Based on the same inventive concept, Figure 2 A schematic diagram of a control system for plastic foam processing provided by an embodiment of the present invention, wherein the control system for plastic foam processing comprises:

[0056] An acquisition module 21 is used to acquire a heating video of the plastic foam in the starting time period of the foaming machine;

[0057] A heating information processing module 22 is used to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model;

[0058] A prediction module 23 is used to determine a predicted video segment of a subsequent heating process of the plastic foam based on a heating video of the plastic foam in a starting time period of a foaming machine, a color change sequence data of the plastic foam in the starting time period, a swelling degree change sequence data of the plastic foam in the starting time period, and a surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder;

[0059] A stop time determination module 24, configured to determine the stop time of heating the plastic foam using a time determination model based on the heating video of the plastic foam in the start time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam;

[0060] The control module 25 is used to control the foaming machine to stop heating based on the stop time of heating the plastic foam.

[0061] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 3 As shown, including:

[0062] The invention comprises: a processor 31; a memory 32; and a computer program; wherein the computer program is stored in the memory 32 and is configured to be executed by the processor 31 to implement the control method for plastic foam processing as provided above, the method comprising: obtaining a heating video of the plastic foam in the starting time period of the foaming machine; determining the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; determining a predicted video clip of the subsequent heating process of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period using a variational autoencoder; determining the stop time of the heating of the plastic foam based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam using a time determination model; and controlling the foaming machine to stop heating based on the stop time of the heating of the plastic foam.

[0063] Based on the same inventive concept, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 31, implements the aforementioned control method for plastic foam processing, the method comprising: obtaining a heating video of the plastic foam in the starting time period of the foaming machine; using a heating information processing model based on the heating video of the plastic foam in the starting time period of the foaming machine to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period; using a variational autoencoder based on the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period to determine a predicted video clip of a subsequent heating process of the plastic foam; using a time determination model based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam to determine the stop time of heating of the plastic foam; controlling the foaming machine to stop heating based on the stop time of heating of the plastic foam.

[0064] The control method for plastic foam processing provided in the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiment of the present application does not impose any restrictions on this.

[0065] Taking the mobile phone 100 as an example of the electronic device, Figure 4 A schematic structural diagram of the mobile phone 100 is shown.

[0066] like Figure 4 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0067] The sensor module 180 may include a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor and other sensors.

[0068] It is to be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may include more or fewer components than those illustrated, or combine certain components, or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0069] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0070] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0071] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0072] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0073] In some embodiments, the processing module 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0074] The processing module 110 can obtain chronic difficult-to-heal wound images and wound detection reports of multiple users.

[0075] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0076] The power management module 141 is used to connect the battery 142, the charging management module 140 and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0077] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0078] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0079] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0080] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0081] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0082] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0083] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change display information.

[0084] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1. In the embodiment of the present application, the display screen 194 can be used to display a remote session window, an image of a chronic, difficult-to-heal wound, and a wound detection report.

[0085] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0086] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0087] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0088] The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0089] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0090] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0091] In some embodiments, the NPU computing processor can run a convolutional neural network model and classify the multiple users to obtain multiple user groups.

[0092] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0093] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0094] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0095] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0096] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0097] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0098] Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0099] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0100] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0101] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0102] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0103] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0104] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0105] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0106] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0107] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0108] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A control method for plastic foam processing, characterized in that: include: Obtain a heating video of the plastic foam at the beginning of the foaming machine; Based on the heating video of the plastic foam in the starting time period of the foaming machine, a heating information processing model is used to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period; Determine a predicted video clip of the subsequent heating process of the plastic foam using a variational autoencoder based on the heating video of the plastic foam in the initial time period of the foaming machine, the color change sequence data of the plastic foam in the initial time period, the expansion degree change sequence data of the plastic foam in the initial time period, and the surface smoothness change sequence data of the plastic foam in the initial time period; Determine the stopping time of heating the plastic foam using a time determination model based on the heating video of the plastic foam in the starting time period of the foaming machine and the predicted video clip of the subsequent heating process of the plastic foam; The foaming machine is controlled to stop heating based on the stop time of heating the plastic foam.

2. The control method for plastic foam processing according to claim 1, characterized in that: The heating information processing model is a gated recurrent unit, and the time determination model is a gated recurrent unit.

3. The control method for plastic foam processing according to claim 1, characterized in that: The heating video of the plastic foam in the initial period of the foaming machine is captured by a high-definition camera arranged on the foaming machine.

4. The control method for plastic foam processing according to claim 1, characterized in that: The input of the variational autoencoder is the heating video of the plastic foam in the starting time period of the foaming machine, the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period. The output of the variational autoencoder is a predicted video clip of the subsequent heating process of the plastic foam.

5. The control method for plastic foam processing according to claim 1, characterized in that: The method also includes: inputting the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period into a judgment model to judge whether the heating process of the plastic foam in the starting time period of the foaming machine is normal.

6. A control system for plastic foam processing, characterized in that: include: An acquisition module, used to acquire a heating video of the plastic foam in the starting time period of the foaming machine; A heating information processing module, used to determine the color change sequence data of the plastic foam in the starting time period, the expansion degree change sequence data of the plastic foam in the starting time period, and the surface smoothness change sequence data of the plastic foam in the starting time period based on the heating video of the plastic foam in the starting time period of the foaming machine using a heating information processing model; A prediction module, for determining a predicted video segment of a subsequent heating process of the plastic foam using a variational autoencoder based on a heating video of the plastic foam in a starting time period of a foaming machine, a color change sequence data of the plastic foam in the starting time period, a swelling degree change sequence data of the plastic foam in the starting time period, and a surface smoothness change sequence data of the plastic foam in the starting time period; A stop time determination module, used to determine the stop time of heating the plastic foam using a time determination model based on a heating video of the plastic foam in the start time period of the foaming machine and a predicted video clip of a subsequent heating process of the plastic foam; The control module is used to control the foaming machine to stop heating based on the stop time of heating the plastic foam.

7. The control system for plastic foam processing according to claim 6, characterized in that: The heating information processing model is a gated recurrent unit, and the time determination model is a gated recurrent unit.

8. The control system for plastic foam processing according to claim 6, characterized in that: The heating video of the plastic foam in the initial period of the foaming machine is captured by a high-definition camera arranged on the foaming machine.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the control method for plastic foam processing as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the control method for plastic foam processing as described in any one of claims 1 to 5 is implemented.

Images