CN114474473B - Production method and system of modified plastic - Google Patents
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Abstract
The embodiment of the specification provides a production method of modified plastics, which comprises the steps of stirring, extruding, cooling and cutting production raw materials to obtain modified plastic particles; in the extrusion process, obtaining an extrusion temperature sequence; in the cooling process, obtaining a cooling temperature sequence; and when the extrusion temperature sequence and/or the cooling temperature sequence do not meet the first preset condition, performing exception processing.
Description
Technical Field
The specification relates to the field of plastic production, in particular to a method and a system for producing modified plastic.
Background
The production process of modified plastics involves various steps such as heating, cooling, cutting and the like. Among these, the temperature during the production process has a particularly important influence on the quality of the final product. For example, if the heating temperature is too high, the resin may be decomposed, and the finally produced plastic particles may be black in color; if the heating temperature is too low, the resin is not sufficiently melted, and voids are formed in the plastic particles finally produced. Accurately controlling each production link, and taking measures in time when abnormity occurs, which is the key for producing high-quality modified plastics. It is therefore desirable to provide a solution that allows more efficient and high quality production of modified plastics.
Disclosure of Invention
One of the embodiments of the present specification provides a method for producing a modified plastic. The production method of the modified plastic comprises the following steps: stirring, extruding, cooling and cutting the production raw materials to obtain modified plastic particles; in the extrusion process, obtaining an extrusion temperature sequence; acquiring a cooling temperature sequence in the cooling process; and when the extrusion temperature sequence and/or the cooling temperature sequence do not meet a first preset condition, performing exception handling.
One of the embodiments of the present specification provides a production system for modified plastics. The production system of the modified plastic comprises: the production processing module is used for stirring, extruding, cooling and cutting the production raw materials to obtain modified plastic particles; the first judgment module is used for acquiring the extrusion temperature sequence and the cooling temperature sequence and performing exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not meet a first preset condition.
One of the embodiments of the present specification provides a modified plastic production apparatus, including a processor, configured to execute the modified plastic production method according to any one of the embodiments of the present specification.
One of the embodiments of the present specification provides a computer-readable storage medium, which stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the method for producing modified plastic according to any one of the embodiments of the present specification.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals refer to like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a system for producing modified plastics, according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart of a method of producing a modified plastic, according to some embodiments herein;
FIG. 3 is another exemplary flow chart of a method of producing a modified plastic, according to some embodiments herein;
FIG. 4 is yet another exemplary flow diagram of a method for producing a modified plastic, according to some embodiments herein;
FIG. 5 is an exemplary block diagram of a production system for modified plastics, shown in accordance with some embodiments of the present disclosure.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "device," "unit," and/or "module" as used herein is a method for distinguishing between different components, elements, parts, portions, or assemblies of different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified steps or elements as not constituting an exclusive list and that the method or apparatus may comprise further steps or elements.
Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of the present specification. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic diagram of an application scenario of a system for producing modified plastics according to some embodiments of the present disclosure. As shown in fig. 1, an application scenario 100 of a modified plastic production system according to an embodiment of the present disclosure may include a processor 110, a storage device 120, a network 130, a user terminal 140, and a modified plastic production apparatus 150.
In some embodiments, the processor 110 may be part of the modified plastic production apparatus 150. In some embodiments, a production system for modified plastics can achieve production of modified plastics by implementing the methods and/or processes disclosed in this specification.
In some embodiments, processor 110 may process data and/or information obtained from other devices or system components. Processor 110 may execute program instructions based on such data, information, and/or processing results to perform one or more of the functions described in the embodiments herein. In some embodiments, the processor 110 may obtain the extrusion temperature sequence during extrusion, obtain the cooling temperature sequence during cooling, and the processor 110 may perform exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not satisfy the first preset condition. In some embodiments, the processor 110 may obtain the performance parameter of the output material during the production process, and the processor 110 may perform exception handling when the performance parameter does not satisfy the second preset condition. In some embodiments, the processor 110 may include one or more sub-processing devices (e.g., single core processing devices or multi-core processing devices).
The network 130 may connect the various components of the system and/or connect the system with external resource components. The network 130 allows communication between the various components and with other components outside the system to facilitate the exchange of data and/or information. In some embodiments, the network 130 may be any one or more of a wired network or a wireless network. One or more components of the application scenario 100 may connect to the network 130 to exchange data and/or information; for example, the processor may obtain production related data 150-1 based on the modified plastic production device 150 via the network 130, and for example, the processor 110 may obtain performance parameters of the produced materials via the network.
The modified plastic production apparatus 150 may refer to an apparatus for producing modified plastic. In some embodiments, the modified plastic production apparatus 150 may perform stirring, extruding, cooling, and cutting processes on the production raw material to obtain modified plastic particles. In some embodiments, the modified plastic production apparatus 150 may include a stirring apparatus, an extrusion apparatus, a cooling apparatus, a cutting apparatus, an air drying apparatus, a screening apparatus, and the like. Different devices can carry out different treatments to the raw materials for production, and then obtain modified plastics. In some embodiments, processor 110 may obtain production related data 150-1 via a modified plastic production facility. Production related data 150-1 may include temperature data (e.g., extrusion temperature, cooling temperature, etc.), performance data (e.g., thermal parameters, rheological parameters, color parameters, etc.). For further description of the modified plastic production apparatus 150, reference is made to the description in relation to FIG. 2.
FIG. 2 is an exemplary flow chart of a method of producing a modified plastic, according to some embodiments of the present disclosure. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by the modified plastic production apparatus 150.
And step 210, stirring, extruding, cooling and cutting the production raw materials to obtain the modified plastic particles. In some embodiments, step 210 may be performed by production process module 510.
In some embodiments, the production processing module 510 can process the production feedstock through the modified plastic production device 150 to obtain modified plastic particles. For example, the production raw material is poured into a stirring apparatus, and the production raw material is sequentially subjected to processes of stirring, extrusion, cooling, cutting, and the like in the stirring apparatus, the extrusion apparatus, the cooling apparatus, the cutting apparatus, and the like until modified plastic particles are obtained. All there is temperature sensor in extrusion device and the cooling device, and temperature sensor can be semiconductor sensor, and semiconductor sensor can sense the temperature when output material extrudees and cools off. In some embodiments, the production process module 510 may effectively extrude the heated molten plastic into the cooling device by controlling hydraulic pumps, hydraulic plates, etc. in the extrusion device. The air cooler and the spraying machine in the cooling device can carry out dual cooling on the output materials.
In some embodiments, a process for producing a feedstock can be considered a production cycle, and a production cycle can produce a plurality of batches of output materials, each batch of output materials corresponding to at least one set of performance parameters at different points in time for a corresponding production cycle, at least one extrusion temperature sequence for different time periods and at least one cooling temperature sequence for different time periods for a corresponding production cycle, and the like.
An extrusion temperature sequence may refer to a plurality of series of temperatures of the production feedstock during the extrusion process. The extrusion temperature sequence may comprise extrusion temperatures at a plurality of time points. In some embodiments, the sequence of extrusion temperatures may include sequences of extrusion temperatures of different lengths, which may refer to a different number of extrusion temperatures included. For example, the extrusion temperature series 1 includes 60 extrusion temperatures; the extrusion temperature series 2 included 120 extrusion temperatures.
In some embodiments, the first determination module 520 may obtain the extrusion temperature sequence during the extrusion process. For example, the first determining module 520 may obtain a plurality of extrusion temperatures through temperature sensors in the extrusion device to generate the extrusion temperature sequence. The plurality of extrusion temperatures may be a plurality of extrusion temperatures at spaced intervals. For example, the extrusion process is 1 minute, the preset time interval is 1 second, and the first determining module 520 may obtain the extrusion temperature from the temperature sensor in the extrusion apparatus every 1 second, may obtain 60 extrusion temperatures, and the extrusion temperature sequence may be an extrusion temperature sequence including 60 extrusion temperatures. For another example, the user may send the interval time for collecting the extrusion temperature through the user terminal, obtain a plurality of extrusion temperatures, and generate extrusion temperature sequences of different lengths. For example, the extrusion process is 1 minute, the interval time between the user terminal and the extrusion temperature acquisition is 0.5 second, and the first determining module 520 may obtain the extrusion temperature from the temperature sensor in the extrusion device every 0.5 second, and may obtain 120 extrusion temperatures, where the extrusion temperature sequence may be an extrusion temperature sequence including 120 extrusion temperatures. When the interval time for the user terminal to transmit the collected squeezing temperatures is 2 seconds, the corresponding squeezing temperature sequence may be a squeezing temperature sequence including 30 squeezing temperatures. The first judgment module 520 related to the interval time may be preset according to actual requirements or may be sent by the user through the user terminal, and may be determined according to the actual requirements.
The cooling temperature sequence may refer to a plurality of series of temperatures of the production feedstock during the cooling process. Similar to the extrusion temperature sequence, the cooling temperature sequence may also include cooling temperatures at multiple time points. In some embodiments, the cooling temperature sequence may include cooling temperature sequences of different lengths, which may refer to different numbers of cooling temperatures included. For example, the cooling temperature sequence a includes 50 cooling temperatures; the cooling temperature sequence 2 includes 100 cooling temperatures.
In some embodiments, during cooling, the first determination module 520 may obtain a cooling temperature sequence. For example, the first determining module 520 may obtain a plurality of cooling temperatures through a temperature sensor in the cooling device to generate a cooling temperature sequence. The plurality of cooling temperatures may be a plurality of cooling temperatures at intervals. For another example, the user may obtain a plurality of cooling temperatures by sending the interval time for collecting the cooling temperatures through the user terminal, and generate a cooling temperature sequence with different lengths. The cooling temperature sequence is obtained in a similar manner to the extrusion temperature sequence, and further details regarding obtaining the cooling temperature sequence can refer to the related description in obtaining the extrusion temperature, and are not repeated herein.
And step 230, acquiring predicted temperature information at a future moment through a temperature prediction model based on the extrusion temperature sequence and the cooling temperature sequence. In some embodiments, step 230 may be performed by the first determination module 520.
A certain moment in the future may refer to a certain moment in the future relative to the current moment, e.g. a starting moment of a next production cycle relative to the current production cycle, e.g. a next moment in the future relative to a certain moment in the current production cycle, etc.
The predicted temperature information may refer to temperature information at a time in the future, for example, a sequence of extrusion temperatures and a sequence of cooling temperatures at a time in the future.
The temperature prediction model refers to a model capable of predicting the temperature at a future time. In some embodiments, the type of temperature prediction model may be a variety of machine learning models, such as Deep Neural Networks (DNNs), recurrent Neural Networks (RNNs), and the like. The choice of model type may be case specific.
In some embodiments, the inputs to the temperature prediction model may include an extrusion temperature sequence, a cooling temperature sequence, and the like. The output of the temperature prediction model may include predicted temperature information at a time in the future. The inputs to the temperature prediction model may include extrusion temperature sequences and cooling temperature sequences for a plurality of production cycles, e.g., extrusion temperature sequences and cooling temperature sequences for some (5, 10, etc.) production cycles in the past, extrusion temperature sequences and cooling temperature sequences for production cycles within the past 3 days, etc.
In some embodiments, the temperature prediction model may be derived based on a plurality of training samples and label training.
In some embodiments, the training samples include a sample extrusion temperature sequence, a sample cooling temperature sequence. The label predicts temperature information for a sample at a future time. The training data can be obtained based on historical data, and the labels of the training data can be determined in a manual labeling mode or an automatic labeling mode. For example, the actual temperature information (e.g., the extrusion temperature sequence and the cooling temperature sequence) at a time in the future corresponding to the sample extrusion temperature sequence and the sample cooling temperature sequence is labeled as the label of the sample extrusion temperature sequence and the sample cooling temperature sequence.
In some embodiments, the first determining module 520 may obtain the predicted temperature information at a future time through a temperature prediction model. For example, the first determination module 520 may input the extrusion temperature sequences and the cooling temperature sequences of a few past production cycles into a temperature prediction model, which outputs predicted temperature information at a future time. For example, predicted temperature information (e.g., extrusion temperature sequence and cooling temperature sequence) at the start of the next production cycle.
In some embodiments of the present description, the predicted temperature information at a future time (e.g., a next production cycle) is obtained through the temperature prediction model, the predicted temperature information at the future time can be more accurately obtained based on the actual temperature information, the extrusion temperature sequence and the cooling temperature sequence at the future time can be effectively predicted, it is helpful to determine whether an abnormality exists in the next production cycle, and when the abnormality exists, the processing is timely performed, so as to reduce waste of materials.
And 240, performing exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not meet the first preset condition or the predicted temperature information does not meet the first preset condition. In some embodiments, step 240 may be performed by the first determination module 520.
The first preset condition may refer to a condition under which the modified plastic production apparatus normally operates. For example, the modified plastic production apparatus normally runs the conditions (e.g., threshold values, etc.) that the extrusion temperature sequence and the cooling temperature sequence need to satisfy. In some embodiments, the first preset condition may have a plurality of expressions. For example, the first preset condition is that a plurality of temperature values in the extrusion temperature sequence and the cooling temperature sequence are both smaller or larger than a certain threshold value. Illustratively, a plurality of temperature values in the extrusion temperature sequence and the cooling temperature sequence are each less than a threshold value a; or a plurality of temperature values in the extrusion temperature sequence and the cooling temperature sequence are both larger than the threshold value C; for another example, the first preset condition is that a plurality of temperature values in the extrusion temperature sequence are all smaller than a certain threshold (e.g., threshold a), and a plurality of temperature values in the cooling temperature sequence are all smaller than a certain threshold (e.g., threshold B). For another example, the first preset condition is that the absolute value of the difference between any two adjacent temperature values in the extrusion temperature sequence and the cooling temperature sequence is smaller than a certain threshold (e.g., threshold D).
In some embodiments, the first preset condition may be set based on experience. For example, the above threshold values (e.g., threshold values a, B, C, D, etc.) may be set based on experience. For example, the extrusion temperature sequence corresponds to the setting of the extrusion temperature conditions. Illustratively, the base temperature of ABS material granulation is about 200 ℃, the extrusion temperature can be controlled at 180-230 ℃, if the extrusion temperature is too high (such as over 240 ℃ and the like), resin decomposition can be caused, the appearance color of the particles is blackened, and the like, so that the quality of the particles is influenced; if the extrusion temperature is too low (e.g. below 170 ℃), the resin may not be sufficiently melted, the inorganic powder may not be completely coated by the limited carrier resin, voids may occur in the surface coarse particles of the master batch, and the resin and the inorganic powder may not form a uniform system. For another example, the cooling temperature conditions corresponding to the cooling temperature sequence are set. For example, the cooling water temperature is too high, which may cause too high cooling temperature and possibly cause the rejection of the material; too low a cooling water temperature may cause material shrinkage, resulting in shrinkage holes and thus poor plasticization. Therefore, the cooling temperature condition may be set based on a comprehensive consideration such as the material properties, the production process, and the historical production experience. In some embodiments, the first preset condition may be set according to actual requirements.
In some embodiments, the first determining module 520 may perform an exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not satisfy the first preset condition. For example, the first judging module 520 may issue an alarm through the modified plastic manufacturing apparatus when the extrusion temperature sequence and/or the cooling temperature sequence do not satisfy the first preset condition. For another example, the first determining module 520 may stop the production of the modified plastic production apparatus when the extrusion temperature sequence and/or the cooling temperature sequence do not satisfy the first preset condition. In some embodiments, the first determining module 520 may perform exception handling when the predicted temperature information does not satisfy the first preset condition. For example, the first determination module 520 may cause the modified plastic production apparatus to stop production and issue an alarm when the predicted temperature information does not satisfy the first preset condition. And after the abnormity of the modified plastic production device is processed, entering the next production period, for example, manually overhauling the modified plastic production device, and starting the next production period after the abnormity is eliminated.
In some embodiments of the present description, through obtaining relevant temperature information (such as an extrusion temperature sequence, a cooling temperature sequence, etc.) in the production process, whether preset conditions are met based on the extrusion temperature sequence, the cooling temperature sequence, etc., and then whether an abnormality exists in the production process is judged, and when the abnormality exists in the production process, an abnormality is processed, so that the production process can be monitored in real time, the quality of output materials is ensured, and the waste of the materials is reduced.
FIG. 3 is another exemplary flow diagram of a method for producing a modified plastic, according to some embodiments of the present disclosure. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by the processor 110.
In step 310, performance parameters of the produced material are obtained, and the performance parameters may include one or more of thermal parameters, rheological parameters, and color parameters. The output material may comprise cooled semifinished products or modified plastic granules. In some embodiments, step 310 may be performed by the second determination module 530.
The output material may refer to a product obtained by subjecting a production raw material to a series of treatments, for example, an intermediate product (e.g., a cooled semi-finished product, etc.), a final product (e.g., modified plastic particles, etc.), and the like.
Performance parameters refer to parameters that may represent characteristics of the produced material itself. The quality of the produced material can be obtained or evaluated through the performance parameters of the produced material. In some embodiments, the performance parameters of the produced material may include one or more of thermal parameters, rheological parameters, color parameters, and the like.
The thermal parameter index may reflect a parameter associated with the thermal properties of the produced material. In some embodiments, the thermal parameters may include thermal conductivity, specific heat capacity, coefficient of thermal expansion, melting point, thermal diffusivity, and the like.
In some embodiments, the second determining module 530 may automatically obtain the thermal parameters of the produced material through the modified plastic production apparatus. For example, the modified plastic manufacturing apparatus may have related testing components to perform related tests on the manufactured materials, and the second determining module 530 further obtains thermal parameters (e.g., thermal expansion coefficient, etc.) of the related manufactured materials of the testing components. In some embodiments, the second determining module 530 may obtain the thermal parameter of the produced material through an input of the user terminal. For example, a user may test one or more testing instruments for thermal parameters related to the produced materials, and input the test results into the system through a user terminal, and the second determining module 530 may further obtain the thermal parameters (such as thermal expansion coefficient) related to the produced materials. The thermal parameters related to the produced materials can be obtained in other possible ways, and can be determined according to actual conditions.
The rheological parameter refers to a relevant parameter which can reflect the deformation and the flowing property of the produced material under the action of external force.
In some embodiments, the second determination module 530 may automatically obtain rheological parameters of the produced material. For example, there are related testing components in the modified plastic manufacturing apparatus to perform related tests on the output materials, and the second determining module 530 further obtains rheological parameters of the output materials related to the testing components. In some embodiments, the second determination module 530 may obtain the rheological parameters of the produced material through an input of the user terminal. For example, the user tests the related rheological parameters of the produced materials through the rheological tester, and inputs the test result into the system through the user terminal, and the second determining module 530 further obtains the rheological parameters of the produced materials. The rheological parameters of the produced materials can be obtained in other possible ways, and can be determined according to actual conditions.
The color parameter refers to a parameter that can reflect the color of the produced material, such as RGB value.
In some embodiments, the second determining module 530 may obtain images of the produced materials under a plurality of preset angle light sources, and determine the color parameters through an image recognition model based on the images.
The plurality of preset angle light sources may refer to a plurality of different color light sources at different angles. The plurality of preset angle light sources may be light sources of a specified color at a plurality of specified angles set in advance.
The images of the produced materials may refer to images taken of the produced materials.
In some embodiments, the second determining module 530 can obtain images of the produced materials under a plurality of preset angle light sources. For example, the second determination module 530 may place the produced material in a shooting area having a plurality of preset angle light sources and having a plurality of high definition cameras arranged at different positions through the robot arm, the high definition cameras may periodically shoot the produced material, and the second determination module 530 may transmit the shot image data to the second determination module 530.
In some embodiments, the second determination module 530 may determine the color and luster parameter through an image recognition model based on the photographed image.
The image recognition model refers to a model which can recognize a produced material region in an image through shape detection or a recognition algorithm and the like. In some embodiments, the image recognition model may include FT algorithms, HOG algorithms, hidden variable support vector machines, hidden conditional random fields, and the like. The choice of model type may be case specific.
In some embodiments, the input to the image recognition model may include one or more images of the product material, and the like. The output of the image recognition model may include color data (e.g., RGB values, etc.) for areas containing production materials.
In some embodiments, the image recognition model may be derived based on a plurality of training samples and label training.
In some embodiments, the training samples comprise sample images. The label is color data of the sample image. The training data can be obtained based on historical data, and the labels of the training data can be determined in a manual labeling mode or an automatic labeling mode. For example, color data (e.g., RGB values, etc.) corresponding to the sample image and including the produced material area is labeled as a label of the sample image.
In some embodiments, the second determination module 530 may determine the color parameter through an image recognition model. For example, the second determination module 530 may input the image into an image recognition model, which outputs color data including the produced material area. The second determination module 530 may determine the color parameter of the produced material using one or more algorithms, such as the second determination module 530 calculating an average RGB value for the areas containing the produced material, or a weighted average RGB value for the areas containing the produced material, etc., and determining the average RGB value as the color parameter of the produced material.
In some embodiments of the present description, the color parameter of the produced material is determined through the image recognition model based on the images under the light sources at the plurality of preset angles, so that the accuracy of the color parameter of the produced material can be ensured.
In some embodiments, the second determining module 530 may obtain an image of the produced material, determine a plurality of identified regions based on the YOLO model segmentation, and determine the color parameters based on the color data of the plurality of identified regions.
The YOLO model refers to a model that can identify a region of produced material in an image by segmentation or the like.
In some embodiments, the inputs to the YOLO model may include one or more images of the produced material, and the like. The output of the YOLO model may include the image segmentation results. The image segmentation result may include a plurality of identification regions of the image, classifications corresponding to the plurality of identification regions (e.g., whether an identification region is a produced material, color data of an identification region), and the like.
In some embodiments, the YOLO model may be derived based on a plurality of training samples and label training.
In some embodiments, the training samples comprise sample images. The label is color data of the sample image. The training data can be obtained based on historical data, and the labels of the training data can be determined in a manual labeling mode or an automatic labeling mode. For example, different regions corresponding to the sample image and classifications corresponding to the different regions are labeled as labels of the sample image.
The second determination module 530 may determine the plurality of identified regions through a YOLO model. For example, the second determination module 530 may input the image into a YOLO model, which outputs a plurality of identification regions of the image and a plurality of classifications that the identification regions correspond to (e.g., whether the identification region is a production material, color data of the identification region). The second determining module 530 may determine the color parameter of the produced material through one or more algorithms, for example, 4 identification areas and corresponding 4 RGB values of the output image of the YOLO model, and the second determining module 530 may average the 4 RGB values and determine the RGB average value as the color parameter of the produced material.
In some embodiments of the present description, the YOLO model is used to segment and determine the multiple identification areas of the image, and the color parameters of the produced materials are determined based on the color data of the multiple identification areas, so as to further ensure the accuracy of the color parameters of the produced materials.
And step 320, determining the predicted performance parameters at a future moment through a product abnormity prediction model based on the performance parameter sequence and the cooling temperature sequence. In some embodiments, step 320 may be performed by the second determination module 530.
The predicted performance parameter may refer to a predicted performance parameter of the produced material at a future time. The quality of the produced materials at a future time can be obtained or evaluated through the predicted performance parameters of the produced materials.
The product abnormity prediction model refers to a model capable of predicting performance parameters at a certain future time. In some embodiments, the type of product anomaly prediction model may be a variety of machine learning models, such as Deep Neural Networks (DNNs), recurrent Neural Networks (RNNs), and the like. The choice of model type may be case specific.
In some embodiments, the inputs to the product anomaly prediction model may include a sequence of performance parameters, a sequence of cooling temperatures, and the like. The output of the product anomaly prediction model may include predicted performance parameters at some future time. The input to the product anomaly prediction model may include a sequence of performance parameters, a sequence of cooling temperatures for one or more production cycles.
In some embodiments, a product anomaly prediction model may be derived based on a plurality of training samples and label training.
In some embodiments, the training samples include a sequence of sample performance parameters, a sequence of sample cooling temperatures. The label predicts a performance parameter for a sample at a time in the future. The training data can be obtained based on historical data, and the labels of the training data can be determined in a manual labeling mode or an automatic labeling mode. For example, the actual performance parameters (e.g., thermal parameters, rheological parameters, color parameters, etc.) at a future time corresponding to the sample performance parameter sequence and the sample cooling temperature sequence are labeled as labels of the sample performance parameter sequence and the sample cooling temperature sequence.
In some embodiments, the second determining module 530 may obtain the predicted performance parameter at a future time through a product anomaly prediction model. For example, the second determining module 530 may input the performance parameter sequence and the cooling temperature sequence of one or more past production cycles into the product abnormality prediction model, and the product abnormality prediction model outputs the predicted performance parameters at a future time.
Illustratively, one production cycle is 10 minutes, the production start time is 8 00, and the production time of the first produced material is 8; the output time of the second batch of output materials is 8, and the second determining module 530 may obtain a performance parameter sequence composed of the performance parameters of the two production cycles and a cooling temperature sequence composed of the cooling temperatures of the two production cycles. The second determining module 530 may input the performance parameter sequence and the cooling temperature sequence into the product anomaly prediction model, and predict the predicted performance parameters of the third batch of produced materials with the output time of 8. When the third production cycle is finished, the second determining module 530 may input the performance parameter sequence and the cooling temperature sequence formed in the three production cycles into the product abnormality prediction model, and predict the predicted performance parameter of the fourth batch of output materials in the next production cycle (output time is 8).
In some embodiments, the second determination module 530 may predict the predicted performance parameter for the next production cycle from the sequence of performance parameters and the sequence of cooling temperatures for the single production cycle at the current time. For example, as described above, a production cycle is 10 minutes, the production start time is 8; the second determining module 530 may input the performance parameter sequence and the cooling temperature sequence of the first batch of produced materials into the product anomaly prediction model, and predict the predicted performance parameters of the second batch of produced materials with the output time of 8; the second determining module 530 can input the series of performance parameters and the series of cooling temperatures of the second batch of produced materials into the product anomaly prediction model, and predict the predicted performance parameters of the third batch of produced materials with the production time of 8.
In some embodiments of the present description, a product anomaly prediction model is used to obtain a predicted performance parameter at a future time (e.g., a next production cycle), so that the quality of a material output at the future time can be determined based on the predicted performance parameter, which is helpful for determining whether an anomaly exists in the next production cycle, and when the anomaly exists, the material can be processed in time (e.g., production is stopped), thereby reducing material waste. In some embodiments of the present disclosure, the accuracy of determining whether there is an abnormality in the quality of the produced material at a future time may be further improved by not only predicting the performance parameter at a future time through the historical performance parameter sequence, but also considering the influence of the historical cooling temperature sequence.
And step 330, performing exception handling when the performance parameter or the predicted performance parameter does not meet a second preset condition. In some embodiments, step 330 may be performed by the second determination module 530.
The second predetermined condition may refer to a condition that a performance parameter or predicted performance parameter of the produced material needs to satisfy. For example, the thermal parameters, rheological parameters, color parameters, etc. of the produced material need to satisfy the conditions (such as preset ranges, etc.). In some embodiments, the second preset condition may have a plurality of expressions. For example, the second predetermined condition is that the performance parameters of the product material are within a predetermined range. Illustratively, the thermal conductivity in the thermal parameter is between a preset range a-b; the rheological parameter is in a preset range c-d; the color parameters are within a preset range e-f, and the like.
In some embodiments, the second preset condition may be set based on experience. For example, the above preset range may be set based on experience. In some embodiments, the setting of the second predetermined condition may be determined based on relevant standards for the production of modified plastics, i.e. the performance parameters of the output materials need to meet the relevant production standards. In some embodiments, the second preset condition may be set according to actual requirements.
In some embodiments, the second determining module 530 may perform exception handling when the performance parameter or the predicted performance parameter does not satisfy the second preset condition. For example, the second judging module 530 may issue an alarm or stop production through the modified plastic production apparatus when the performance parameter does not satisfy the second preset condition. In some embodiments, the second determining module 530 may perform exception handling when the predicted performance parameter does not satisfy the second preset condition. For example, the second determining module 530 may stop the production of the modified plastic production apparatus and issue an alarm when the predicted performance parameter does not satisfy the second preset condition. And after the abnormity of the modified plastic production device is processed, entering the next production period, for example, manually overhauling the modified plastic production device, and starting the next production period after the abnormity is eliminated.
In some embodiments of this specification, through obtaining the performance parameter (such as thermal parameter, rheological parameter, color and luster parameter) of output material, can be based on whether performance parameter satisfies preset condition, and then judge whether the quality of output material exists unusually, when the product material exists unusually, make exception handling, can carry out real time monitoring to the production process, guarantee the quality of output material, reduce the waste of material.
FIG. 4 is yet another exemplary flow diagram of a method of producing a modified plastic, according to some embodiments herein. As shown in fig. 4, the process 400 includes the following steps. In some embodiments, the process 400 may be performed by the processor 110 (e.g., the first determining module 520).
The composite abnormality index refers to a composite index that can indicate a temperature abnormality rate and a performance abnormality rate. The temperature abnormality rate may refer to a degree that the temperature exceeds a preset range, for example, the preset range of the temperature is 190 ℃ to 220 ℃, the temperature abnormality rate at 230 ℃ is 4.5%, and the abnormality rate is calculated by the formula (230-220)/220; for another example, the temperature abnormality rate at 170 ℃ is-10.5%, and the abnormality rate is calculated by the formula (170-190)/190. The performance anomaly rate may refer to the extent to which a performance parameter of the produced material is outside a predetermined range, for example, a predetermined range of thermal conductivity of the thermal parameter of 0.25 Watts per meter Kelvin (W/m.K)
0.35W/m.Kelvin (W/m.K), and a performance abnormality rate of 0.2 (W/m.K) in thermal conductivity of-20%, which is calculated by the formula (0.2-0.25)/0.25. In some embodiments, the temperature preset range and the performance preset range may be determined empirically. In some embodiments, the temperature preset range and the performance preset range may be determined by related production standards. In some embodiments, the composite abnormality index may reflect the magnitude of the likelihood of an abnormality occurring. For example, the larger the composite abnormality index, the greater the likelihood of an abnormality occurring.
In some embodiments, the first determining module 520 may obtain the composite abnormality index through a certain algorithm. For example, the first determining module 520 performs a weighted summation on the temperature abnormality rate and the performance abnormality rate to obtain a comprehensive abnormality index. In some embodiments, the first determining module 520 may determine the weight according to a preset rule. For example, the weight may be determined based on the magnitude of the effect of temperature and/or performance parameters, etc. on the quality of the produced material. Illustratively, the temperature has a large influence on the quality of the produced material, and the preset weight is 0.3; the influence of the thermal conductivity in the thermal parameters on the quality of the produced materials is small, the preset weight is 0.1, the influence of the rheological parameters on the quality of the produced materials is general, and the preset weight is 0.15 and the like.
And step 420, performing exception handling when the comprehensive exception index is higher than the preset value and does not meet a third preset condition.
The third preset condition may refer to a minimum composite anomaly index that requires exception handling. For example, the composite abnormality index may be a condition to be satisfied (e.g., a predetermined range). In some embodiments, the third preset condition may be determined based on experience. For example, the above preset range may be set based on experience. In some embodiments, the third preset condition may be set according to actual requirements.
In some embodiments, the first determining module 520 may perform exception handling when the composite exception index is higher than a preset value. For example, when the comprehensive abnormality index is higher than the preset value, the first judgment module 520 may issue an alarm or stop production through the modified plastic production apparatus.
In some embodiments of the present description, by obtaining a comprehensive abnormal index (such as a temperature abnormal rate, a performance abnormal rate, etc.), whether the comprehensive abnormal index is higher than a preset value or not can be determined, and then whether the temperature at a future time or the performance parameter of the produced material is abnormal or not can be determined.
It should be noted that the above description of the flow is for illustration and description only and does not limit the scope of the application of the present specification. Various modifications and alterations to the flow may occur to those skilled in the art, given the benefit of this description. However, such modifications and variations are intended to be within the scope of the present description.
Fig. 5 is an exemplary block diagram of a production system for modified plastics, shown in accordance with some embodiments herein. In some embodiments, one or more modules in system 500 may be provided in processor 110 in fig. 1. As shown in fig. 5, system 500 may include at least the following modules:
the production processing module 510 can be used for performing stirring, extruding, cooling and cutting processing on production raw materials to obtain modified plastic particles. For more details on the modified plastic particles and obtaining the modified plastic particles, refer to fig. 2 and the related description thereof, and are not repeated herein.
The first determining module 520 may be configured to obtain an extrusion temperature sequence and a cooling temperature sequence, and perform exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not satisfy a first preset condition. For more details of the extrusion temperature sequence and the obtaining method thereof, the cooling temperature sequence and the obtaining method thereof, and the first preset condition, reference may be made to fig. 2 and the related description thereof, which are not repeated herein.
In some embodiments, the first determining module 520 may be configured to obtain predicted temperature information at a future time through a temperature prediction model based on the extrusion temperature sequence and the cooling temperature sequence; and when the predicted temperature information does not meet a first preset condition, performing exception handling. For more details on the temperature prediction model and exception handling, reference may be made to fig. 2 and its associated description, which are not repeated herein.
The second determining module 530 may be configured to obtain performance parameters of the output material, where the performance parameters include at least one of thermal parameters, rheological parameters, and color parameters; and the output material comprises the cooled semi-finished product or the modified plastic particles, and when the performance parameters do not meet second preset conditions, exception handling is carried out. For more details on the performance parameters of the produced materials and the second predetermined condition, refer to fig. 3 and the related description thereof, which are not repeated herein.
In some embodiments, the second determining module 530 may be configured to obtain images of the produced material under a plurality of preset-angle light sources; determining the color and luster parameter through an image recognition model based on the image. For more details on the image recognition model and the color parameters, reference may be made to fig. 3 and the related description thereof, which are not repeated herein.
It should be understood that the system and its modules shown in FIG. 5 may be implemented in a variety of ways. For example, in some embodiments the system and its modules may be implemented in hardware, software, or a combination of software and hardware.
It should be noted that the above description of the system and its modules is for convenience only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the production processing module, the first determining module and the second determining module disclosed in fig. 5 may be different modules in a system, or may be a module that implements the functions of two or more modules. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the specification. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the foregoing description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit-preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of the present specification shall control if they are inconsistent or inconsistent with the statements and/or uses of the present specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present specification can be seen as consistent with the teachings of the present specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (4)
1. A method of producing a modified plastic comprising:
stirring, extruding, cooling and cutting the production raw materials to obtain modified plastic particles;
in the extrusion process, obtaining an extrusion temperature sequence;
acquiring a cooling temperature sequence in the cooling process;
acquiring performance parameters of output materials, wherein the performance parameters comprise one or more of thermal parameters, rheological parameters and color parameters, and the output materials comprise cooled semi-finished products or modified plastic particles; wherein obtaining the color parameter comprises:
acquiring images under a plurality of preset angle light sources, wherein the preset angle light sources are light sources with specified colors at preset specified angles;
segmenting the image based on a YOLO model, determining a plurality of identification areas, and processing color data of the plurality of identification areas based on an algorithm to determine the color parameter;
when the extrusion temperature sequence and/or the cooling temperature sequence do not meet a first preset condition, performing exception handling; wherein, when the extrusion temperature sequence and/or the cooling temperature sequence do not meet a first preset condition, the making of the exception handling comprises the following steps:
acquiring predicted temperature information at a future moment through a temperature prediction model based on the extrusion temperature sequence and the cooling temperature sequence;
when the predicted temperature information does not meet the first preset condition, performing exception processing;
when the performance parameter does not satisfy a second preset condition, performing the exception handling, wherein when the performance parameter does not satisfy the second preset condition, performing the exception handling comprises:
determining the predicted performance parameters at a certain future moment through a product abnormity prediction model based on a performance parameter sequence and the cooling temperature sequence;
and when the predicted performance parameter does not meet the second preset condition, performing exception processing.
2. A system for producing a modified plastic, comprising:
the production processing module is used for stirring, extruding, cooling and cutting the production raw materials to obtain modified plastic particles;
the second judgment module is used for acquiring performance parameters of output materials, wherein the performance parameters comprise at least one of thermal parameters, rheological parameters and color parameters, and the output materials comprise cooled semi-finished products or modified plastic particles;
the second determination module is further configured to:
acquiring images under a plurality of preset angle light sources, wherein the preset angle light sources are preset light sources with specified colors at a plurality of specified angles;
segmenting the image based on a YOLO model, determining a plurality of identification areas, and processing color data of the plurality of identification areas based on an algorithm to determine the color parameter;
the device comprises a first judgment module, a second judgment module and a control module, wherein the first judgment module is used for acquiring an extrusion temperature sequence and a cooling temperature sequence, and performing exception handling when the extrusion temperature sequence and/or the cooling temperature sequence do not meet a first preset condition;
the first determining module is further configured to:
acquiring predicted temperature information at a future moment through a temperature prediction model based on the extrusion temperature sequence and the cooling temperature sequence;
when the predicted temperature information does not meet the first preset condition, performing exception processing;
the second judging module is further configured to perform the exception handling when the performance parameter does not satisfy a second preset condition;
the second determination module is further configured to:
determining the predicted performance parameters at a certain future moment through a product abnormity prediction model based on a performance parameter sequence and the cooling temperature sequence;
and when the predicted performance parameter does not meet the second preset condition, performing exception processing.
3. A modified plastic production apparatus comprising a processor for performing the modified plastic production method of claim 1.
4. A computer-readable storage medium storing computer instructions which, when read by a computer, cause the computer to perform the method for producing a modified plastic according to claim 1.
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| CN117261028A (en) * | 2023-03-16 | 2023-12-22 | 苏州博之顺材料科技有限公司 | Automatic control system and method for modified plastic production |
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