CN114596308A - Information processing method, device, equipment and medium based on 5G network - Google Patents
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Abstract
The embodiment of the application provides an information processing method, an information processing device, information processing equipment and an information processing medium based on a 5G network, and relates to the technical field of molds, wherein the method comprises the following steps: acquiring the working information of the die based on a 5G network; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold; determining a processing model corresponding to each of the first spare part and the second spare part; respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result; if the detection result indicates that the mold is in an abnormal state, outputting state abnormality prompt information; the abnormal state prompt information is used for indicating the abnormal state of the die. The information processing method, device, equipment and medium based on the 5G network are used for prolonging the service life of the die.
Description
Technical Field
The present application relates to the field of mold technologies, and in particular, to a method, an apparatus, a device, and a medium for processing information based on a 5G network.
Background
A mold is a piece of process equipment used in conjunction with a mechanical machine tool to produce a product. At present, the work information of the die is usually recorded on a papery document manually.
In the related art, a document is usually found manually, and based on experience, it is determined whether related processing needs to be performed on a mold (for example, whether an abnormality exists in the mold is manually detected) according to related information (for example, maintenance time, detection time, and the like) recorded in the document, and the related processing cannot be performed on the mold in time, so that the service life of the mold is short.
Disclosure of Invention
The embodiment of the application provides an information processing method, device, equipment and medium based on a 5G network, which are used for solving the problem of short service life of a mold and have the beneficial effect of prolonging the service life of the mold.
In a first aspect, an embodiment of the present application provides an information processing method based on a 5G network, including:
acquiring the working information of the die based on a 5G network; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold;
determining a processing model corresponding to each of the first spare part and the second spare part;
respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result;
if the detection result indicates that the mold is in an abnormal state, outputting state abnormality prompt information; the abnormal state prompt information is used for indicating the abnormal state of the die.
In one possible design, the detection result includes flash detection information indicating whether the first spare part has flash;
through the processing model that first spare part corresponds, detect the processing to the working image, obtain flash detection information, include:
and sequentially carrying out principal component analysis, minimum distance classification processing and similarity comparison processing on the working image, the pre-stored standard image and preset threshold information through a processing model corresponding to the first spare part to obtain the flash detection information.
In one possible design, the detection result includes an image of the second spare part in which the abnormality exists;
through the processing model that the second spare part corresponds, detect the processing to the working image, the image that obtains the second spare part that has the anomaly includes:
and sequentially carrying out mean value filtering processing, region-of-interest extraction processing, minimum value region extraction processing, binarization conversion processing, detection matching processing, region feature extraction and feature identification processing on the working image through a processing model corresponding to the second spare part to obtain an abnormal image of the second spare part.
In one possible design, the outputting of the state anomaly prompting message includes:
and sending the detection result to the mobile terminal based on the 5G network so that the mobile terminal outputs the prompt information of the abnormal state and displays the detection result.
In one possible design, the operational information further includes operational parameters; the method further comprises the following steps:
judging whether the working parameters are within a preset working parameter range or not;
if the working parameters are not within the preset working parameter range, outputting parameter abnormality prompt information; the parameter abnormity prompt information is used for indicating the abnormity of the parameters of the die.
In one possible design, the method further includes:
acquiring the current use date;
determining the remaining service life of the preset spare parts according to the current service date, the first service date of the preset spare parts in the pre-stored mould and the preset service life;
if the residual service time is not more than the preset residual time, outputting spare part alarm information; spare part alarm information is used for instructing to change the preset spare part.
In a second aspect, an embodiment of the present application provides an information processing apparatus based on a 5G network, including: the device comprises an acquisition module and a processing module;
the acquisition module is used for acquiring the working information of the die by a 5G network; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold;
the processing module is used for determining processing models corresponding to the first spare part and the second spare part respectively;
the processing module is further used for respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result;
the processing module is also used for outputting a state abnormity prompting message if the detection result indicates that the mold is in an abnormal state; the abnormal state prompt information is used for indicating the abnormal state of the die.
In one possible design, the detection result includes flash detection information indicating whether the first spare part has flash; the processing module is further specifically configured to:
and sequentially carrying out principal component analysis, minimum distance classification processing and similarity comparison processing on the working image, the pre-stored standard image and preset threshold information through a processing model corresponding to the first spare part to obtain the flash detection information.
In one possible design, the detection result includes an image of the second spare part in which the abnormality exists; the processing module is further specifically configured to:
and sequentially carrying out mean value filtering processing, region-of-interest extraction processing, minimum value region extraction processing, binarization conversion processing, detection matching processing, region feature extraction and feature identification processing on the working image through a processing model corresponding to the second spare part to obtain an abnormal image of the second spare part.
In one possible design, the processing module is further specifically configured to:
and the 5G network sends the detection result to the mobile terminal so that the mobile terminal outputs the prompt information of the abnormal state and displays the detection result.
In one possible design, the operational information further includes operational parameters; the processing module is further configured to:
judging whether the working parameters are within a preset working parameter range or not;
if the working parameters are not in the preset working parameter range, outputting parameter abnormality prompt information; the parameter abnormity prompt information is used for indicating the abnormity of the parameters of the die.
In one possible design, the processing module is further to:
acquiring the current use date;
determining the remaining service life of the preset spare parts according to the current service date, the first service date of the preset spare parts in the pre-stored mould and the preset service life;
if the residual service time is not more than the preset residual time, outputting spare part alarm information; spare part alarm information is used for instructing to change the preset spare part.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method as in any one of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method of any one of the first aspect is implemented.
In a fifth aspect, the present application provides a computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of the first aspect.
The embodiment of the application provides an information processing method, a device, equipment and a medium based on a 5G network, wherein the method comprises the following steps: the 5G network acquires the working information of the die; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold; determining a processing model corresponding to each of the first spare part and the second spare part; respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result; if the detection result indicates that the mold is in an abnormal state, outputting state abnormality prompt information; the abnormal state prompt information is used for indicating the abnormal state of the die. In the method, a processing model corresponding to each of a first spare part and a second spare part is determined; respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result; if the detection result indicates that the mold is in an abnormal state, abnormal state prompt information is output and used for indicating that the state of the mold is abnormal, so that the information processing method based on the 5G network has the beneficial effects that field operators can timely perform relevant processing on the mold, and the service life of the mold is further prolonged.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario of an information processing method based on a 5G network according to an embodiment of the present application;
fig. 2 is a flowchart of an information processing method based on a 5G network according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a processing model corresponding to a first spare part according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a processing model corresponding to a second spare part according to an embodiment of the present application;
FIG. 5 is a functional framework diagram of a platform of a full lifecycle management system for molds according to an embodiment of the present application;
FIG. 6 is a resource architecture of a mold full lifecycle management system provided by an embodiment of the present application;
fig. 7 is a schematic structural diagram of an information processing apparatus based on a 5G network according to an embodiment of the present application;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with aspects of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the drawings of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the related art, documents are usually searched manually, and based on experience, it is determined whether related processing needs to be performed on the mold (for example, whether an abnormality exists in the mold is detected manually) according to related information (for example, maintenance time, detection time, and the like) recorded in the documents, and the related processing cannot be performed on the mold in time, so that the service life of the mold is short.
In the application, in order to prolong the service life of the mold, the inventor thinks of collecting the working image of the mold, obtaining a detection result based on image detection processing on the working image, and outputting the state abnormity prompt information to indicate the state abnormity of the mold when the detection result indicates that the mold is in the abnormal state, so that a field operator can timely perform relevant processing on the mold, and further prolong the service life of the mold.
An application scenario of the information processing method based on the 5G network provided in the embodiment of the present application is described below with reference to fig. 1.
Fig. 1 is a schematic application scenario diagram of an information processing method based on a 5G network according to an embodiment of the present application. As shown in fig. 1, the application scenario includes, for example: a mold full life cycle management system and an enterprise campus.
An Intelligent Guided Vehicle (IGV) and a machine tool may be included in the enterprise campus.
IGVs can be used to store molds, transport molds to machine tools, transport production materials to machine tools, and the like.
The mechanical machine may produce the product using the production material based on the mold.
The mechanical machine tool can collect the working information of the mold and send the working information to the full life cycle management system of the mold.
The die full life cycle management system is used for obtaining a detection result according to the working information of the die, and outputting state abnormity prompt information when the detection result indicates that the die is in an abnormal state.
It should be noted that, in the present application, for example, information transmission between the mold full-life cycle management system and the enterprise campus, and between devices in the enterprise campus (for example, between a machine tool and an IGV) may be performed based on a 5G network.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of an information processing method based on a 5G network according to an embodiment of the present application. As shown in fig. 2, the method includes:
s201, acquiring work information of the mold based on a 5G network, wherein the work information comprises a work image, and the work image comprises images of a first spare part and a second spare part in the mold.
Optionally, an execution subject of the information processing method based on the 5G network provided in the embodiment of the present application may be an electronic device, and may also be an information processing apparatus based on the 5G network and disposed in the electronic device, where the information processing apparatus based on the 5G network may be implemented by a combination of software and/or hardware. For example, the software includes, but is not limited to, the mold full lifecycle management system shown in the embodiment of fig. 1.
The mechanical machine tool is provided with a camera, and the camera can acquire images of the die to obtain working images.
Optionally, the working image may include a corresponding working image when the mold is in a mold-closing state, and may further include a corresponding working image when the mold is in a mold-opening state.
In practical application, the mechanical machine tool is also provided with a laser distance meter, when the mold is in a mold closing state, the laser distance meter starts to work, the laser distance meter detects and records the position (such as vertical position, horizontal position and the like) of the mold at the moment and sends a position signal to the camera, when the camera receives the position signal, the camera acquires an image of the mold to obtain a corresponding working image when the mold is in the mold closing state, and sends the corresponding working image when the mold is in the mold closing state to the electronic equipment based on a 5G network; when the mold is in the mold opening state, the laser range finder starts to work, detects and records the position (including a vertical position, a horizontal position and the like) of the mold at the moment, sends a position signal to the camera, and when the camera receives the position signal, carries out image acquisition on the mold, obtains a corresponding working image when the mold is in the mold opening state, and sends the corresponding working image when the mold is in the mold opening state to the electronic equipment based on a 5G network.
Optionally, the first spare part comprises at least one of: a first core (i.e., core), a mold cavity.
Optionally, the second spare part comprises at least one of: the mould comprises a sliding block, a thimble, a second mould core (namely a small mould core) and an insert.
Optionally, the working image may further include: images of the guide post and the guide sleeve, images of a mold cavity, a mold core, a product, a runner residue and the like before mold assembly.
S202, determining a processing model corresponding to each of the first spare part and the second spare part.
Optionally, the preset corresponding relationship, the first spare part identifier of the first spare part, and the second spare part identifier of the second spare part are pre-stored in the electronic device
The preset corresponding relation comprises a plurality of spare part identifications and model identifications corresponding to the spare part identifications.
The plurality of spare part identifiers include a first spare part identifier and a second spare part identifier.
Optionally, determining a first model identifier corresponding to the first spare part identifier and a second model identifier corresponding to the second spare part identifier according to a preset corresponding relationship and the first spare part identifier and the second spare part identifier; and determining the processing model corresponding to the first model identification as the processing model corresponding to the first spare part, and determining the processing model corresponding to the second model identification as the processing model corresponding to the second spare part.
The first spare part and the second spare part are respectively corresponding to a processing model, and the first spare part and the second spare part.
And S203, respectively detecting and processing the working images through the processing models corresponding to the first spare part and the second spare part to obtain detection results.
Alternatively, the detection result may include flash detection information indicating whether or not the first spare part has a flash, and an image of the second spare part in which an abnormality exists.
In one possible design, the detecting and processing the working image through a processing model corresponding to the first spare part to obtain the flash detection information includes:
and sequentially carrying out principal component analysis, minimum distance classification processing and similarity comparison processing on the working image, the pre-stored standard image and preset threshold information through a processing model corresponding to the first spare part to obtain the flash detection information.
Fig. 3 is a schematic structural diagram of a processing model corresponding to a first spare part according to an embodiment of the present disclosure. As shown in fig. 3, the processing model corresponding to the first spare part includes: a Principal Component Analysis (PCA) feature extraction model, a minimum distance classification model and a comparative analysis model.
The PCA feature extraction model is used for performing principal component analysis on the input working image, the standard image and the preset threshold information to obtain feature information corresponding to the working image and feature information corresponding to the standard image. Wherein the preset threshold information is a foreign object free threshold.
Optionally, the preset threshold information is obtained by training a sample image without a flash and a sample image with a flash by using a machine learning method. In practical applications, since the number of the sample images without the flash is usually much larger than the number of the sample images with the flash, the minimum distance classification algorithm in the bayesian classification algorithm is usually adopted to select the sample images without the flash and the sample images with the flash from the sample set (including a plurality of sample images without the flash and the sample images without the flash), so that the difference between the total number of the sample images without the flash and the sample images with the flash is smaller than or equal to the preset number.
Optionally, the PCA feature extraction model should also control the boundary information of noise and alien materials to ensure that the noise information is removed as much as possible to preserve the feature information of alien materials.
The minimum distance classification model (for example, a minimum distance classification algorithm model in a bayesian classification algorithm model) is used for performing minimum distance classification processing on the feature information corresponding to the working image and the feature information corresponding to the standard image to obtain difference information (for example, euclidean distance) between the feature information corresponding to the working image and the feature information corresponding to the standard image.
And the comparison analysis model is used for processing the difference information and the standard difference information to obtain the similarity between the difference information and the standard difference information, if the similarity is greater than or equal to a preset value, the first spare part is determined to have no flash, and otherwise, the first spare part is determined to have flash.
In a possible design, the detecting and processing of the working image through a processing model corresponding to the second spare part to obtain an image of the second spare part with an abnormality includes:
and performing mean value filtering processing, region-of-interest extraction processing, minimum value region extraction processing, binarization conversion processing, detection matching processing, region feature extraction and feature identification processing on the working image through a processing model corresponding to the second spare part to obtain an abnormal image of the second spare part.
Fig. 4 is a schematic structural diagram of a processing model corresponding to a second spare part according to an embodiment of the present application. As shown in fig. 4, the processing model corresponding to the second spare part includes: the system comprises an image preprocessing model, a minimum value region extraction model, a template matching algorithm model, a region feature extraction model and a feature identification model.
The image preprocessing model is used for carrying out mean value filtering processing on the working image to obtain an image to be processed.
The minimum value region extraction model is used for extracting a region of interest of the image to be processed to obtain an image region including the second spare part, and performing minimum value region extraction processing and binarization conversion processing on the image region including the second spare part to obtain a binary image.
The template matching algorithm model is used for determining a sub-image of the second spare part in the working image according to the binary image.
The regional characteristic extraction model is used for carrying out a regional characteristic extraction model on the subimage of the second spare part to obtain regional characteristic information corresponding to the second spare part.
The feature recognition model is used for recognizing and processing the regional feature information corresponding to the second spare part to obtain an image of the second spare part with abnormality.
S204, if the detection result indicates that the mold is in an abnormal state, outputting state abnormality prompt information; the abnormal state prompt information is used for indicating the abnormal state of the die.
In a possible design, the abnormal state prompt message may be sound and light alarm message, which prompts the abnormal state of the mold, and when the sound and light alarm message is seen by a field operator, the model can be processed in time (for example, the first spare part and the second spare part are detected and maintained).
In another possible design, the detection result is sent to the mobile terminal based on the 5G network, so that the mobile terminal outputs the state abnormality prompting information and displays the detection result.
Optionally, the mobile terminal includes an APP (Application) corresponding to the mold full-life-cycle management system, and may output the state exception notification information through the APP to be handled.
In the application, after the mobile terminal outputs the received state abnormality prompt information, a field operator can check the received detection result, and compare a standard image of the abnormal second spare part pre-stored in the mobile terminal with an image of the abnormal second spare part included in the detection result, so as to detect, maintain and the like the second spare part; and after the first spare part is determined to have the flash according to the flash detection information in the detection result, the flash in the first spare part can be cleaned. Further, after the second spare part is detected, repaired, and the like, and the flash in the first spare part is cleaned, first processing information indicating that the abnormal state prompting information has been processed may be sent to the electronic device through the mobile terminal based on the 5G network.
Optionally, the mobile terminal may further record a duration of the non-processing of the abnormal state prompting message, and when the duration is greater than or equal to a first preset duration, send the abnormal state prompting message to the mobile terminal of the relevant responsible person based on the 5G network.
Optionally, after the field operator performs related processing on the mold according to the detection result based on the state anomaly prompting information, if the mold is found to be normal, the field operator may send the detection result anomaly information to the electronic device through the mobile terminal based on the 5G network.
In the information processing method based on the 5G network provided by the embodiment of the application, processing models corresponding to a first spare part and a second spare part respectively are determined; respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result; if the detection result indicates that the mold is in an abnormal state, abnormal state prompt information is output and used for indicating the abnormal state of the mold, so that field operators can timely perform relevant processing on the mold, and the service life of the mold is prolonged.
In one possible design, the operational information may also include operational parameters; the information processing method based on the 5G network provided in the embodiment of the present application may further include: judging whether the working parameters are within a preset working parameter range or not;
if the working parameters are not in the preset working parameter range, outputting parameter abnormality prompt information; the parameter abnormity prompt information is used for indicating the abnormity of the parameters of the die.
The operating parameters may include temperature, pressure, and the like. The preset operating parameter ranges may be pre-stored in the mold full lifecycle management system.
Optionally, the outputting the parameter abnormality prompting information includes: and sending the working parameters to the mobile terminal based on the 5G network so that the mobile terminal outputs parameter abnormity prompt information and displays the working parameters.
Optionally, the parameter abnormality prompt information may be displayed for reminding through the to-do of the APP.
In the application, when the working parameters are not in the preset working parameter range, the abnormal parameter prompt information is output, so that field operators can adjust the working parameters in time, the working parameters are in the preset working parameter range, and the service life of the die is prolonged.
In a possible design, the information processing method based on a 5G network provided in an embodiment of the present application may further include: acquiring the current use date; determining the remaining service life of the preset spare parts according to the current service date, the first service date of the preset spare parts in the pre-stored mould and the preset service life;
if the residual service time is not more than the preset residual time, outputting spare part alarm information; spare part alarm information is used for instructing to change the preset spare part.
The preset spare parts can comprise the first spare part and/or the second spare part, and wearing parts such as guide posts, guide sleeves and air cylinders.
The first use date and the preset use duration of the preset equipment part may be previously stored in the mold full life cycle management system.
Alternatively, the remaining usage time period may be obtained by: determining the difference between the current use date and the first use date as the used time length; and determining the difference value between the preset using time length and the used time length as the residual using time length.
Alternatively, the remaining usage time may also be obtained by: acquiring prestored working time for producing a product by using a mold each time and preset using time; determining the sum of the time lengths of the working time lengths of the products produced by using the die each time; and determining the difference value of the sum of the preset using time length and the time length as the remaining using time length. Wherein the work time length of each time the mold is used to produce the product can be stored in the mold full life cycle management system in advance.
Optionally, the preset use duration may be obtained by the following method: acquiring working parameters (such as using environment, temperature, pressure and other information) of the mold; the working parameters are stored in the die full life cycle management system; analyzing the correlation between the working parameters of the die and the service life of the die by a linear analysis technology; and establishing a prediction model by combining the number of the products produced by the mold to predict the preset service life of the mold.
Optionally, the outputting the spare part alarm information may include: and sending spare part alarm information to the mobile terminal based on the 5G network so that the mobile terminal (such as a mobile terminal of a field operator or a related responsible person) displays related to-be-handled processing according to the spare part alarm information.
Further, when the relevant to-do processing is processed within a second preset time period (e.g., 3 months, 2 months, etc.), sending second processing information to the electronic device based on the 5G network, where the second processing information indicates that the relevant to-do processing has been processed (e.g., the preset spare part has been replaced); and when the related to-be-handled processing exceeds a second preset time (for example, 3 months, 2 months and the like) and is not processed, sending automatic alarm information to the electronic equipment based on the 5G network so that the full-life-cycle management system of the mold carries out alarm prompt according to the automatic alarm information.
Optionally, after receiving the spare part alarm information, the mobile terminal may also send an alarm information mail reminder and/or a short message reminder through the App.
In the information processing method based on the 5G network, the current use date is acquired; determining the remaining service life of the preset spare parts according to the current service date, the first service date of the preset spare parts in the pre-stored mould and the preset service life; if the residual service time is not longer than the preset residual time, spare part alarm information is output, so that the preset spare part can be replaced in time, the service life of the die is prolonged, the die is prevented from being damaged in the production and manufacturing process, and the production and manufacturing efficiency of the die is improved.
Different from the prior art, in the prior art, the first use date of the preset equipment part is preserved by adopting a papery receipt manually, the preset use time is long, the residual use time is long by manual calculation, so that an operator cannot replace the equipment part in time, and the service life of a die is shortened. In the application, the current use date, the first use date and the preset use duration are recorded in the full-life-cycle management system of the mold, manual paper record is replaced, and digital operation is realized, so that an operator can replace spare parts in time, and the service life of the mold is prolonged.
Fig. 5 is a functional framework diagram of a platform of a mold full-life-cycle management system according to an embodiment of the present application. As shown in fig. 5, includes: edge layer, platform layer, application layer (solution), user layer.
The edge layer comprises industrial equipment, a mold internet of things product and a high-end AP (Access Point) product. For example, industrial equipment includes molds, injection molding machines, numerical controls, and three-coordinate systems. For example, the mold internet of things product comprises a camera, a laser range finder and the like. For example, high end AP products include wireless access points.
The edge layer may communicate with.
The platform layer includes industrial big data systems and big data analysis. For example, industrial big data systems include big databases, big data services, open platforms, and the like.
The application layer includes a variety of functions such as in-line toner, cloud simulation, raw material formulation, and the like.
Fig. 6 is a resource architecture of a mold full-life-cycle management system according to an embodiment of the present application. As shown in fig. 6, the resource hierarchy includes: a data source, a data layer, a capability layer, and an application layer.
The data source includes source data. For example, the source data may include mold design data, mold tooling data, mold life data, mold inspection data.
The data layer can be used for storing data, filtering, sorting, converting formats and the like. The data layers may include a primary data warehouse, a distributed database, detailed data, summarized data, a major and minor receipt (M/R), a distributed storage system for structured data (HBase), a data warehouse tool (Hive), a distributed file system HDFS.
The capability layer is used for processing the data provided by the data layer, for example, the processing includes multidimensional processing, statistics processing, data mining processing and the like.
The application layer is used for carrying out relevant treatment (such as mold maintenance, mold replacement, service life analysis and the like) on the mold based on the treatment result provided by the capability layer.
Optionally, the full life cycle management system of the mold may store a three-dimensional model design draft of the mold, various process parameters of the mold, a standard image when the mold is in a mold closing state and a mold opening state, a preset use duration (use life) and a first use date of various (vulnerable) preset equipment parts in the mold, a storage position and a use position of the mold, a production cycle of the mold (time for completing one-time mold closing and opening), a utilization rate of the mold, a maintenance time node of the mold, a payment node of a model sale or purchase project, a progress cycle of the project, and the like.
Optionally, the full-life-cycle management system of the mold in the application can also obtain the number N of the production cavities of the mold according to the production rhythm of the mold and the production rhythm of the product, as well as the temperature, the pressure, the injection molding time, the raw material cooling time and the like, and display the number N of the production cavities. N is an integer greater than or equal to 1.
In this application, according to the production beat of mould and product to and temperature, pressure, the time of moulding plastics, raw materials cooling time etc, obtain the production chamber number N of mould, and adopt N chamber mould to carry out the production of product, can improve the utilization ratio that improves the mould, reduce the idle rate of mould.
In the mold industry, the management of the mold depends on manual work (such as manual storage and storage position recording) and the like, so that the management efficiency of the mold is low, the types of the mold are various, the utilization rate is low,
optionally, the monitoring and positioning of the mold can also be realized in the application. Wherein, the monitoring and the positioning of the die comprise the following steps: a common base station (for example, 3G \ 4G) reads label information (including storage position information and process parameters of the mold) from a special sensing label of the mold and sends the label information to a 5G base station; and the 5G base station continues signal preprocessing operations such as filtering and amplifying the label information and sends the preprocessed information to the die full-life-cycle management system, so that the die full-life-cycle management system can record the position of the die, and the die is monitored.
Different from the prior art, in prior art, the mould class is many, depositing of mould relies on artifically, the position is comparatively dispersed, the management of traditional mould relies on the manual work to check, artifical when using the mould need spend a large amount of times to carry out the check and the inquiry of mould, extravagant cost of labor, can't in time acquire the position at mould (in warehouse, the lathe, personnel exchange) place, and paste the bar code on the mould, confirm the position of mould through the information acquisition to the bar code, because the bar code drops easily, consequently, can't know the position of mould. In the application, the full-life-cycle management system of the die can record the position of the die, can clearly and timely determine the specific position of the die, and saves labor.
Fig. 7 is a schematic structural diagram of an information processing apparatus based on a 5G network according to an embodiment of the present application. As shown in fig. 7, the 5G network-based information processing apparatus 10 includes: an acquisition module 101 and a processing module 102;
an obtaining module 101, configured to obtain work information of a mold; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold;
a processing module 102, configured to determine a processing model corresponding to each of the first spare part and the second spare part;
the processing module 102 is further configured to perform detection processing on the working images through respective corresponding processing models of the first spare part and the second spare part, so as to obtain detection results;
the processing module 102 is further configured to output a state exception prompt message if the detection result indicates that the mold is in an exception state; the abnormal state prompt information is used for indicating the abnormal state of the die.
The information processing apparatus 10 based on the 5G network provided in the embodiment of the present application may execute the technical solutions shown in the above method embodiments, and the implementation principles and beneficial effects thereof are similar and will not be described herein again.
In one possible design, the detection result includes flash detection information indicating whether the first spare part has flash; the processing module 102 is further specifically configured to:
and sequentially carrying out principal component analysis, minimum distance classification processing and similarity comparison processing on the working image, the pre-stored standard image and preset threshold information through a processing model corresponding to the first spare part to obtain the flash detection information.
In one possible design, the detection result includes an image of the second spare part in which the abnormality exists; the processing module 102 is further specifically configured to:
and sequentially carrying out mean value filtering processing, region-of-interest extraction processing, minimum value region extraction processing, binarization conversion processing, detection matching processing, region feature extraction and feature identification processing on the working image through a processing model corresponding to the second spare part to obtain an abnormal image of the second spare part.
In one possible design, the processing module 102 is further specifically configured to:
and sending the detection result to the mobile terminal based on the 5G network so that the mobile terminal outputs the prompt information of the abnormal state and displays the detection result.
In one possible design, the operational information further includes operational parameters; the processing module 102 is further configured to:
judging whether the working parameters are within a preset working parameter range or not;
if the working parameters are not in the preset working parameter range, outputting parameter abnormality prompt information; the parameter abnormity prompt information is used for indicating the abnormity of the parameters of the die.
In one possible design, the processing module 102 is further configured to:
acquiring the current use date;
determining the remaining service life of the preset spare parts according to the current service date, the first service date of the preset spare parts in the pre-stored mould and the preset service life;
if the residual service time is not more than the preset residual time, outputting spare part alarm information; spare part alarm information is used for instructing to change the preset spare part.
The information processing apparatus 10 based on the 5G network provided in the embodiment of the present application may execute the technical solutions shown in the above method embodiments, and the implementation principles and beneficial effects thereof are similar and will not be described herein again.
Fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application. As shown in fig. 8, the electronic apparatus 20 includes: a transceiver 201, a memory 202, and a processor 203.
Among them, the transceiver 201 may include: a transmitter and/or a receiver. The transmitter may also be referred to as a transmitter, a transmission port or a transmission interface, and the like. A receiver may also be referred to as a receiver, a receive port, or a receive interface, and the like.
The transceiver 201, memory 202, and processor 203 are illustratively interconnected via a bus 204.
The memory 202 is used to store computer-executable instructions.
The processor 203 is configured to execute the computer executable instructions stored in the memory 202, so that the processor 203 executes the above-mentioned information processing method based on the 5G network.
In the embodiment shown in fig. 8, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the methods disclosed in the incorporated application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.
The memory may comprise high speed RAM memory, and may also include non-volatile storage NVM, such as disk memory.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The application also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when a processor executes the computer execution instructions, the information processing method based on the 5G network is realized.
The embodiment of the application provides a computer program product, which comprises a computer program, and the computer program is used for realizing the information processing method based on the 5G network when being executed by a processor.
The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium may also reside as discrete components in the apparatus.
The division of the unit is only a logical division, and other division ways are possible in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. An information processing method based on a 5G network is characterized by comprising the following steps:
acquiring the working information of the die based on a 5G network; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold;
determining a processing model corresponding to each of the first spare part and the second spare part;
respectively detecting and processing the working image through the processing models corresponding to the first spare part and the second spare part to obtain a detection result;
if the detection result indicates that the die is in an abnormal state, outputting state abnormality prompt information; the abnormal state prompt information is used for indicating that the state of the die is abnormal.
2. The method according to claim 1, wherein the detection result includes a flash detection information indicating whether there is flash in the first spare part;
through the processing model that first spare part corresponds, right the work image carries out detection process, obtains flash detection information, includes:
and sequentially carrying out principal component analysis, minimum distance classification processing and similarity comparison processing on the working image, the pre-stored standard image and preset threshold information through a processing model corresponding to the first spare part to obtain the flash detection information.
3. The method according to claim 1, wherein the detection result includes an image of the second spare part in which the abnormality exists;
detecting and processing the working image through a processing model corresponding to the second spare part to obtain an image of the second spare part with abnormality, wherein the method comprises the following steps:
and sequentially carrying out mean value filtering processing, region-of-interest extraction processing, minimum value region extraction processing, binarization conversion processing, detection matching processing, region feature extraction and feature identification processing on the working image through a processing model corresponding to the second spare part to obtain the image of the second spare part with the abnormality.
4. The method according to any one of claims 1 to 3, wherein the outputting of the state abnormality prompt message includes:
and sending the detection result to a mobile terminal based on the 5G network so that the mobile terminal outputs the state abnormity prompting information and displays the detection result.
5. The method according to any one of claims 1 to 3, wherein the operational information further comprises operational parameters; the method further comprises the following steps:
judging whether the working parameters are within a preset working parameter range or not;
if the working parameter is not in the preset working parameter range, outputting a parameter abnormity prompt message; the parameter abnormity prompt information is used for indicating that the parameters of the die are abnormal.
6. The method according to any one of claims 1 to 3, further comprising:
acquiring the current use date;
determining the remaining service life of a preset spare part according to the current service date, and the pre-stored first service date and the pre-stored preset service life of the preset spare part in the mold;
if the residual service time is not more than the preset residual time, outputting spare part alarm information; the spare part alarm information is used for indicating to replace the preset spare part.
7. An information processing apparatus based on a 5G network, comprising: the device comprises an acquisition module and a processing module;
the acquisition module is used for acquiring the working information of the die based on a 5G network; the working information comprises a working image, and the working image comprises images of a first spare part and a second spare part in the mold;
the processing module is used for determining a processing model corresponding to each of the first spare part and the second spare part;
the processing module is further configured to perform detection processing on the working image respectively through processing models corresponding to the first spare part and the second spare part, so as to obtain a detection result;
the processing module is further configured to output a state abnormality prompting message if the detection result indicates that the mold is in an abnormal state; the abnormal state prompt information is used for indicating that the state of the die is abnormal.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1 to 6.
9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the method of any one of claims 1 to 6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060023949A1 (en) * | 2004-07-27 | 2006-02-02 | Sony Corporation | Information-processing apparatus, information-processing method, recording medium, and program |
| CN102982322A (en) * | 2012-12-07 | 2013-03-20 | 大连大学 | Face recognition method based on PCA (principal component analysis) image reconstruction and LDA (linear discriminant analysis) |
| CN103761538A (en) * | 2014-01-10 | 2014-04-30 | 沈阳工业大学 | Traffic sign recognition method based on shape feature invariant subspace |
| CN108527803A (en) * | 2018-04-24 | 2018-09-14 | 宁波易联汇商信息科技有限公司 | Internet of Things suitable for mold monitor system and method |
| CN111062918A (en) * | 2019-12-10 | 2020-04-24 | 歌尔股份有限公司 | Anomaly detection method and device based on computer vision |
| CN111767802A (en) * | 2020-06-05 | 2020-10-13 | 京东数字科技控股有限公司 | Method and device for detecting abnormal state of object |
| CN112329698A (en) * | 2020-11-18 | 2021-02-05 | 安徽文香信息技术有限公司 | Face recognition method and system based on intelligent blackboard |
| CN112669316A (en) * | 2021-01-29 | 2021-04-16 | 南方电网调峰调频发电有限公司 | Power production abnormity monitoring method and device, computer equipment and storage medium |
| CN112927239A (en) * | 2021-02-22 | 2021-06-08 | 北京安德医智科技有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN113427731A (en) * | 2021-06-24 | 2021-09-24 | 广州中和互联网技术有限公司 | Injection molding machine mold detection system and method based on machine vision |
-
2022
- 2022-04-02 CN CN202210339988.9A patent/CN114596308A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060023949A1 (en) * | 2004-07-27 | 2006-02-02 | Sony Corporation | Information-processing apparatus, information-processing method, recording medium, and program |
| CN102982322A (en) * | 2012-12-07 | 2013-03-20 | 大连大学 | Face recognition method based on PCA (principal component analysis) image reconstruction and LDA (linear discriminant analysis) |
| CN103761538A (en) * | 2014-01-10 | 2014-04-30 | 沈阳工业大学 | Traffic sign recognition method based on shape feature invariant subspace |
| CN108527803A (en) * | 2018-04-24 | 2018-09-14 | 宁波易联汇商信息科技有限公司 | Internet of Things suitable for mold monitor system and method |
| CN111062918A (en) * | 2019-12-10 | 2020-04-24 | 歌尔股份有限公司 | Anomaly detection method and device based on computer vision |
| CN111767802A (en) * | 2020-06-05 | 2020-10-13 | 京东数字科技控股有限公司 | Method and device for detecting abnormal state of object |
| CN112329698A (en) * | 2020-11-18 | 2021-02-05 | 安徽文香信息技术有限公司 | Face recognition method and system based on intelligent blackboard |
| CN112669316A (en) * | 2021-01-29 | 2021-04-16 | 南方电网调峰调频发电有限公司 | Power production abnormity monitoring method and device, computer equipment and storage medium |
| CN112927239A (en) * | 2021-02-22 | 2021-06-08 | 北京安德医智科技有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| CN113427731A (en) * | 2021-06-24 | 2021-09-24 | 广州中和互联网技术有限公司 | Injection molding machine mold detection system and method based on machine vision |
Non-Patent Citations (2)
| Title |
|---|
| 程相等: "基于信息量的工控网络异常检测技术", 《计算机工程与设计》 * |
| 赖朝安: "《智能制造 模型体系与实施路径》", 31 January 2020, 机械工业出版社 * |
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Application publication date: 20220607 |