CN111007817A - Equipment processing workpiece quality detection method and device and computer readable storage medium - Google Patents
Equipment processing workpiece quality detection method and device and computer readable storage medium Download PDFInfo
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The application provides a method and a device for detecting the quality of a device processing workpiece and a computer readable storage medium, which relate to the technical field of quality detection and comprise the following steps: acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece processing process; determining a preset standard operation data sequence corresponding to the actual operation data sequence; comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result; and determining the quality of the workpiece processed by the equipment according to the comparison result, and solving the technical problem of lower accuracy of the quality detection result of the workpiece processed by the equipment.
Description
Technical Field
The present disclosure relates to the field of quality detection technologies, and in particular, to a method and an apparatus for detecting quality of a device-processed workpiece, and a computer-readable storage medium.
Background
The quality unification of same product has important meaning to product quality control, but the factory is carrying out batch machining work piece in-process, because the operation workman level differs, workman's state is unstable to and factors such as equipment running state, lead to the product can't form comparatively stable processingquality.
At present, the monitoring of the processing quality of a workpiece mainly focuses on processing parameters and flow management in the processing process, and the methods are often affected by human factors, so that the accuracy of the quality detection result of the workpiece processed by equipment is low.
Disclosure of Invention
The invention aims to provide a method and a device for detecting the quality of a workpiece machined by equipment and a computer readable storage medium, so as to solve the technical problem of low accuracy of the quality detection result of the workpiece machined by the equipment.
In a first aspect, an embodiment of the present application provides a method for detecting quality of a device-processed workpiece, which is applied to an electronic device, and the method includes:
acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece processing process;
determining a preset standard operation data sequence corresponding to the actual operation data sequence;
comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result;
and determining the quality of the workpiece processed by the equipment according to the comparison result.
In one possible implementation, the step of determining a preset standard operation data sequence corresponding to the actual operation data sequence includes:
classifying first actual operation data which are generated firstly according to time sequence in the actual operation data sequence by utilizing a classification model to obtain a first actual processing workpiece processed by the equipment when the first actual operation data are generated;
determining first preset standard operation data generated by presetting the equipment when the first actually-machined workpiece is machined, and second preset standard operation data generated by presetting the equipment after the first actually-machined workpiece is machined according to a time sequence;
and forming a preset standard operation data sequence by the first preset standard operation data and the second preset standard operation data according to a time sequence.
In one possible implementation, the actual operating data includes at least one of current, torque, and rotational speed of the device.
In one possible implementation, the step of obtaining the actual operation data sequence generated by the apparatus in time sequence during the workpiece processing further includes:
collecting a plurality of operating data generated by the equipment according to a time sequence;
segmenting the plurality of operation data by utilizing a time series segmentation clustering TICC algorithm to obtain operation data corresponding to the standby state of the equipment and operation data corresponding to the workpiece machining state of the equipment;
and taking an operation data sequence formed by the operation data corresponding to the workpiece machining state according to a time sequence as an actual operation data sequence generated by the equipment according to the time sequence in the workpiece machining process.
In one possible implementation, the first actual operation data generated in time sequence is: and in the actual operation data sequence, the actual operation data is generated by the equipment from the initial time point to the end of the preset time period according to the time sequence.
In one possible implementation, the classification model is a long short term memory network LSTM classification model.
In one possible implementation, during the pre-training process of the LSTM classification model, the data input to the initial neural network model is the preset standard operating data sequence with a label that is a standard workpiece of the equipment that is preset to be processed when the preset standard operating data sequence is generated.
In one possible implementation, the step of determining the quality of the workpiece processed by the equipment according to the comparison result comprises:
and determining the quality of the workpiece processed by the equipment according to the difference degree between the actual operation data sequence and the preset standard operation data sequence in the comparison result.
In a second aspect, an apparatus for detecting the quality of a machined workpiece is provided, which is applied to an electronic apparatus, and comprises:
the acquisition module is used for acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece processing process;
the first determining module is used for determining a preset standard operation data sequence corresponding to the actual operation data sequence;
the comparison module is used for comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result;
and the second determining module is used for determining the quality of the workpiece processed by the equipment according to the comparison result.
In a third aspect, embodiments of the present application further provide a computer-readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect.
The embodiment of the application brings the following beneficial effects:
in the scheme, the actual operation data sequence generated by the equipment according to the time sequence in the workpiece processing process is firstly obtained, then the preset standard operation data sequence corresponding to the actual operation data sequence is determined, then the preset standard operation data sequence is compared with the actual operation data sequence according to the time sequence to obtain a comparison result, and finally the quality of the workpiece processed by the equipment is determined according to the comparison result, because each operation data generated according to the time sequence in the workpiece processing process of the equipment can reflect the complete flow in the workpiece processing process of the equipment to a certain extent, namely, in the workpiece process with similar processing quality, the operation data generated according to the time sequence can have high similarity, therefore, the actual operation data sequence generated according to the time sequence in the workpiece processing process is compared with the corresponding preset standard operation data sequence, the quality of the actually processed workpiece can be detected more accurately and objectively, the influence of human factors is reduced, the quality detection result of the actually processed workpiece of the equipment can be quantized more accurately and objectively, and the technical problem of low accuracy of the quality detection result of the actually processed workpiece of the equipment in the prior art is solved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a method for detecting quality of a workpiece machined by an apparatus according to an embodiment of the present disclosure;
fig. 2 is another schematic flow chart of a method for detecting the quality of a workpiece machined by an apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a device for detecting quality of a machined workpiece according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "comprising" and "having," and any variations thereof, as referred to in the embodiments of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In the description of the present application, the meaning of "at least one" means one or more than one unless otherwise stated.
Features and exemplary embodiments of various aspects of the present application will be described in detail below. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof. The present application is in no way limited to any specific configuration and algorithm set forth below, but rather covers any modification, replacement or improvement of elements, components or algorithms without departing from the spirit of the present application. In the drawings and the following description, well-known structures and techniques are not shown in order to avoid unnecessarily obscuring the present application.
At present, the quality unification of the same product has important significance for the product quality control, but in the process of processing workpieces in batches in a factory, due to factors such as different levels of operators, unstable states of workers, running states of equipment and the like, the product cannot form stable processing quality.
The monitoring of the workpiece processing quality mainly focuses on processing parameters and flow management in the processing process, and the methods are often influenced by human factors, so that the accuracy of the quality detection result of the workpiece processed by the equipment is low. Meanwhile, the quality of the machined workpieces on a large scale, multiple personnel and multiple devices is difficult to monitor.
Based on this, the method and the device for detecting the quality of the equipment processing workpiece and the computer readable storage medium provided by the embodiments of the present application can solve the technical problem that the accuracy of the quality detection result of the equipment processing workpiece is low in the prior art.
To facilitate understanding of the present embodiment, a method, an apparatus, and a computer-readable storage medium for quality inspection of a machined workpiece disclosed in the embodiments of the present application will be described in detail.
Fig. 1 is a schematic flow chart of a method for detecting quality of a workpiece machined by equipment according to an embodiment of the present disclosure. As shown in fig. 1, the method includes:
and S110, acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece machining process.
The actual operation data is the equipment operation data of the equipment in the actual workpiece processing process, and the actual operation data may include: at least one of current, torque, and rotational speed of the device. For example, when the apparatus starts to operate at an apparatus current of 5.7A in an actual workpiece machining process, and then the current becomes 4.5A when the machining process advances to the next process and the current becomes 2A when the machining process advances to the last process, actual operation data is 5.7A, 4.5A, … …, 2A. The torque, the rotating speed and the like of the equipment are the same.
And S120, determining a preset standard operation data sequence corresponding to the actual operation data sequence.
Wherein, the preset standard operation data sequence comprises: the method comprises the steps of presetting standard operation data corresponding to an actually processed workpiece and presetting standard operation data generated by equipment after the actually processed workpiece is processed according to a time sequence.
And S130, comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result.
In this step, as shown in fig. 2, the classified actually processed workpieces may be first matched with the standard operation data that should be generated when the actually processed workpieces are processed, and the abnormal situation in the processing process may be detected by comparing the difference between the actual operation data and the standard operation data.
And S140, determining the quality of the workpiece processed by the equipment according to the comparison result.
And detecting the quality and abnormal conditions of the processed workpiece by comparing the difference between the actual operation data and the matched standard operation data.
It should be noted that, because each operation data generated according to the time sequence in the equipment processing process can reflect the complete flow in the equipment processing workpiece process to a certain extent, if the same workpiece has similar quality, the operation data generated according to the time sequence should have high similarity.
In this embodiment, the operation data in the workpiece processing process is monitored, and the corresponding data of the standard workpiece is compared, so that the workpiece processing quality can be detected, online equipment processing quality monitoring is realized, and references are provided for personnel configuration, equipment management and product quality management of an enterprise. Compared with the existing method, the method provided by the embodiment can monitor the processing quality of the processed workpiece in real time, judge the processing abnormity, has low human interference factor, and has great advantages for quality control of the processed workpieces in large batch, with multiple personnel and multiple devices.
In some embodiments, the step S120 may specifically include the following steps:
step a, classifying first actual operation data which are generated firstly according to time sequence in an actual operation data sequence by using a classification model to obtain a first actual processing workpiece which is processed when equipment generates the first actual operation data;
b, determining first preset standard operation data generated by preset equipment when the first actual machined workpiece is machined and second preset standard operation data generated by the preset equipment after the first actual machined workpiece is machined according to a time sequence;
and c, forming a preset standard operation data sequence by the first preset standard operation data and the second preset standard operation data according to a time sequence.
For step a, the first actual operation data generated according to the time sequence is: and in the actual operation data sequence, the actual operation data is generated by the equipment from the initial time point to the end of the preset time period according to the time sequence.
Illustratively, the classification model is used for classifying a part of operation data which is generated firstly according to a time sequence in the plurality of actual operation data to obtain an actual processing workpiece corresponding to the part of operation data. Wherein, the part of the operation data generated firstly according to the time sequence is: and in the plurality of actual operation data, the operation data generated by the equipment is generated from the starting time point to the end of the preset time period according to the time sequence. When the operation data generated by the equipment for actually processing the workpiece is the operation data, the type of the product part actually processed by the equipment at the moment.
The classification model can be a variety of neural network models for classification, and is illustratively a long-short term memory network (LSTM) classification model. In this embodiment, the first machined workpiece is classified by using the algorithm of the LSTM classification model, and the type of the workpiece to be machined is determined. For example, as shown in fig. 2, the first 60 seconds of the equipment operation data is used in combination with the previously trained LSTM classification model to determine the 60 second processed workpiece, so as to match the workpiece with the standard data.
In some embodiments, during the pre-training process of the LSTM classification model, the data input to the initial neural network model is a pre-defined standard operating data sequence with a label, and the label is a standard machining workpiece machined by a pre-defined device when the pre-defined standard operating data sequence is generated.
As shown in fig. 2, in the process of performing classification model training, it is necessary to first collect operation data in the processing process of each type of workpiece, and perform classification training on the data by using LSTM to obtain a classification model.
By utilizing the trained LSTM classification model, the efficiency of the formal classification process can be improved, and a classification mechanism with higher accuracy, namely the type of the workpiece corresponding to the operation data, can be quickly obtained.
In some embodiments, before the step S110, the following steps may be further included:
step d, collecting a plurality of operation data generated by the equipment according to a time sequence;
e, segmenting a plurality of operation data by utilizing a time series segmentation clustering TICC algorithm to obtain operation data corresponding to the standby state of the equipment and operation data corresponding to the workpiece machining state of the equipment;
and f, taking an operation data sequence formed by the operation data corresponding to the workpiece machining state according to the time sequence as an actual operation data sequence generated by the equipment according to the time sequence in the workpiece machining process.
As shown in fig. 2, a TICC algorithm is used to divide the processing data, specifically, a data acquisition device is used to obtain real-time operation data generated according to a time sequence during the processing of a workpiece by a device, the TICC algorithm is used to divide the acquired operation data, the operation data is divided into a standby state and a working state, and if the current operation data is divided into the working state, the current operation data is used as a plurality of actual operation data generated by the device according to a time sequence during the processing of the workpiece.
Through the data segmentation process before classification, the subsequent classification process can be more targeted, and classification is only carried out on the operating data in the working state, so that the time required by the classification process is saved, and the processing efficiency of the classification process is improved.
In some embodiments, the step S140 may include the following steps:
and determining the quality of the workpiece processed by the equipment according to the difference degree between the actual operation data sequence and the preset standard operation data sequence in the comparison result.
Therefore, the quality of the machined workpiece can be judged according to the difference between the actual operation data sequence and the preset standard operation data sequence, so that the quality of the machined workpiece of the equipment can be accurately quantized, and the detected quality result is more objective and accurate.
Fig. 3 provides a schematic structural diagram of a device for detecting the quality of a machined workpiece. The apparatus is applied to an electronic device, and as shown in fig. 3, the device processing workpiece quality detection apparatus 300 includes:
an obtaining module 301, configured to obtain an actual operation data sequence generated by a device in a time sequence during a workpiece processing process;
a first determining module 302, configured to determine a preset standard operating data sequence corresponding to an actual operating data sequence;
the comparison module 303 is configured to compare the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result;
and a second determining module 304, configured to determine the quality of the workpiece processed by the apparatus according to the comparison result.
In some embodiments, the first determining module 302 is specifically configured to:
classifying first actual operation data which are generated firstly according to time sequence in an actual operation data sequence by utilizing a classification model to obtain a first actual processing workpiece which is processed when the equipment generates the first actual operation data;
determining first preset standard operation data generated by preset equipment when a first actually-processed workpiece is processed and second preset standard operation data generated by the preset equipment after the first actually-processed workpiece is processed according to a time sequence;
and forming a preset standard operation data sequence by the first preset standard operation data and the second preset standard operation data according to a time sequence.
In some embodiments, the actual operating data includes at least one of current, torque, and rotational speed of the device.
In some embodiments, the apparatus further comprises:
the acquisition module is used for acquiring a plurality of operation data generated by equipment according to a time sequence;
the segmentation module is used for segmenting the plurality of operation data by utilizing a time series segmentation clustering TICC algorithm to obtain operation data corresponding to the standby state of the equipment and operation data corresponding to the workpiece machining state of the equipment;
and the module is used for forming an operation data sequence by using the operation data corresponding to the workpiece machining state according to a time sequence, and using the operation data sequence as an actual operation data sequence generated by the equipment according to the time sequence in the workpiece machining process.
In some embodiments, the first actual operating data generated in chronological order is: and in the actual operation data sequence, the actual operation data is generated by the equipment from the initial time point to the end of the preset time period according to the time sequence.
In some embodiments, the classification model is a long short term memory network LSTM classification model.
In some embodiments, during the pre-training process of the LSTM classification model, the data input to the initial neural network model is a pre-defined standard operating data sequence with a label, and the label is a standard machining workpiece machined by a pre-defined device when the pre-defined standard operating data sequence is generated.
In some embodiments, the second determining module 304 is specifically configured to:
and determining the quality of the workpiece processed by the equipment according to the difference degree between the actual operation data sequence and the preset standard operation data sequence in the comparison result.
The quality detection device for the equipment processing workpiece provided by the embodiment of the application has the same technical characteristics as the quality detection method for the equipment processing workpiece provided by the embodiment, so that the same technical problems can be solved, and the same technical effects are achieved.
As shown in fig. 4, the electronic device 4 includes a memory 41 and a processor 42, where the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method provided in the foregoing embodiment.
Referring to fig. 4, the electronic device further includes: a bus 43 and a communication interface 44, the processor 42, the communication interface 44 and the memory 41 being connected by the bus 43; the processor 42 is for executing executable modules, such as computer programs, stored in the memory 41.
The Memory 41 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 44 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 43 may be an ISA bus, a PCI bus, an 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, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The memory 41 is used for storing a program, and the processor 42 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present application may be applied to the processor 42, or implemented by the processor 42.
The processor 42 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by instructions in the form of hardware, integrated logic circuits, or software in the processor 42. The Processor 42 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 41, and a processor 42 reads information in the memory 41 and performs the steps of the method in combination with hardware thereof.
Corresponding to the above method for detecting the quality of the machined workpiece by the equipment, an embodiment of the application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the steps of the above method for detecting the quality of the machined workpiece by the equipment.
The device for detecting the quality of the machined workpiece of the equipment provided by the embodiment of the application can be specific hardware on the equipment or software or firmware installed on the equipment and the like. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and 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 of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The 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 provided in 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 mobile control 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application.
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 (10)
1. A quality detection method for a device processing workpiece is applied to electronic equipment, and is characterized by comprising the following steps:
acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece processing process;
determining a preset standard operation data sequence corresponding to the actual operation data sequence;
comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result;
and determining the quality of the workpiece processed by the equipment according to the comparison result.
2. The method of claim 1, wherein the step of determining a predetermined standard operational data sequence corresponding to the actual operational data sequence comprises:
classifying first actual operation data which are generated firstly according to time sequence in the actual operation data sequence by utilizing a classification model to obtain a first actual processing workpiece processed by the equipment when the first actual operation data are generated;
determining first preset standard operation data generated by presetting the equipment when the first actually-machined workpiece is machined, and second preset standard operation data generated by presetting the equipment after the first actually-machined workpiece is machined according to a time sequence;
and forming a preset standard operation data sequence by the first preset standard operation data and the second preset standard operation data according to a time sequence.
3. The method of claim 1, wherein the actual operational data comprises at least one of current, torque, and rotational speed of the device.
4. The method of claim 1, wherein the step of obtaining a sequence of actual operational data generated by the apparatus in chronological order during the processing of the workpiece further comprises:
collecting a plurality of operating data generated by the equipment according to a time sequence;
segmenting the plurality of operation data by utilizing a time series segmentation clustering TICC algorithm to obtain operation data corresponding to the standby state of the equipment and operation data corresponding to the workpiece machining state of the equipment;
and taking an operation data sequence formed by the operation data corresponding to the workpiece machining state according to a time sequence as an actual operation data sequence generated by the equipment according to the time sequence in the workpiece machining process.
5. The method of claim 2, wherein the first actual operating data generated in time order is: and in the actual operation data sequence, the actual operation data is generated by the equipment from the initial time point to the end of the preset time period according to the time sequence.
6. The method of claim 2, wherein the classification model is a long short term memory network (LSTM) classification model.
7. The method of claim 6, wherein during the pre-training of the LSTM classification model, the data input to the initial neural network model is the pre-defined standard operational data sequence with a label that is a standard work piece that the equipment is pre-defined to process when generating the pre-defined standard operational data sequence.
8. The method of claim 1, wherein the step of determining the quality of the workpiece processed by the apparatus based on the comparison comprises:
and determining the quality of the workpiece processed by the equipment according to the difference degree between the actual operation data sequence and the preset standard operation data sequence in the comparison result.
9. The utility model provides a quality detection device of equipment processing work piece, is applied to electronic equipment, its characterized in that, the device includes:
the acquisition module is used for acquiring an actual operation data sequence generated by the equipment according to a time sequence in the workpiece processing process;
the first determining module is used for determining a preset standard operation data sequence corresponding to the actual operation data sequence;
the comparison module is used for comparing the preset standard operation data sequence with the actual operation data sequence according to a time sequence to obtain a comparison result;
and the second determining module is used for determining the quality of the workpiece processed by the equipment according to the comparison result.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to execute the method of any of claims 1 to 8.
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