CN115983532A - Method, system, electronic device and storage medium for detecting production quality of equipment - Google Patents
Method, system, electronic device and storage medium for detecting production quality of equipment Download PDFInfo
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Abstract
The application relates to a method, a system, an electronic device and a storage medium for detecting the production quality of equipment, wherein the method comprises the following steps: acquiring real-time production data, and selecting data in a preset time period from the real-time production data according to actual production requirements; according to data in a preset time period, a real-time production detection model is constructed through a label-free automatic classification algorithm, and a standard curve is obtained; and importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through a standard curve in the production detection model, and judging whether the production quality reaches a qualified standard. By the method and the device, the problems of high industrial production quality detection cost, low efficiency and low accuracy in the related technology are solved, the equipment production quality detection accuracy is improved, and the cost is reduced.
Description
Technical Field
The present application relates to the field of quality testing, and more particularly to the testing of manufacturing equipment and the testing of manufactured products.
Background
In the production and manufacturing industry, in order to improve the production efficiency and the quality, enterprises can monitor and detect the production behavior so as to achieve the purposes of monitoring whether production equipment is damaged or not, whether the product quality is qualified or not and the like.
At present, the monitoring mode that the enterprise adopted mainly has two kinds, and firstly regularly physical spot check detects, secondly with the help of the intellectual detection system of production data. The main process of physical detection is to randomly inspect a certain number of products at fixed time intervals to perform destructive testing, if the products are qualified, the products are continuously produced, and if the products are not qualified, the machine parameters are required to be adjusted, and the products are continuously produced after being qualified again. It can be found that the traditional physical detection method has the problems of long time consumption, large destructiveness, discontinuity and the like, so that the method is likely to cause untimely detection of product quality, thereby generating a large amount of defective products and huge economic loss, and more seriously, the defective products which are not found in time can cause serious safety accidents in the use process of next-level finished products. For the intelligent detection which depends on data to carry out intelligent judgment, the data used for the intelligent judgment mainly come from the data generated by industrial production equipment in the actual production process, such as power, current, voltage, frequency, temperature and the like. The processes involve real-time data collection, storage, labeling, modeling, deployment, model tuning, equipment instruction control and the like, and are complex and high in cost. Therefore, in most cases, enterprises do not have enough manpower and material resources to build a complete set of intelligent detection system.
At present, no effective solution is provided for the problems of high cost, low efficiency and low accuracy of industrial production quality detection in the related technology.
Disclosure of Invention
The embodiment of the application provides a method, a system, an electronic device and a storage medium for detecting the production quality of equipment, so as to at least solve the problems of high cost, low efficiency and low accuracy of industrial production quality detection in the related technology.
In a first aspect, an embodiment of the present application provides a method for detecting production quality of a device, where the method includes:
acquiring real-time production data, and selecting data in a preset time period from the real-time production data according to actual production requirements;
according to the data in the preset time period, a real-time production detection model is constructed through a label-free automatic classification algorithm, and a standard curve is obtained;
and importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through the standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
In some embodiments, constructing the real-time production test model by a label-free automatic classification algorithm and obtaining the standard curve comprises:
acquiring label-free real-time sample data in a preset time period;
learning and classifying the label-free real-time sample data through a user-defined clustering analysis algorithm to obtain the characteristics of different clusters, automatically classifying real-time data curves in the production process through a relevance classification algorithm according to the characteristic information of the different clusters, and identifying and distinguishing qualified curves and unqualified curves;
and automatically rejecting unqualified curves, and fitting according to the qualified curves to obtain standard curves.
In some embodiments, the inspection and identification of imported production data by the standard curve in the production inspection model comprises:
and generating a production curve according to data generated by production equipment in real time, comparing and analyzing the production curve and a standard curve in the production detection model from multiple dimensions, and judging the current production quality grade according to the grade divided by each dimension.
In some embodiments, after the production detection model reaches a preset update parameter value, a model update instruction is automatically triggered, and the current production detection model is updated according to the model update instruction, so as to obtain a new model suitable for the current production environment.
In some of these embodiments, in constructing the production testing model, the method comprises:
generating a plurality of selectable models;
and selecting the best model from the plurality of selectable models according to the current production data, and storing the input and output data and the model version of the model into a model library for future model optimization and product quality tracking.
In some of these embodiments, prior to obtaining the real-time production data, the method includes:
the real-time production data generated by the production equipment is collected through the data collector and stored into the database in real time.
In a second aspect, an embodiment of the present application provides a system for detecting production quality of a device, where the system includes:
the acquisition module is used for acquiring real-time production data and selecting data in a preset time period from the real-time production data according to actual production requirements;
the construction module is used for constructing a real-time production detection model through a label-free automatic classification algorithm according to the data in the preset time period and obtaining a standard curve;
the detection module is used for importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through the standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
In some embodiments, the construction module is further configured to obtain non-tag real-time sample data within a preset time period,
learning and classifying the label-free real-time sample data by a self-defined cluster analysis algorithm to obtain the characteristics of different clusters, automatically classifying real-time data curves in the production process by a correlation classification algorithm according to the characteristic information of the different clusters, identifying and distinguishing qualified curves and unqualified curves,
and automatically rejecting unqualified curves, and fitting according to the qualified curves to obtain standard curves.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor, when executing the computer program, implements the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium, on which a computer program is stored, which when executed by a processor implements the method according to the first aspect.
Compared with the related art, the method for detecting the production quality of the equipment, provided by the embodiment of the application, acquires the real-time production data, and selects the data in the preset time period from the real-time production data according to the actual production requirement; according to data in a preset time period, a real-time production detection model is constructed through a label-free automatic classification algorithm, and a standard curve is obtained; and importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through a standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
The intelligent quality detection system capable of achieving the industrial production line standard can be constructed through the application, the system is combined with a data acquisition module matched with production equipment and an industrial control quality inspection terminal program, a whole set of automatic processes such as data real-time collection, modeling, deployment, optimization, production equipment control and the like are communicated, the requirement of automatic full-quantity online quality detection of enterprises is well met, and personalized modeling for each equipment is possible. The method provides a systematic scheme with high accuracy, low implementation difficulty and low cost for industrial manufacturing enterprises and intelligent equipment manufacturers to realize online real-time quality detection.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method of device production quality detection according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a product production curve according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a model calibration curve according to an embodiment of the present application;
FIG. 4 is a graph comparing schematic diagrams according to embodiments of the present application;
FIG. 5 is a block diagram of a system for device production quality detection according to an embodiment of the present application;
fig. 6 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
At present, the following problems exist in the related art of production quality detection:
firstly, in the modeling process, a developer firstly collects a large amount of sample data, then labels each sample data, and establishes a model by taking the labeled sample data as training data. However, the models created in this way are typically less accurate. Because each production equipment has unique physical characteristics, the production environment and energy supply of each equipment have slight differences, and if a model is established by using data generated by a certain equipment in a certain production environment, the model is not exactly suitable for monitoring the production quality of other equipment.
Secondly, in many cases, the product cannot be identified by naked eyes, so that in the actual production process, identification and confirmation need to be performed through destructive experiments, namely, in a manner of labeling the product. And when destructive experiments are carried out on a large number of products, the consumed cost is too large, and the efficiency is lower.
Therefore, in view of the existing problems, the present embodiment provides a method for detecting the production quality of a device, and fig. 1 is a flowchart of the method for detecting the production quality of a device according to the embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
and S101, acquiring real-time production data, and selecting data in a preset time period from the real-time production data according to actual production requirements.
Preferably, real-time production data generated by the production equipment is collected through a specific data collector and is stored in the database in real time. The database has the advantages of flexible storage, quick calculation and the like, can be quickly accessed no matter multi-dimensional data or high-frequency data of tens of thousands of points generated in one second, and provides data support for subsequent modeling, judgment and model updating optimization.
And after the real-time production data are obtained, selecting the data volume N in a preset time period from the real-time production data according to the current actual production requirement as a training sample for carrying out personalized modeling. In this embodiment, the amount of data generated by the production equipment in one hour is most preferable, for example, 200 pieces of data are generated by the equipment in one hour, and then N is 200. This means that the model can be established by acquiring the data volume of one hour of equipment production, and the requirement on timeliness in the actual industrial production process is basically met.
Step S102, according to data in a preset time period, a real-time production detection model is constructed through a label-free automatic classification algorithm, and a standard curve is obtained;
in this embodiment, a real-time production detection model is constructed by a label-free automatic classification algorithm according to the production data within the preset time period selected in step S101, and a standard curve is obtained. The method comprises the following specific steps:
s1, obtaining label-free real-time sample data in a preset time period, wherein the label-free means that training sample data are not labeled and specific information is unknown.
S2, learning and classifying the unlabeled real-time sample data through a custom cluster analysis algorithm, and discovering the inherent properties and rules among the sample data so as to determine the characteristics of different clusters.
And S3, automatically classifying the real-time data curves in the production process through a correlation classification algorithm according to the characteristic information of different clusters, and identifying and distinguishing qualified curves and unqualified curves. The correlation classification algorithm is to study the degree of correlation between variables based on correlation coefficients, and the correlation coefficients have a plurality of definition modes due to different study objects, preferably, the correlation coefficients in this embodiment are pearson correlation coefficients, and a specific calculation formula is shown in the following formula (1):
wherein r represents a correlation coefficient, X and Y represent two variables, respectively, X i Representing the eigenvalues of X in the ith dimension, Y i Representing the eigenvalues of Y in the ith dimension,
represents the mean value of X, and Y represents the mean value of Y.
It should be noted that the absolute value of r is between 0 and 1, and the correlation coefficient between variables is generally very strong between 0.8 and 1, strong between 0.6 and 0.8, moderate between 0.4 and 0.6, weak between 0.2 and 0.4, and very weak or no correlation between 0 and 0.2.
In the embodiment, the real-time data curves are automatically classified according to the strength of the correlation among the curves, so that qualified curves and unqualified curves are identified.
And S4, automatically eliminating unqualified curves, and further fitting acceptable curves to obtain a standard curve for subsequent production curve judgment. It should be noted that the model includes one or several standard curves.
In the embodiment, the mode of production, acquisition and modeling is adopted, the personalized online modeling is realized, the problem of low detection accuracy of the model constructed in the prior art is solved, and the technical problem that the label is required to be marked in advance in the prior art is solved through the designed label-free automatic classification algorithm. The accuracy, efficiency and cost of the equipment production quality detection are effectively improved.
Step S103, importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through a standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
After the production detection model is generated and obtained through the steps, the production equipment transmits the data of each production to the detection model in real time, the production curve generated by the production equipment in real time and the standard curve in the production detection model are compared and analyzed from multiple dimensions through the model, for example, correlation, peak power, the area contained below the curve, the area difference of the non-overlapped part of the two curves and the like are carried out, the difference grade is divided according to each dimension, the quality grade of the product is corresponded, and whether the quality of the secondary product is qualified or not can be judged in real time. It should be noted that the response time of the detection model judgment is usually within half a second.
Through the steps from S101 to S103, a complete set of intelligent quality detection system is designed in the embodiment, complete links including data real-time collection, storage, modeling, deployment, tuning, instruction automatic control and the like are opened, pain points which puzzle enterprises for a long time in the aspects of data acquisition, data storage, data marking, online model establishment and the like are solved, the requirements of the enterprises on the accuracy, timeliness and operability of equipment production quality detection are met, and the intelligent degree of the enterprises in the quality detection field is effectively enhanced continuously.
In some embodiments, after the production detection model reaches the preset update parameter value, the model update instruction is automatically triggered, and the current production detection model is updated according to the model update instruction, so as to obtain a new model suitable for the current production environment. Wherein the preset update parameter values include: model usage time, equipment production times, changes in equipment configuration parameters, and the like.
Compared with a fixed and unchangeable general model in the prior art, the model automatic adjustment mechanism is designed in the embodiment, and when the adaptability of the original model is reduced due to the change of the production environment or the change of the equipment, the model self-adaptive adjustment mechanism is automatically triggered, so that the change of the new production environment or the equipment is matched. The technical problem that the equipment needs to be modeled again due to the change of the production environment or the change of the equipment is solved. The accuracy of model detection is effectively improved.
In some embodiments, a plurality of alternative models are generated when the production test model is constructed; at the moment, the best model is selected from a plurality of selectable models according to the current production data, and the input and output data and the model version of the model are stored in a model library for future model optimization and product quality tracking.
Fig. 2 is a schematic diagram of a production curve of a product according to an embodiment of the present application, fig. 3 is a schematic diagram of a model standard curve according to an embodiment of the present application, and fig. 4 is a schematic diagram of a curve comparison according to an embodiment of the present application. The following specifically describes the practical application process of the above method for detecting the production quality of equipment, taking the quality detection of lathe machining as an example:
first, the data of the spindle of the lathe motor, such as real-time production data of load, current, torque, etc., are collected, and from these data, a production curve can be generated at each production process, as shown in fig. 2. After a certain amount of sample data is collected in real time, personalized modeling is carried out according to the sample data, and meanwhile, the model divides the production curve in the graph 2 into a qualified type and an unqualified type according to a label-free automatic classification algorithm, for example, a red curve in the graph 2 is seriously deviated and can be classified as an unqualified type, other curves can be classified as qualified types, and the remained qualified curves can be fitted, so that a standard curve is obtained, as shown in the graph 3.
After the model is established through the process, the judgment process is carried out, and the production curve generated by the subsequent lathe is compared with the standard curve in the model for identification, so that whether the produced product is qualified or not is judged. As shown in fig. 4, the curve 1 is a standard curve, and the curve 2 is a curve generated by a certain processing production, and it can be seen from the figure that the difference between the curve 2 and the curve 1 is far beyond the qualified range set by the model, so that the product piece corresponding to the curve 2 can be judged to be unqualified.
When the model is used for a plurality of times or after a certain time, the specific value is determined according to the production characteristics of different equipment, the system can automatically trigger the automatic model updating mechanism and recalculate new standard curves and parameters so as to adapt to the change of the production system.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The present embodiment further provides a system for detecting a production quality of a device, where the system is used to implement the foregoing embodiments and preferred embodiments, and details of which have been already described are omitted. As used below, the terms "module," "unit," "sub-unit," and the like may implement a combination of software and/or hardware of predetermined functions. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a system for detecting the production quality of a device according to an embodiment of the present application, and as shown in fig. 4, the system includes an acquisition module 51, a construction module 52, and a detection module 53:
the acquisition module 51 is used for acquiring real-time production data and selecting data in a preset time period from the real-time production data according to actual production requirements; the construction module 52 is configured to construct a real-time production detection model according to data in a preset time period by using a label-free automatic classification algorithm, and obtain a standard curve; and the detection module 53 is configured to import the data generated by the production equipment in real time into the production detection model, detect and identify the imported production data through a standard curve in the production detection model, and determine whether the production quality meets the qualified standard.
Through the system, the acquisition module 51 and the construction module 52 not only adopt the mode of production, acquisition and construction simultaneously to realize personalized online modeling, solve the problem of low accuracy of model detection constructed in the prior art, but also solve the technical problem of pre-labeling in the prior art through a designed label-free automatic classification algorithm. The accuracy, efficiency and cost of the equipment production quality detection are effectively improved.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
Note that each of the modules may be a functional module or a program module, and may be implemented by software or hardware. For a module implemented by hardware, the above modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
The present embodiment also provides an electronic device, comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
In addition, in combination with the method for detecting the production quality of the device in the above embodiments, the embodiments of the present application may provide a storage medium to implement. The storage medium having stored thereon a computer program; the computer program is executed by a processor to implement the method for detecting the production quality of the equipment in any one of the above embodiments.
In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for device production quality detection. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
In an embodiment, fig. 6 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 6, there is provided an electronic device, which may be a server, and an internal structure diagram of which may be as shown in fig. 6. The electronic device comprises a processor, a network interface, an internal memory and a non-volatile memory connected by an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used for providing calculation and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing an environment for an operating system and the running of a computer program, the computer program is executed by the processor to realize a method for detecting the production quality of the equipment, and the database is used for storing data.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is a block diagram of only a portion of the structure associated with the present application, and does not constitute a limitation on the electronic device to which the present application applies, and that a particular electronic device may include more or fewer components than shown, or combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of device production quality inspection, the method comprising:
acquiring real-time production data, and selecting data in a preset time period from the real-time production data according to actual production requirements;
according to the data in the preset time period, a real-time production detection model is constructed through a label-free automatic classification algorithm, and a standard curve is obtained;
and importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through the standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
2. The method of claim 1, wherein constructing a real-time production inspection model by a label-free automatic classification algorithm and obtaining a standard curve comprises:
acquiring label-free real-time sample data in a preset time period;
learning and classifying the label-free real-time sample data through a user-defined clustering analysis algorithm to obtain the characteristics of different clusters, automatically classifying real-time data curves in the production process through a relevance classification algorithm according to the characteristic information of the different clusters, and identifying and distinguishing qualified curves and unqualified curves;
and automatically rejecting unqualified curves, and fitting according to the qualified curves to obtain standard curves.
3. The method of claim 1 or 2, wherein the step of performing detection identification on the imported production data through the standard curve in the production detection model comprises:
and generating a production curve according to data generated by production equipment in real time, comparing and analyzing the production curve and a standard curve in the production detection model from multiple dimensions, and judging the current production quality grade according to the grade divided by each dimension.
4. The method of claim 1,
and after the production detection model reaches a preset updating parameter value, automatically triggering a model updating instruction, and updating the current production detection model according to the model updating instruction to obtain a new model suitable for the current production environment.
5. The method of claim 1, wherein in constructing the production testing model, the method comprises:
generating a plurality of selectable models;
and selecting the best model from the plurality of selectable models according to the current production data, and storing the input and output data and the model version of the model into a model library for future model optimization and product quality tracking.
6. The method of claim 1, wherein prior to obtaining real-time production data, the method comprises:
the real-time production data generated by the production equipment is collected through the data collector and stored into the database in real time.
7. A system for equipment production quality inspection, the system comprising:
the acquisition module is used for acquiring real-time production data and selecting data in a preset time period from the real-time production data according to actual production requirements;
the construction module is used for constructing a real-time production detection model through a label-free automatic classification algorithm according to the data in the preset time period and obtaining a standard curve;
the detection module is used for importing data generated by the production equipment in real time into a production detection model, detecting and identifying the imported production data through the standard curve in the production detection model, and judging whether the production quality reaches a qualified standard.
8. The system of claim 7,
the construction module is also used for acquiring the non-label real-time sample data in the preset time period,
learning and classifying the label-free real-time sample data by a self-defined cluster analysis algorithm to obtain the characteristics of different clusters, automatically classifying real-time data curves in the production process by a correlation classification algorithm according to the characteristic information of the different clusters, identifying and distinguishing qualified curves and unqualified curves,
and automatically rejecting unqualified curves, and fitting according to the qualified curves to obtain standard curves.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 6.
10. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any one of claims 1 to 6 when executed.
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| CN (1) | CN115983532A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523274A (en) * | 2023-07-04 | 2023-08-01 | 成都掠食鸟科技有限公司 | Computer remote production management method and system based on Internet of things technology |
| CN117311295A (en) * | 2023-11-28 | 2023-12-29 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
| CN117689270A (en) * | 2024-01-30 | 2024-03-12 | 领军(辽宁)科技有限公司 | Method, system and storage medium for improving quality management of power equipment production process |
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2023
- 2023-02-10 CN CN202310098638.2A patent/CN115983532A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523274A (en) * | 2023-07-04 | 2023-08-01 | 成都掠食鸟科技有限公司 | Computer remote production management method and system based on Internet of things technology |
| CN116523274B (en) * | 2023-07-04 | 2023-10-27 | 成都掠食鸟科技有限公司 | Computer remote production management method and system based on Internet of things technology |
| CN117311295A (en) * | 2023-11-28 | 2023-12-29 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
| CN117311295B (en) * | 2023-11-28 | 2024-01-30 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
| CN117689270A (en) * | 2024-01-30 | 2024-03-12 | 领军(辽宁)科技有限公司 | Method, system and storage medium for improving quality management of power equipment production process |
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