CN117873007B - Manufacturing flow management method, system, equipment and medium based on industrial Internet of things - Google Patents
Manufacturing flow management method, system, equipment and medium based on industrial Internet of things Download PDFInfo
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- CN117873007B CN117873007B CN202410270289.2A CN202410270289A CN117873007B CN 117873007 B CN117873007 B CN 117873007B CN 202410270289 A CN202410270289 A CN 202410270289A CN 117873007 B CN117873007 B CN 117873007B
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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
- 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]
- G05B19/41865—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 job scheduling, process planning, material flow
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
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Abstract
The application discloses a manufacturing flow management method, a manufacturing flow management system, manufacturing flow management equipment and manufacturing flow management media based on industrial Internet of things, which comprise the following steps: obtaining the qualification rate and the production rate of a target product produced in a preset period; comparing the qualification rate with a preset qualification threshold value, and if the qualification rate is smaller than the qualification threshold value, acquiring a product image of a defective product in the target product; acquiring the equipment type associated with the defective product according to the product image; wherein, the equipment type comprises production equipment and quality inspection equipment; performing fault detection on a plurality of production devices and/or performing fault detection on a plurality of quality inspection devices, and marking the production devices and/or quality inspection devices with unqualified fault detection as first fault devices; comparing the productivity with a preset productivity threshold, if the productivity is smaller than the productivity threshold, performing yield detection on a plurality of production devices, and marking the production devices with unqualified yield detection as second fault devices.
Description
Technical Field
The application relates to the technical field of industrial Internet of things, in particular to a manufacturing flow management method, a manufacturing flow management system, manufacturing flow management equipment and manufacturing flow management media based on the industrial Internet of things.
Background
The industrial Internet of things integrates various acquisition and control sensors or controllers with sensing and control capabilities into various links of an industrial production process through the Internet of things sensing and communication technology, so that the production efficiency is improved, the product quality is improved, the product cost and the resource consumption are reduced, and finally the traditional industry is improved to an intelligent new stage.
At present, when the industrial Internet of things is used in an intelligent manufacturing workshop, background management personnel can conveniently master the operation condition of the workshop in real time so as to quickly and uniformly schedule the workshop to ensure normal operation of production, in a manufacturing process, the product quality and the production efficiency are always primary pursuits of industrial manufacturing, a plurality of devices are generally configured in the manufacturing workshop to operate so as to form an automatic production line, and when the product quality or the production efficiency is problematic, the existing industrial manufacturing management method is difficult to accurately check out fault devices, manual check out is needed one by one, the number of the devices is more, and the troubleshooting efficiency of the fault devices is low.
Disclosure of Invention
The application mainly aims to provide a manufacturing flow management method, a manufacturing flow management system, manufacturing flow management equipment and a manufacturing flow management medium based on an industrial Internet of things, and aims to solve the technical problem that the troubleshooting efficiency of fault equipment is low when the existing industrial manufacturing management method aims at the problem of product quality or production efficiency.
In order to achieve the above object, the present application provides a manufacturing process management method based on industrial internet of things, comprising the following steps:
obtaining the qualification rate and the production rate of a target product produced in a preset period;
comparing the qualification rate with a preset qualification threshold value, and if the qualification rate is smaller than the qualification threshold value, acquiring a product image of a defective product in the target product;
acquiring the equipment type associated with the defective product according to the product image; wherein, the equipment type comprises production equipment and quality inspection equipment;
Performing fault detection on a plurality of production devices and/or performing fault detection on a plurality of quality inspection devices, and marking the production devices and/or quality inspection devices with unqualified fault detection as first fault devices;
And comparing the productivity with a preset productivity threshold, and if the productivity is smaller than the productivity threshold, performing yield detection on the plurality of production devices, and marking the production devices with unqualified yield detection as second fault devices.
Optionally, acquiring the device type associated with the defective product according to the product image includes:
Obtaining a defect degree value according to the product image; wherein the defect level value represents the ratio of the defective portion of the defective product to the standard product;
comparing the defect degree value with a preset first standard threshold value;
if the defect degree value is greater than or equal to the first standard threshold value, identifying the equipment type associated with the defective product as production equipment;
if the defect level value is less than the first standard threshold, identifying the type of equipment associated with the defective product as quality inspection equipment.
Optionally, obtaining the defect level value according to the product image includes:
Carrying out gray processing on the product image to obtain a gray image;
Noise reduction treatment is carried out on the gray level image;
threshold segmentation is carried out on the gray level image after noise reduction treatment so as to obtain a black-and-white image;
And comparing the black-and-white image with a preset database to obtain a defect degree value.
Optionally, performing fault detection on the plurality of production devices includes:
detecting target products with preset quantity produced by each production device to obtain the quantity of defective products;
comparing the number of defective products with a preset second standard threshold;
And if the number of defective products is greater than or equal to the second standard threshold, identifying the corresponding production equipment as first faulty equipment.
Optionally, performing fault detection on the plurality of quality inspection devices includes:
inputting the same number of sample products into each quality inspection device and standard device; wherein, a plurality of defective products are doped in the sample product, and the standard equipment is fault-free quality inspection equipment;
Obtaining the actual measurement defect rate of a sample product detected by each quality inspection device and the standard defect rate of a sample product detected by a standard device;
Comparing the measured defect rate with a standard defect rate;
And if the measured defect rate is greater than or equal to the standard defect rate, identifying the corresponding quality inspection equipment as first fault equipment.
Optionally, performing yield detection on the plurality of production devices includes:
Obtaining the product throughput of each production device at a preset time;
comparing the product throughput with a preset standard throughput value;
If the product throughput is less than the standard throughput value, the corresponding production facility is identified as a second malfunctioning facility.
Optionally, if the product throughput corresponding to all the production devices is greater than or equal to the standard production value, the method further includes:
obtaining the average yield value of the production quantity data of multiple groups of products;
Comparing the average yield value with a preset standard average value; wherein the standard average value is greater than the standard output value;
If the average production value is smaller than the standard average value, ranking the production capacity according to the value, and identifying the corresponding production equipment at the last N bits of the ranking of the production capacity as fault equipment; wherein N is a positive integer greater than 0.
In order to achieve the above object, the present application further provides a manufacturing process management system based on the industrial internet of things, which includes a user platform, a service platform, a management platform, a sensor network platform and an equipment object platform that are sequentially connected in communication, where the equipment object platform includes:
the rate value acquisition module is used for acquiring the qualification rate and the production rate of the target product produced in a preset period;
The comparison and acquisition module is used for comparing the qualification rate with a preset qualification threshold value, and acquiring a product image of a defective product in the target product if the qualification rate is smaller than the qualification threshold value;
the device type identification module is used for acquiring the device type associated with the defective product according to the product image; wherein, the equipment type comprises production equipment and quality inspection equipment;
The first detection module is used for carrying out fault detection on a plurality of production devices and/or carrying out fault detection on a plurality of quality inspection devices, and marking the production devices and/or quality inspection devices with unqualified fault detection as first fault devices;
and the second detection module is used for comparing the productivity with a preset productivity threshold, detecting the output of the plurality of production equipment if the productivity is smaller than the productivity threshold, and marking the production equipment with unqualified output detection as second fault equipment.
To achieve the above object, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.
To achieve the above object, the present application further provides a computer readable storage medium having a computer program stored thereon, and a processor executing the computer program to implement the above method.
The beneficial effects that the application can realize are as follows:
According to the application, through obtaining the qualification rate and the production rate of target products produced in a preset period, comparing the qualification rate with a preset qualification threshold value for qualification rate control, if the qualification rate is smaller than the qualification threshold value, corresponding equipment with the qualification rate reduced is required to be checked; for productivity, the application can detect the fault equipment on line by comparing productivity with the preset productivity threshold, and if the productivity is smaller than the productivity threshold, detecting the output of a plurality of production equipment, and marking the production equipment with unqualified output detection as the fault equipment.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of a computer device architecture of a hardware operating environment involved in an embodiment of the present application;
FIG. 2 is a flow chart of a manufacturing flow management method based on the industrial Internet of things in an embodiment of the application;
Fig. 3 is a schematic diagram of a framework of an internet of things system according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship between the components, the movement condition, etc. in a specific posture, if the specific posture is changed, the directional indicators are correspondingly changed.
In the present application, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and/or" as it appears throughout includes three parallel schemes, for example "A and/or B", including the A scheme, or the B scheme, or the scheme where A and B are satisfied simultaneously. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
Example 1
Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device of a hardware running environment according to an embodiment of the present invention, as shown in fig. 1, the computer device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., a wireless FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and an electronic program may be included in the memory 1005 as one type of storage medium.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present embodiment may be provided in the computer device, and the computer device calls the manufacturing flow management system based on the industrial internet of things stored in the memory 1005 through the processor 1001, and executes the manufacturing flow management method based on the industrial internet of things provided in the present embodiment.
Referring to fig. 2, based on the foregoing hardware environment, the present embodiment provides a manufacturing flow management method based on the industrial internet of things, including the following steps:
obtaining the qualification rate and the production rate of a target product produced in a preset period;
comparing the qualification rate with a preset qualification threshold value, and if the qualification rate is smaller than the qualification threshold value, acquiring a product image of a defective product in the target product;
acquiring the equipment type associated with the defective product according to the product image; wherein, the equipment type comprises production equipment and quality inspection equipment;
Performing fault detection on a plurality of production devices and/or performing fault detection on a plurality of quality inspection devices, and marking the production devices and/or quality inspection devices with unqualified fault detection as first fault devices;
And comparing the productivity with a preset productivity threshold, and if the productivity is smaller than the productivity threshold, performing yield detection on the plurality of production devices, and marking the production devices with unqualified yield detection as second fault devices.
In this embodiment, by acquiring the qualification rate and the productivity of the target product produced in the preset period, comparing the qualification rate with a preset qualification threshold value for qualification rate control, if the qualification rate is smaller than the qualification threshold value, the corresponding equipment with reduced qualification rate needs to be checked out, and by acquiring the product image of the defective product in the target product, according to the product image, the equipment type associated with the defective product can be confirmed, mainly including production equipment and quality inspection equipment, so that the inspection range can be shortened first, the inspection efficiency and the accuracy rate can be improved, then according to the confirmed equipment type, fault detection is performed on a plurality of production equipment and/or fault detection is performed on a plurality of quality inspection equipment, and then the production equipment and/or quality inspection equipment with unqualified fault detection is marked as fault equipment, so that subsequent fault maintenance is facilitated; for productivity, by comparing productivity with a preset productivity threshold, if productivity is smaller than the productivity threshold, a plurality of production apparatuses are subjected to yield inspection, and production apparatuses whose yield inspection is failed are marked as defective apparatuses, so that the present embodiment can perform online troubleshooting of defective apparatuses, thereby improving troubleshooting efficiency when problems occur in product quality or production efficiency.
It should be noted that the preset period is specifically set according to the production requirement, for example, one day, two days, or three days; the target products are products with a certain quantity which are produced in a preset period, and because of the large quantity of the products, standard quality inspection can be carried out on the products produced in a certain batch in a sampling inspection mode by adopting standard quality inspection equipment, so that the qualification rate and the production rate of the products in the batch are predicted; the qualification threshold and the yield threshold are also set according to the specific product technological condition and the production requirement; the product image can be obtained through a CCD industrial camera, and image processing software is built in the product image to analyze the defect condition of the product; according to the marked fault equipment, the equipment self-checking information including equipment numbers, fault reason information and the like can be further confirmed, so that the equipment can be rapidly checked out and overhauled.
As an alternative embodiment, acquiring a device type associated with a defective product from a product image includes:
Obtaining a defect degree value according to the product image; wherein the defect level value represents the ratio of the defective portion of the defective product to the standard product;
comparing the defect degree value with a preset first standard threshold value;
if the defect degree value is greater than or equal to the first standard threshold value, identifying the equipment type associated with the defective product as production equipment;
if the defect level value is less than the first standard threshold, identifying the type of equipment associated with the defective product as quality inspection equipment.
In this embodiment, when determining the type of equipment associated with a defective product according to a product image, the defect condition of the product may be quantitatively represented by obtaining a defect degree value of a corresponding defective product in the product image, and setting a first standard threshold, where the first standard threshold is set according to specific process conditions, and when the defect degree value is greater than or equal to the first standard threshold, it is indicated that the defect degree is higher, and is easily detected by quality inspection equipment, so that the high probability is that the production equipment fails, and therefore, the associated equipment is first identified as the production equipment, and if the defect degree value is less than the first standard threshold, it is indicated that the detected defect degree is lower, and is not easily detected by quality inspection equipment, and then the high probability is that the quality inspection equipment fails, and thus, the associated equipment is first identified as the quality inspection equipment, and thus, the corresponding associated equipment is identified according to the defect degree value calculated in the product image is realized, and thus, the fault equipment inspection accuracy is improved, and efficiency is improved.
It should be noted that, here, the product image is an image of a single product, and the spot inspection products have a certain number, the defect level values corresponding to the obtained multiple product images may be greater than or equal to the first standard threshold value and less than the first standard threshold value, which indicates that the production equipment and the quality inspection equipment may have faults at the same time, and at this time, it is required to detect whether the production equipment and the quality inspection equipment have faults at the same time.
As an alternative embodiment, obtaining a defect level value according to a product image includes:
Carrying out gray processing on the product image to obtain a gray image;
Noise reduction treatment is carried out on the gray level image;
threshold segmentation is carried out on the gray level image after noise reduction treatment so as to obtain a black-and-white image;
And comparing the black-and-white image with a preset database to obtain a defect degree value.
In this embodiment, the product image refers to a physical image of a product, and the gray level processing, the noise reduction processing and the threshold segmentation processing are sequentially performed on the product image, so that a black-and-white image with clear outline and clear black-and-white can be obtained, which is convenient for extracting and identifying the outline when the image is subsequently input into the database for image analysis, thereby improving the accuracy of calculating the defect degree value.
As an alternative embodiment, fault detection for a plurality of production devices includes:
detecting target products with preset quantity produced by each production device to obtain the quantity of defective products;
comparing the number of defective products with a preset second standard threshold;
And if the number of defective products is greater than or equal to the second standard threshold, identifying the corresponding production equipment as first faulty equipment.
In this embodiment, when the associated equipment is confirmed to be production equipment, centralized checking is further required for a plurality of production equipment to check whether a specific one or a plurality of production equipment is a fault equipment, here, a preset number of target products (the more the number is set according to the process condition, the higher the accuracy) are produced by each production equipment, standard quality checking equipment is adopted for detection during detection, so that the number of defective products in the same number of products produced by each production equipment can be obtained, the number of defective products is compared with a preset second standard threshold, and if the number of defective products is greater than the second standard threshold, the corresponding production equipment can be identified as the fault equipment, thereby realizing fault detection checking for the plurality of production equipment, and the checking accuracy and efficiency are high.
It should be noted that if the number of defective products is smaller than the second standard threshold (this is smaller), that is, the number of defective products corresponding to each production device is smaller than the second standard threshold, to indicate the failed quality inspection device, the step of performing fault detection on the plurality of quality inspection devices is entered.
As an alternative embodiment, fault detection for a plurality of quality inspection devices includes:
inputting the same number of sample products into each quality inspection device and standard device; wherein, a plurality of defective products are doped in the sample product, and the standard equipment is fault-free quality inspection equipment;
Obtaining the actual measurement defect rate of a sample product detected by each quality inspection device and the standard defect rate of a sample product detected by a standard device;
Comparing the measured defect rate with a standard defect rate;
And if the measured defect rate is greater than or equal to the standard defect rate, identifying the corresponding quality inspection equipment as first fault equipment.
In this embodiment, when it is confirmed that the associated device is a quality inspection device, a plurality of quality inspection devices are further required to be inspected in a centralized manner, a standard device is added, the same number of sample products (the number of defect products contained in the sample products should be the same) are input to each quality inspection device and the standard device, so that corresponding quality inspection data are obtained, that is, an actual measurement defect rate of each quality inspection device for detecting the sample product and a standard defect rate of the standard device for detecting the sample product are obtained, the actual measurement defect rate is compared with the standard defect rate, and the quality inspection device with the actual measurement defect rate being greater than the standard defect rate can be identified as a fault device, so that fault detection and inspection of the plurality of quality inspection devices are realized, and the inspection accuracy and efficiency are high.
If the measured defect rate is smaller than the standard defect rate (this is the case, namely, the measured defect rate corresponding to each quality inspection device is smaller than the standard defect rate, the production device with failure is described, and the step of failure detection is performed on a plurality of production devices.
As an alternative embodiment, the method for detecting the output of a plurality of production devices includes:
Obtaining the product throughput of each production device at a preset time;
comparing the product throughput with a preset standard throughput value;
If the product throughput is less than the standard throughput value, the corresponding production facility is identified as a second malfunctioning facility.
In this embodiment, when the yield is detected for the efficiency problem, the production equipment is mainly detected, and the product yield of each production equipment at a preset time (for example, 1 hour, 2 hours, 3 hours, etc.) is obtained, and compared with a preset standard yield value (i.e., a normal average yield value of the production equipment under the condition of no failure), if the product yield is smaller than the standard yield value, the corresponding production equipment can be identified as a failure equipment, so that the online yield detection of the production equipment is realized, and the data authenticity and accuracy are high.
As an alternative embodiment, if the product throughput corresponding to all production facilities is greater than or equal to the standard output value, the method further includes:
obtaining the average yield value of the production quantity data of multiple groups of products;
Comparing the average yield value with a preset standard average value; wherein the standard average value is greater than the standard output value;
If the average production value is smaller than the standard average value, ranking the production capacity according to the value, and identifying the corresponding production equipment at the last N bits of the ranking of the production capacity as fault equipment; wherein N is a positive integer greater than 0.
In this embodiment, when the product throughput corresponding to all production devices is greater than or equal to the standard throughput value, in order to further improve the troubleshooting accuracy of the faulty device, the average throughput value of the multiple sets of product throughput data may be calculated, and the average throughput value is compared with the preset standard average value, if the average throughput value is smaller than the standard average value, which indicates that the product throughput of multiple production devices is only slightly greater than the standard throughput value, and if a certain number of production devices have reached the fatigue cycle, the multiple production devices need to be overhauled in advance to avoid the subsequent collective fault, and in normal case, only a few product throughput should be slightly greater than the standard throughput value, and at this time, the corresponding production device of the last N bits (e.g., last 3 bits/4 bits/5 bits) of the product throughput is identified as the faulty device for fault detection.
Example 2
Referring to fig. 3, based on the same inventive concept as the foregoing embodiment, the present embodiment further provides a manufacturing process management system based on an industrial internet of things, including a user platform, a service platform, a management platform, a sensor network platform, and an equipment object platform that are sequentially connected in communication, where the equipment object platform includes:
the rate value acquisition module is used for acquiring the qualification rate and the production rate of the target product produced in a preset period;
The comparison and acquisition module is used for comparing the qualification rate with a preset qualification threshold value, and acquiring a product image of a defective product in the target product if the qualification rate is smaller than the qualification threshold value;
the device type identification module is used for acquiring the device type associated with the defective product according to the product image; wherein, the equipment type comprises production equipment and quality inspection equipment;
The first detection module is used for carrying out fault detection on a plurality of production devices and/or carrying out fault detection on a plurality of quality inspection devices, and marking the production devices and/or quality inspection devices with unqualified fault detection as first fault devices;
and the second detection module is used for comparing the productivity with a preset productivity threshold, detecting the output of the plurality of production equipment if the productivity is smaller than the productivity threshold, and marking the production equipment with unqualified output detection as second fault equipment.
It should be noted that, the manufacturing process management system based on the industrial internet of things in this embodiment may be connected to the internet of things system, so as to form a standard five-platform structure of the internet of things. The physical entity of the user platform comprises various user terminals, such as a mobile phone, a computer, a special terminal and the like, and the user terminal service is realized by combining the physical entity with user information system software.
The service platform is a functional platform for realizing service communication. In some embodiments, the service platform may include a service end such as an operation service and a security service.
The management platform is a functional platform for realizing operation management of the Internet of things system, and comprises a device operation state monitoring management module, a fault data monitoring management module, a device parameter management module, a device life cycle management module, a data center module and the like, wherein the data center module is used for carrying out interaction and processing of device data, and can manage and monitor various index data of the device through each functional module.
The sensing network platform is a functional platform for realizing sensing communication, and comprises a device management module and a data transmission management module, wherein the device management module comprises a network management module, an instruction management module and a device state management module, and the data transmission management module comprises a data protocol management module, a data analysis module, a data classification module, a data transmission monitoring module and a data transmission safety module.
The device object platform is a functional platform for realizing perception control. In some embodiments, the device object platform may include a plurality of devices, each device corresponding to a value acquisition module, a comparison and acquisition module, a device type identification module, a first detection module, and a second detection module, respectively.
In some embodiments, the device object platform not only includes the above modules, but also includes an MCU control module. Therefore, through the synergistic effect of the functional modules, the interactive Internet of things five-platform structure of the Internet of things is realized, and a frame foundation is provided for a manufacturing flow management system based on the industrial Internet of things.
Example 3
Based on the same inventive concept as the previous embodiments, this embodiment provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the above method.
Example 4
Based on the same inventive concept as the previous embodiments, this embodiment provides a computer readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (9)
1. The manufacturing flow management method based on the industrial Internet of things is characterized by comprising the following steps of:
obtaining the qualification rate and the production rate of a target product produced in a preset period;
Comparing the qualification rate with a preset qualification threshold value, and if the qualification rate is smaller than the qualification threshold value, acquiring a product image of a defective product in the target product;
acquiring a device type associated with the defective product according to the product image; wherein the equipment types comprise production equipment and quality inspection equipment; comprising the following steps: obtaining a defect degree value according to the product image; wherein the defect level value represents a ratio of a defective portion of the defective product to a standard product; comparing the defect degree value with a preset first standard threshold value; if the defect level value is greater than or equal to the first standard threshold, identifying the equipment type associated with the defective product as the production equipment; if the defect level value is smaller than the first standard threshold value, identifying the equipment type associated with the defective product as the quality inspection equipment;
performing fault detection on a plurality of production devices and/or performing fault detection on a plurality of quality inspection devices, and marking the production devices and/or the quality inspection devices with unqualified fault detection as first fault devices;
comparing the productivity with a preset productivity threshold, and if the productivity is smaller than the productivity threshold, performing yield detection on a plurality of production equipment, and marking the production equipment with unqualified yield detection as second fault equipment.
2. The method for managing a manufacturing process based on the industrial internet of things according to claim 1, wherein the obtaining a defect level value according to the product image comprises:
Carrying out gray scale processing on the product image to obtain a gray scale image;
Noise reduction processing is carried out on the gray level image;
Threshold segmentation is carried out on the gray level image after noise reduction treatment so as to obtain a black-and-white image;
and comparing the black-and-white image with a preset database to obtain a defect degree value.
3. The method for managing a manufacturing process based on the industrial internet of things according to claim 1, wherein the performing fault detection on the plurality of production devices comprises:
Detecting target products with preset quantity produced by each production device to obtain the quantity of defective products;
Comparing the number of defective products with a preset second standard threshold;
and if the number of the defective products is greater than or equal to the second standard threshold, identifying the corresponding production equipment as first faulty equipment.
4. The method for managing a manufacturing process based on the industrial internet of things according to claim 1, wherein the performing fault detection on the plurality of quality inspection devices comprises:
Inputting the same number of sample products into each quality inspection device and standard device; wherein the sample product is doped with a plurality of defect products, and the standard equipment is fault-free quality inspection equipment;
obtaining the actual measurement defect rate of each quality inspection device for detecting the sample product and the standard defect rate of the standard device for detecting the sample product;
comparing the measured defect rate with the standard defect rate;
and if the measured defect rate is greater than or equal to the standard defect rate, identifying the corresponding quality inspection equipment as first fault equipment.
5. The method for managing a manufacturing process based on the industrial internet of things according to claim 1, wherein the detecting the output of the plurality of production devices comprises:
Obtaining the product throughput of each production device at preset time;
Comparing the product throughput with a preset standard throughput value;
and if the product throughput is smaller than the standard production value, identifying the corresponding production equipment as second fault equipment.
6. The method of claim 5, further comprising, if the product throughput corresponding to all the production devices is greater than or equal to the standard throughput value:
Obtaining the average yield value of a plurality of groups of the product yield data;
comparing the average yield value with a preset standard average value; wherein the standard average value is greater than the standard output value;
if the average production capacity value is smaller than the standard average value, ranking the production capacity according to the numerical value, and identifying the corresponding production equipment of the last N bits of the production capacity ranking as fault equipment; wherein N is a positive integer greater than 0.
7. The manufacturing flow management system based on the industrial Internet of things is characterized by comprising a user platform, a service platform, a management platform, a sensor network platform and a device object platform which are sequentially in communication connection, wherein the device object platform comprises:
the rate value acquisition module is used for acquiring the qualification rate and the production rate of the target product produced in a preset period;
The comparison and acquisition module is used for comparing the qualification rate with a preset qualification threshold value, and acquiring a product image of a defective product in the target product if the qualification rate is smaller than the qualification threshold value;
The equipment type identification module is used for acquiring the equipment type associated with the defective product according to the product image; wherein the equipment types comprise production equipment and quality inspection equipment; comprising the following steps: obtaining a defect degree value according to the product image; wherein the defect level value represents a ratio of a defective portion of the defective product to a standard product; comparing the defect degree value with a preset first standard threshold value; if the defect level value is greater than or equal to the first standard threshold, identifying the equipment type associated with the defective product as the production equipment; if the defect level value is smaller than the first standard threshold value, identifying the equipment type associated with the defective product as the quality inspection equipment;
The first detection module is used for carrying out fault detection on a plurality of production devices and/or carrying out fault detection on a plurality of quality inspection devices, and marking the production devices and/or the quality inspection devices which are unqualified in fault detection as first fault devices;
And the second detection module is used for comparing the productivity with a preset productivity threshold, detecting the output of a plurality of production equipment if the productivity is smaller than the productivity threshold, and marking the production equipment with unqualified output detection as second fault equipment.
8. A computer device, characterized in that it comprises a memory in which a computer program is stored and a processor which executes the computer program, implementing the method according to any of claims 1-6.
9. A computer readable storage medium, having stored thereon a computer program, the computer program being executable by a processor to implement the method of any of claims 1-6.
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