CN117596160B - Method and system for manufacturing industry data link communication - Google Patents
Method and system for manufacturing industry data link communication Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 180
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- H—ELECTRICITY
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Abstract
The invention discloses a method and a system for melting a data link in manufacturing industry, which belong to the field of electric digital data processing.
Description
Technical Field
The invention belongs to the technical field of electric digital data processing, and particularly relates to a method and a system for melting a data link in a manufacturing industry.
Background
At present: the manufacturing enterprises can realize the interconnection and intercommunication of equipment, systems and data of different manufacturing links by means of cloud platforms and the Internet of things technology. Through the cloud platform and the Internet of things technology, a manufacturing enterprise can realize remote monitoring, remote control and fault diagnosis of equipment, production efficiency and product quality are improved, and data link communication of the manufacturing industry is promoted;
For example, a method, a device, equipment and a medium for operation and maintenance of a DTS data link are disclosed in a patent with application publication number CN115495529a, which relate to the field of information technology. The method comprises the following steps: acquiring a target operation request aiming at a data link of a target end and a source end in DTS data, and acquiring a target yaml configuration file corresponding to the target end and the source end; reading the target yaml configuration file and obtaining target data information in the target yaml configuration file, and then selecting a preset target application program interface corresponding to the target operation according to the target operation corresponding to the target operation request; and calling the preset target application program interface to perform target operation based on the target data information so as to complete the target operation request. By the scheme, batch operation on the multi-synchronous link can be performed when the operation and the maintenance of the DTS data link are performed, so that the operation and the maintenance efficiency of the DTS data link are improved;
Meanwhile, for example, in the patent with the application publication number of CN115269870A, a method for realizing the fault classification early warning of the data link in the data is provided, firstly, the fault classification in the data link is taken as a target, a fault classification model is obtained through Kmeans-SVM model training, then a knowledge graph of a fault type is built through a Markov model and other methods, a basis of fault classification is obtained based on the knowledge graph of the fault field and the Kmeans-SVM method, and then the association relation among faults is analyzed through the similarity of fault reasons, so that the possible association faults in the data link are predicted. Through the mutual cooperation of the machine learning fault classification module and the knowledge graph fault early warning module, intelligent analysis of the data link is realized, a series of problems existing in an expert system and machine learning are solved, the maintenance efficiency of the data link is effectively improved, and the fault classification accuracy is greatly improved.
The problems proposed in the background art exist in the above patents: in the prior art, in the process of melting a data link, the abnormality of a product caused by equipment failure cannot be quickly found, and the abnormality of the product can be found only after the product is detected, so that a large number of subsequent product abnormalities are easily caused, the time for remedying the subsequent equipment is delayed, the production quality of the product is reduced, and the problems in the prior art are solved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a system for melting a data link in a manufacturing industry, the invention acquires the transmission data of equipment in a manufacturing scene, simultaneously acquires the transmission data of historical equipment in the manufacturing scene and the product abnormal data of the historical manufacturing equipment, acquires the transmission data of the historical equipment in the manufacturing scene and the product abnormal data of the historical manufacturing equipment, imports the product abnormal identification model in a product abnormal identification model construction strategy, extracts the calculation of the equipment association degree in the equipment association degree calculation strategy of the transmission data of the historical equipment in the manufacturing scene, acquires the transmission data of the equipment in the manufacturing scene, imports the product abnormal data in the product abnormal identification model, takes the equipment corresponding to the maximum equipment association degree of the manufacturing equipment as compensation equipment, imports the transmission data adjustment value required by the calculation of the compensation equipment into the product abnormal identification model, adjusts the corresponding parameters according to the required transmission data adjustment value, improves the production quality of products, rapidly discovers the product abnormal caused by equipment faults, can discover the product abnormal without detecting the product, simultaneously rapidly controls the subsequent equipment abnormal in a large number, greatly improves the subsequent production quality, and greatly improves the subsequent production quality.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A method for manufacturing an industry data link fuse comprising the specific steps of:
S1, acquiring transmission data of equipment in a manufacturing scene, and simultaneously acquiring transmission data of historical equipment in the manufacturing scene and abnormal data of products of the historical manufacturing equipment;
s2, acquiring historical equipment transmission data in a manufacturing scene and product anomaly data of historical manufacturing equipment, importing the data into a product anomaly identification model construction strategy, and constructing a product anomaly identification model;
S3, extracting historical equipment transmission data in a manufacturing scene, and importing the historical equipment transmission data into an equipment contact degree calculation strategy to calculate the equipment contact degree;
S4, acquiring transmission data of equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model, calculating product anomaly data, taking equipment corresponding to the maximum equipment contact degree of the manufacturing equipment as compensation equipment, importing the transmission data of the compensation equipment into the product anomaly identification model, and calculating a required transmission data adjustment value;
S5, the compensation equipment adjusts corresponding parameters according to the required transmission data adjustment value.
Specifically, the step S1 includes the following specific steps:
S11, collecting transmission data of equipment in a manufacturing scene through an equipment data collecting module, and transmitting the transmission data to a storage module of a manufacturing workshop management system in real time, wherein the storage module stores historical equipment transmission data in the manufacturing scene and product abnormal data of historical manufacturing equipment, and the product abnormal data comprises abnormal data of each product which is not in a detection safety range after the product is manufactured through the equipment;
s12, storing and transmitting the acquired transmission data of the historical equipment in the manufacturing scene and the product abnormal data of the historical manufacturing equipment corresponding to the transmission data in a two-dimensional vector mode.
Specifically, the specific content of the product anomaly identification model construction strategy in S2 is as follows:
constructing a product anomaly identification model by using historical equipment transmission data and product anomaly data of historical manufacturing equipment in a manufacturing scene, and dividing the data into a weight training set of 70% and a weight test set of 30%; performing weight training on an initial equation of a product anomaly identification model, constructing a product anomaly identification model which is input as historical equipment transmission data in a manufacturing scene and output as product anomaly data of historical manufacturing equipment, inputting a weight training set of 70% into the product anomaly identification model for training to obtain the initial product anomaly identification model, testing the initial product anomaly identification model by using a weight testing set of 30%, and outputting an optimal initial product anomaly identification model meeting the accuracy of an anomaly value of a produced product as the product anomaly identification model, wherein the initial equation formula of the product anomaly identification model is as follows: wherein/> For the z-th item exception data of p-products,/>For the z-th abnormal data standard value of p products, n is the number of manufacturing equipment, m is the number of parameters in the transmission data of the i-th manufacturing equipment,/>For the ith manufacturing facility duty cycle,/>The weight of the j-th parameter in the transmission data in the ith manufacturing equipment is/areFor the value of the j-th parameter in the transmission data in the i-th manufacturing equipment,/>For the standard value of the j-th parameter in the transmission data in the i-th manufacturing equipment,/>。
Specifically, the specific content of the device contact degree calculation policy in S3 is:
Extracting working end data in historical equipment transmission data in a manufacturing scene, and importing the working end data in the historical equipment transmission data into an equipment contact degree calculation formula to calculate the equipment contact degree of two equipment, wherein the calculation formula of the equipment contact degree is as follows: wherein S is the number of working end data in the device transmission data,/> Duty ratio coefficient of s-th data of working end in data transmission for equipment,/>Transmitting the s-th data of the working end in the data for one of the devices,/>Transmitting the s-th data of the working end in the data for another device;
Here, it is to be noted that, here According to different flexible settings of scenes, acquiring the device contact degree of two corresponding devices in 500 groups of scenes, then employing an expert to sequence the devices with similar functions in the scenes, substituting the device contact degree into fitting software, and outputting the optimal/>, which accords with the accuracy of the device contact degreeValues.
Specifically, the step S4 includes the following specific steps:
S41, acquiring transmission data of manufacturing equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model to calculate product anomaly data, obtaining the product anomaly data through calculation, comparing the obtained product anomaly data with a set product anomaly threshold value, performing S42 if the product anomaly value is greater than or equal to the set product anomaly threshold value, and displaying equipment normally if the product anomaly value is less than the product anomaly threshold value;
S42, taking the equipment corresponding to the equipment contact degree with the maximum manufacturing equipment as compensation equipment, subtracting the product abnormal data from the set product normal data to obtain a product abnormal difference sequence, and importing each weight value and the product abnormal difference sequence of the compensation equipment into a product abnormal recognition model to derive a transmission data adjustment value required by the compensation equipment.
A system for manufacturing industry data link financing, which is implemented based on the above method for manufacturing industry data link financing, specifically comprising: the system comprises a data acquisition module, a product anomaly identification model construction module, a device contact degree calculation module, a transmission data adjustment value calculation module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring transmission data of devices in a manufacturing scene and simultaneously acquiring transmission data of historical devices in the manufacturing scene and product anomaly data of the historical manufacturing devices, and the product anomaly identification model construction module is used for acquiring the transmission data of the historical devices in the manufacturing scene and the product anomaly data of the historical manufacturing devices to be imported into a product anomaly identification model construction strategy to construct a product anomaly identification model.
Specifically, the device contact degree calculation module is used for extracting transmission data of historical devices in a manufacturing scene and importing the transmission data into a device contact degree calculation strategy to calculate the device contact degree, the transmission data adjustment value calculation module is used for obtaining transmission data of the devices in the manufacturing scene and importing the transmission data into a product anomaly identification model to calculate product anomaly data, the device corresponding to the maximum device contact degree of the manufacturing device is taken as compensation device, the transmission data of the compensation device is imported into the product anomaly identification model to calculate a required transmission data adjustment value, and the parameter adjustment module is used for adjusting corresponding parameters according to the required transmission data adjustment value of the compensation device.
Specifically, the control module is used for controlling the operation of the data acquisition module, the product abnormality identification model construction module, the equipment contact degree calculation module, the transmission data adjustment value calculation module and the parameter adjustment module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor performs a method for manufacturing industry data link finalisation as described above by invoking a computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a method for manufacturing industry data link finalisation as described above.
Compared with the prior art, the invention has the beneficial effects that:
The method comprises the steps of acquiring transmission data of equipment in a manufacturing scene, acquiring transmission data of historical equipment in the manufacturing scene and abnormal data of products of the historical manufacturing equipment, acquiring the transmission data of the historical equipment in the manufacturing scene and the abnormal data of the products of the historical manufacturing equipment, importing the transmission data of the historical equipment into a product abnormal recognition model construction strategy to construct a product abnormal recognition model, extracting the transmission data of the historical equipment in the manufacturing scene, importing the transmission data of the historical equipment into a device contact degree calculation strategy to calculate the device contact degree, acquiring the transmission data of the equipment in the manufacturing scene, importing the transmission data of the equipment corresponding to the maximum device contact degree of the manufacturing equipment into a compensation equipment, importing the transmission data regulation value required by the compensation equipment into the product abnormal recognition model, regulating the corresponding parameters according to the required transmission data regulation value by the compensation equipment, and improving the production quality of products.
Drawings
FIG. 1 is a flow chart of a method for manufacturing industry data link finalisation in accordance with the present invention;
FIG. 2 is a schematic diagram showing a specific flow of step S1 of a method for manufacturing industry data link assembly according to the present invention;
FIG. 3 is a schematic diagram showing a specific flow of step S4 of a method for manufacturing industry data link assembly according to the present invention;
FIG. 4 is a schematic diagram of a system architecture for manufacturing industry data link assembly in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: a method for manufacturing an industry data link fuse comprising the specific steps of:
S1, acquiring transmission data of equipment in a manufacturing scene, and simultaneously acquiring transmission data of historical equipment in the manufacturing scene and abnormal data of products of the historical manufacturing equipment;
s2, acquiring historical equipment transmission data in a manufacturing scene and product anomaly data of historical manufacturing equipment, importing the data into a product anomaly identification model construction strategy, and constructing a product anomaly identification model;
S3, extracting historical equipment transmission data in a manufacturing scene, and importing the historical equipment transmission data into an equipment contact degree calculation strategy to calculate the equipment contact degree;
S4, acquiring transmission data of equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model, calculating product anomaly data, taking equipment corresponding to the maximum equipment contact degree of the manufacturing equipment as compensation equipment, importing the transmission data of the compensation equipment into the product anomaly identification model, and calculating a required transmission data adjustment value;
S5, the compensation equipment adjusts corresponding parameters according to the required transmission data adjustment value.
In this embodiment, S1 includes the following specific steps:
S11, collecting transmission data of equipment in a manufacturing scene through an equipment data collecting module, and transmitting the transmission data to a storage module of a manufacturing workshop management system in real time, wherein the storage module stores historical equipment transmission data in the manufacturing scene and product abnormal data of historical manufacturing equipment, and the product abnormal data comprises abnormal data of each product which is not in a detection safety range after the product is manufactured through the equipment;
The following is an exemplary C language code, configured to collect, by using an equipment data collecting module, transmission data of equipment in a manufacturing scene, and transmit the transmission data to a storage module of a manufacturing shop management system in real time, where the storage module stores historical equipment transmission data in the manufacturing scene and product anomaly data of the historical manufacturing equipment;
#include<stdio.h>
#include<stdlib.h>
Data transmission structure of/(and/or equipment)
typedef struct DeviceData {
Device ID
int deviceId;
Data transmission
float data;
} DeviceData;
Data storage module for/(and/or history transmission
typedef struct StorageModule {
Data array for transmission of data/storage
DeviceData *dataList;
Number of data currently stored
int dataSize;
} StorageModule;
Data structure for abnormality of/(and/or production of products
typedef struct ProductExceptionData {
Product ID
int productId;
Data of// anomalies
float exceptionData;
} ProductExceptionData;
Abnormal data storage module for/(and history product
typedef struct ExceptionStorageModule {
Data array of exception data for/(and/or storage)
ProductExceptionData *exceptionList;
Number of abnormal data currently stored
int exceptionSize;
} ExceptionStorageModule;
Memory module for/(and/or initialization
void initStorageModule(StorageModule *storageModule) {
storageModule->dataList = NULL;
storageModule->dataSize = 0;
}
Data transmission from the device to the storage module
void addDeviceData(StorageModule *storageModule, int deviceId, float data) {
Memory space/allocation
storageModule->dataList = (DeviceData*)realloc(storageModule->dataList, (storageModule->dataSize + 1) * sizeof(DeviceData));
New transmission data addition
DeviceData newData;
newData.deviceId = deviceId;
newData.data = data;
storageModule->dataList[storageModule->dataSize] = newData;
Number of data/one
storageModule->dataSize++;
}
Data storage module for initialization exception
void initExceptionStorageModule(ExceptionStorageModule *exceptionModule) {
exceptionModule->exceptionList = NULL;
exceptionModule->exceptionSize = 0;
}
Product anomaly data is/is added to anomaly data storage module
void addProductExceptionData(ExceptionStorageModule *exceptionModule, int productId, float exceptionData) {
Memory space/allocation
exceptionModule->exceptionList = (ProductExceptionData*)realloc(exceptionModule->exceptionList, (exceptionModule->exceptionSize + 1) * sizeof(ProductExceptionData));
New exception data is/are added
ProductExceptionData newExceptionData;
newExceptionData.productId = productId;
newExceptionData.exceptionData = exceptionData;exceptionModule->exceptionList[exceptionModule->exceptionSize] = newExceptionData;
Number of data per anomaly plus one
exceptionModule->exceptionSize++;
}
int main() {
StorageModule storageModule;
initStorageModule(&storageModule);
ExceptionStorageModule exceptionModule;
initExceptionStorageModule(&exceptionModule);
Data of the equipment is acquired by means of/(and simulation) and added to the storage module
int deviceId1 = 1;
float data1 = 10.5;
addDeviceData(&storageModule, deviceId1, data1);
int deviceId2 = 2;
float data2 = 15.8;
addDeviceData(&storageModule, deviceId2, data2);
Adding product anomaly data to anomaly data storage module by using/(and/or simulation)
int productId1 = 1001;
float exceptionData1 = 30.2;
addProductExceptionData(&exceptionModule, productId1, exceptionData1);
int productId2 = 1002;
float exceptionData2 = 40.5;
addProductExceptionData(&exceptionModule, productId2, exceptionData2);
Device transfer data in a// print storage module
Printf ("device transmitting data: \n");
for (int i = 0; i<storageModule.dataSize; i++) {
printf ("device ID:% d transfer:%. 1f)
", storageModule.dataList[i].deviceId, storageModule.dataList[i].data);
}
Product anomaly data in a/(print storage module)
Printf ("\n product anomaly data: \n");
for (int i = 0; i<exceptionModule.exceptionSize; i++) {
printf ("product ID:% d anomaly:%. 1f)
", exceptionModule.exceptionList[i].productId, exceptionModule.exceptionList[i].exceptionData);
}
Memory space/release
free(storageModule.dataList);
free(exceptionModule.exceptionList);
return 0;
}
This is just a simple example code, in which dynamic memory allocation is used to store data and to add and access data through functions. In practical application, according to specific practical situations, optimization and improvement may need to be performed according to data quantity and storage requirements;
s12, storing and transmitting the acquired transmission data of the historical equipment in the manufacturing scene and the product abnormal data of the historical manufacturing equipment corresponding to the transmission data in a two-dimensional vector mode.
In this embodiment, the specific content of the product anomaly identification model construction strategy in S2 is as follows:
Constructing a product anomaly identification model by using historical equipment transmission data and product anomaly data of historical manufacturing equipment in a manufacturing scene, and dividing the data into a weight training set of 70% and a weight test set of 30%; performing weight training on an initial equation of a product anomaly identification model, constructing a product anomaly identification model which is input as historical equipment transmission data in a manufacturing scene and output as product anomaly data of historical manufacturing equipment, inputting a weight training set of 70% into the product anomaly identification model for training to obtain the initial product anomaly identification model, testing the initial product anomaly identification model by using a weight testing set of 30%, and outputting an optimal initial product anomaly identification model meeting the accuracy of an anomaly value of a produced product as the product anomaly identification model, wherein the formula of the initial equation of the product anomaly identification model is as follows: Wherein For the z-th item exception data of p-products,/>For the z-th abnormal data standard value of p products, n is the number of manufacturing equipment, m is the number of parameters in the transmission data of the i-th manufacturing equipment,/>For the ith manufacturing facility duty cycle,/>The weight of the j-th parameter in the transmission data in the ith manufacturing equipment is/areFor the parameter value of the j-th parameter in the transmission data in the i-th manufacturing apparatus,For the standard value of the j-th parameter in the transmission data in the i-th manufacturing equipment,/>。
In this embodiment, the specific content of the device contact degree calculation policy in S3 is:
Extracting working end data in historical equipment transmission data in a manufacturing scene, and importing the working end data in the historical equipment transmission data into an equipment contact degree calculation formula to calculate the equipment contact degree of two equipment, wherein the calculation formula of the equipment contact degree is as follows: wherein S is the number of working end data in the device transmission data,/> Duty ratio coefficient of s-th data of working end in data transmission for equipment,/>Transmitting the s-th data of the working end in the data for one of the devices,/>Transmitting the s-th data of the working end in the data for another device;
Here, it is to be noted that, here According to different flexible settings of scenes, acquiring the device contact degree of two corresponding devices in 500 groups of scenes, then employing an expert to sequence the devices with similar functions in the scenes, substituting the device contact degree into fitting software, and outputting the optimal/>, which accords with the accuracy of the device contact degreeValues.
In this embodiment, S4 includes the following specific steps:
S41, acquiring transmission data of manufacturing equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model to calculate product anomaly data, obtaining the product anomaly data through calculation, comparing the obtained product anomaly data with a set product anomaly threshold value, performing S42 if the product anomaly value is greater than or equal to the set product anomaly threshold value, and displaying equipment normally if the product anomaly value is less than the product anomaly threshold value;
S42, taking the equipment corresponding to the equipment contact degree with the maximum manufacturing equipment as compensation equipment, subtracting the product abnormal data from the set product normal data to obtain a product abnormal difference sequence, and importing each weight value and the product abnormal difference sequence of the compensation equipment into a product abnormal recognition model to derive a transmission data adjustment value required by the compensation equipment.
The implementation of the embodiment can be realized: the method comprises the steps of acquiring transmission data of equipment in a manufacturing scene, acquiring transmission data of historical equipment in the manufacturing scene and abnormal data of products of the historical manufacturing equipment, acquiring the transmission data of the historical equipment in the manufacturing scene and the abnormal data of the products of the historical manufacturing equipment, importing the transmission data of the historical equipment into a product abnormal recognition model construction strategy to construct a product abnormal recognition model, extracting the transmission data of the historical equipment in the manufacturing scene, importing the transmission data of the historical equipment into a device contact degree calculation strategy to calculate the device contact degree, acquiring the transmission data of the equipment in the manufacturing scene, importing the transmission data of the equipment corresponding to the maximum device contact degree of the manufacturing equipment into a compensation equipment, importing the transmission data regulation value required by the compensation equipment into the product abnormal recognition model, regulating the corresponding parameters according to the required transmission data regulation value by the compensation equipment, and improving the production quality of products.
Example 2
As shown in fig. 4, a system for manufacturing industry data link fusion is implemented based on the above method for manufacturing industry data link fusion, and specifically includes: the system comprises a data acquisition module, a product anomaly identification model construction module, a device contact degree calculation module, a transmission data adjustment value calculation module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring transmission data of devices in a manufacturing scene and simultaneously acquiring transmission data of historical devices in the manufacturing scene and product anomaly data of the historical manufacturing devices; the device contact degree calculation module is used for extracting historical device transmission data in the manufacturing scene, importing the transmission data into the device contact degree calculation strategy to calculate the device contact degree, the transmission data adjustment value calculation module is used for acquiring transmission data of the device in the manufacturing scene, importing the transmission data into the product anomaly identification model to calculate the product anomaly data, taking the device corresponding to the maximum device contact degree of the manufacturing device as compensation device, importing the transmission data of the compensation device into the product anomaly identification model to calculate a required transmission data adjustment value, and the parameter adjustment module is used for adjusting the compensation device to corresponding parameters according to the required transmission data adjustment value; the control module is used for controlling the operation of the data acquisition module, the product abnormality identification model construction module, the equipment contact degree calculation module, the transmission data adjustment value calculation module and the parameter adjustment module;
according to the method and the device for detecting the product abnormality, the transmission data of the equipment in the manufacturing scene is obtained, meanwhile, the transmission data of the historical equipment in the manufacturing scene and the product abnormality data of the historical manufacturing equipment are obtained and imported into a product abnormality recognition model construction strategy to construct a product abnormality recognition model, the transmission data of the historical equipment in the manufacturing scene is imported into an equipment association degree calculation strategy to calculate the equipment association degree, the transmission data of the equipment in the manufacturing scene is imported into the product abnormality recognition model to calculate the product abnormality data, the equipment corresponding to the maximum equipment association degree of the manufacturing equipment is taken as compensation equipment, the transmission data adjustment value required by the compensation equipment is used for calculating the transmission data adjustment value required by the compensation equipment, and the production quality of products is improved.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor performs a method for manufacturing industry data link finals as described above by invoking a computer program stored in the memory.
The electronic device may vary widely in configuration or performance and can include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement a method for manufacturing industry data link finals provided by the above-described method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
The computer program, when executed on a computer device, causes the computer device to perform a method for manufacturing industry data link-through as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. A method for manufacturing an industry data link finalisation, comprising the specific steps of:
S1, acquiring transmission data of equipment in a manufacturing scene, and simultaneously acquiring transmission data of historical equipment in the manufacturing scene and abnormal data of products of the historical manufacturing equipment;
s2, acquiring historical equipment transmission data in a manufacturing scene and product anomaly data of historical manufacturing equipment, importing the data into a product anomaly identification model construction strategy, and constructing a product anomaly identification model;
S3, extracting historical equipment transmission data in a manufacturing scene, and importing the historical equipment transmission data into an equipment contact degree calculation strategy to calculate the equipment contact degree;
S4, acquiring transmission data of equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model, calculating product anomaly data, taking equipment corresponding to the maximum equipment contact degree of the manufacturing equipment as compensation equipment, importing the transmission data of the compensation equipment into the product anomaly identification model, and calculating a required transmission data adjustment value;
S5, adjusting corresponding parameters by the compensation equipment according to the required transmission data adjustment value; the S1 comprises the following specific steps:
S11, collecting transmission data of equipment in a manufacturing scene through an equipment data collecting module, and transmitting the transmission data to a storage module of a manufacturing workshop management system in real time, wherein the storage module stores historical equipment transmission data in the manufacturing scene and product abnormal data of historical manufacturing equipment, and the product abnormal data comprises abnormal data of each product which is not in a detection safety range after the product is manufactured through the equipment;
s12, storing and transmitting the acquired transmission data of the historical equipment in the manufacturing scene and the product abnormal data of the historical manufacturing equipment corresponding to the transmission data in a two-dimensional vector mode; the specific content of the product anomaly identification model construction strategy in the S2 is as follows:
constructing a product anomaly identification model by using historical equipment transmission data and product anomaly data of historical manufacturing equipment in a manufacturing scene, and dividing the data into a weight training set of 70% and a weight test set of 30%; performing weight training on an initial equation of a product anomaly identification model, constructing a product anomaly identification model which is input as historical equipment transmission data in a manufacturing scene and output as product anomaly data of historical manufacturing equipment, inputting a weight training set of 70% into the product anomaly identification model for training to obtain the initial product anomaly identification model, testing the initial product anomaly identification model by using a weight testing set of 30%, and outputting an optimal initial product anomaly identification model meeting the accuracy of an anomaly value of a produced product as the product anomaly identification model, wherein the initial equation formula of the product anomaly identification model is as follows: wherein/> For the z-th item exception data of p-products,/>For the z-th abnormal data standard value of p products, n is the number of manufacturing equipment, m is the number of parameters in the transmission data of the i-th manufacturing equipment,/>For the ith manufacturing facility duty cycle,/>The weight of the j-th parameter in the transmission data in the ith manufacturing equipment is/areFor the value of the j-th parameter in the transmission data in the i-th manufacturing equipment,/>For the standard value of the j-th parameter in the transmission data in the i-th manufacturing equipment,/>; The specific content of the device contact degree calculation strategy in the S3 is as follows:
Extracting working end data in historical equipment transmission data in a manufacturing scene, and importing the working end data in the historical equipment transmission data into an equipment contact degree calculation formula to calculate the equipment contact degree of two equipment, wherein the calculation formula of the equipment contact degree is as follows: wherein S is the number of working end data in the device transmission data,/> Duty ratio coefficient of s-th data of working end in data transmission for equipment,/>The s-th data of the working end in the data is transmitted to one of the devices,Transmitting the s-th data of the working end in the data for another device; the step S4 comprises the following specific steps:
S41, acquiring transmission data of manufacturing equipment in a manufacturing scene, importing the transmission data into a product anomaly identification model to calculate product anomaly data, obtaining the product anomaly data through calculation, comparing the obtained product anomaly data with a set product anomaly threshold value, performing S42 if the product anomaly value is greater than or equal to the set product anomaly threshold value, and displaying equipment normally if the product anomaly value is less than the product anomaly threshold value;
S42, taking the equipment corresponding to the equipment contact degree with the maximum manufacturing equipment as compensation equipment, subtracting the product abnormal data from the set product normal data to obtain a product abnormal difference sequence, and importing each weight value and the product abnormal difference sequence of the compensation equipment into a product abnormal recognition model to derive a transmission data adjustment value required by the compensation equipment.
2. A system for manufacturing industry data link finalisation based on a method for manufacturing industry data link finalisation as claimed in claim 1, characterized in that it comprises in particular: the system comprises a data acquisition module, a product anomaly identification model construction module, a device contact degree calculation module, a transmission data adjustment value calculation module, a parameter adjustment module and a control module, wherein the data acquisition module is used for acquiring transmission data of devices in a manufacturing scene and simultaneously acquiring transmission data of historical devices in the manufacturing scene and product anomaly data of the historical manufacturing devices, and the product anomaly identification model construction module is used for acquiring the transmission data of the historical devices in the manufacturing scene and the product anomaly data of the historical manufacturing devices to be imported into a product anomaly identification model construction strategy to construct a product anomaly identification model.
3. The system for manufacturing industry data link financing according to claim 2, wherein the device association degree calculation module is configured to extract historical device transmission data in a manufacturing scene, import the transmission data into a device association degree calculation policy, calculate the device association degree, the transmission data adjustment value calculation module is configured to obtain transmission data of devices in the manufacturing scene, import the transmission data into a product anomaly identification model, calculate the product anomaly data, take a device corresponding to the device association degree with the largest manufacturing device as a compensation device, import the transmission data of the compensation device into the product anomaly identification model, calculate a required transmission data adjustment value, and the parameter adjustment module is configured to adjust the compensation device according to the required transmission data adjustment value.
4. A system for manufacturing industry data link finalisation as claimed in claim 3, wherein the control module is arranged to control operation of the data acquisition module, the product anomaly identification model construction module, the equipment contact calculation module, the transmission data adjustment value calculation module, the parameter adjustment module.
5. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a method for manufacturing industry data link finalisation as claimed in claim 1 by invoking a computer program stored in the memory.
6. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a method for manufacturing industry data link finalisation as claimed in claim 1.
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