CN111914208B - Detection system and method based on relative quality index early warning - Google Patents

Detection system and method based on relative quality index early warning Download PDF

Info

Publication number
CN111914208B
CN111914208B CN202010645951.XA CN202010645951A CN111914208B CN 111914208 B CN111914208 B CN 111914208B CN 202010645951 A CN202010645951 A CN 202010645951A CN 111914208 B CN111914208 B CN 111914208B
Authority
CN
China
Prior art keywords
quality index
product
early warning
module
relative quality
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010645951.XA
Other languages
Chinese (zh)
Other versions
CN111914208A (en
Inventor
黄种明
许志龙
李煌
郭更生
钟建华
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xiamen Leelen Technology Co Ltd
Original Assignee
Jimei University
Xiamen Leelen Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jimei University, Xiamen Leelen Technology Co Ltd filed Critical Jimei University
Priority to CN202010645951.XA priority Critical patent/CN111914208B/en
Publication of CN111914208A publication Critical patent/CN111914208A/en
Application granted granted Critical
Publication of CN111914208B publication Critical patent/CN111914208B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/11Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/15Correlation function computation including computation of convolution operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0639Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395Quality analysis or management
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Human Resources & Organizations (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Operations Research (AREA)
  • Strategic Management (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Marketing (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Computing Systems (AREA)
  • General Factory Administration (AREA)

Abstract

The invention discloses a detection system and a method thereof based on relative quality index early warning, which comprises a detection module, a data acquisition module, a quality analysis module, a judgment module and an early warning module; the detection module detects actual parameters of the product; the data acquisition module sends the actual parameters to the quality analysis module; the quality analysis module calculates the relative quality index of the product according to the actual parameters of the product; the judgment module is preset with an early warning quality index and is used for comparing the relative quality index with the early warning quality index; and the early warning module gives an alarm when the relative quality index is lower than the early warning quality index. The invention is built in a modularized mode, not only can detect the quality of products, but also can send out an alarm in real time to the product batch lower than the early warning quality index line by building a relative quality index mathematical model and describing a relative quality index curve, thereby guiding the adjustment of front-end production process parameters in time, and having the advantages of quality evaluation, real-time early warning, improvement of yield, reduction of production cost and the like.

Description

Detection system and method based on relative quality index early warning
Technical Field
The invention relates to the technical field of product detection, in particular to a detection system and a detection method based on relative quality index early warning.
Background
With the advent of the industrial 4.0 era, the informatization of industrial manufacture is a necessary requirement, and all production links form a closed-loop networking link through big data. The product detection is the guarantee of the product quality and is an important component of the production link. Currently, the detection systems on the market sort only for qualified or unqualified products and optimize them in terms of detection efficiency and manufacturing cost, as in chinese patent applications 201610722201.1 and 201811227797.3. With the rapid development of integrated circuits, the improvement in detection efficiency has been limited. The manufacturing cost of the product is only realized by reducing the manufacturing cost of the detection system, and cannot be controlled from the production source of the product. In addition, the process analysis and optimization after the unqualified products are sorted has obvious hysteresis, and the production cost can be further increased if the unqualified products are repaired. The existing detection systems only simply detect the quality of products, such as Chinese patent application 201820155661.5, and cannot perform early warning and prompt on each process link on a production line. The chinese patent application 201410069318.5 with an early warning link is also only applied to the field of fatigue driving. A detection system for performing real-time early warning on product quality is not found in the field of product detection.
In summary, under the existing process conditions, the detection system and the detection method based on the relative quality index early warning are invented, and have practical and feasible significance for quality evaluation, real-time early warning, yield improvement and production cost reduction.
Disclosure of Invention
The invention aims to provide a detection system and a detection method based on relative quality index early warning, which can not only detect the quality of products, but also send out alarms in real time for product batches below a quality early warning line through a relative quality index curve so as to guide the adjustment of front-end production process parameters in time, and have the advantages of quality evaluation, real-time early warning, improvement of yield and the like.
In order to achieve the above purpose, the solution of the invention is:
a detection system based on relative quality index early warning comprises a detection module, a data acquisition module, a quality analysis module, a judgment module and an early warning module; the detection module is used for detecting actual parameters of the product; the data acquisition module is used for sending the actual parameters detected by the detection module to the quality analysis module; the quality analysis module is used for calculating a relative quality index of the product according to the actual parameters of the product, wherein the relative quality index is a quality trend of a subsequent product obtained by calculating the deviation amplitude of the actual parameters and the standard parameters of the continuously produced product; the judgment module is preset with an early warning quality index and is used for comparing the relative quality index of the product with the early warning quality index; the early warning module is used for giving an alarm when the relative quality index of the product is lower than the early warning quality index.
The quality analysis module is based on an equation
Figure BDA0002573023050000021
Calculating the relative quality index of the product, wherein RQI represents the relative quality index, OSM represents the average value exceeding the standard, USM represents the average value lower than the standard, and for the Nth product to be detected continuously, presetting a continuous comparison quantity K,
when the N is less than or equal to K,
Figure BDA0002573023050000022
Figure BDA0002573023050000023
when the N is greater than the K, the N is more than the K,
Figure BDA0002573023050000031
Figure BDA0002573023050000032
in the formula, AD represents a test parameter acquisition value of a product, and SD represents a test parameter standard value of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
A detection method based on relative quality index early warning comprises the following steps:
step 1, presetting a continuous comparison quantity K, a test parameter standard value and an early warning quality index of a product on detection equipment;
step 2, the detection equipment collects the test parameters of the tested product;
step 3, aiming at the continuously detected Nth product, comparing the test parameter standard values of the previous continuous K products including the Nth product and the product to calculate the relative quality index of the Nth product;
and 4, comparing the relative quality index of the Nth product with the early warning quality index, judging to send out an alarm by the detection equipment when the relative quality index is lower than the early warning quality index, and cancelling the alarm by the detection equipment when the relative quality index is higher than the early warning quality index.
In the step 3, the relative quality index of the Nth product
Figure BDA0002573023050000033
Wherein RQI represents the relative quality index, OSM represents the mean value over the standard, USM represents the mean value under the standard,
when N is less than or equal to K,
Figure BDA0002573023050000034
Figure BDA0002573023050000035
when the N is greater than the K, the N is more than the K,
Figure BDA0002573023050000041
Figure BDA0002573023050000042
in the above formula, AD represents the collection value of the test parameter of the product, and SD represents the standard value of the test parameter of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
After the technical scheme is adopted, the intelligent monitoring system is built in a modularized mode, not only can the quality of products be detected, but also the relative quality index mathematical model is built, the relative quality index curve is described, an alarm is given to the product batches lower than the early warning quality index line in real time, and the products with greatly fluctuating quality or the product batches with continuously reduced quality are found, so that the adjustment of front-end production process parameters is guided in time, and the intelligent monitoring system has the advantages of quality evaluation, real-time early warning, yield improvement, production cost reduction and the like.
Drawings
FIG. 1 is a block diagram of a detection system of the present invention;
FIG. 2 is a schematic flow chart of the detection method of the present invention;
FIG. 3 is a graph of relative quality index for audio detection according to the present invention;
FIG. 4 is a graph of relative quality index for transmittance measurements according to the present invention;
FIG. 5 is a graph of relative quality index for the assay of chlorothalonil in accordance with the present invention;
the reference numbers illustrate: a detection module 1; a data acquisition module 2; a quality analysis module 3; a judgment module 4; and an early warning module 5.
Detailed Description
In order to further explain the technical solution of the present invention, the present invention is explained in detail by the following specific examples.
Referring to fig. 1, the invention includes a detection system based on relative quality index early warning, which includes a detection module 1, a data acquisition module 2, a quality analysis module 3, a judgment module 4, and an early warning module 5.
The detection module 1 is used for detecting actual parameters of products; the data acquisition module 2 is used for sending the actual parameters detected by the detection module 1 to the quality analysis module 3; the quality analysis module 3 is used for calculating a relative quality index of the product according to the actual parameters of the product, wherein the relative quality index is a quality trend of a subsequent product obtained by calculating the deviation amplitude of the actual parameters and the standard parameters of the continuously produced product; the judgment module 4 is preset with an early warning quality index for comparing the relative quality index of the product with the early warning quality index; the early warning module 5 is used for giving an alarm when the relative quality index of the product is lower than the early warning quality index.
The quality analysis module 3 is based on an equation
Figure BDA0002573023050000051
Calculating the relative quality index of the product, wherein RQI represents the relative quality index, OSM represents the average value exceeding the standard, USM represents the average value lower than the standard, and for the Nth product to be detected continuously, presetting a continuous comparison quantity K,
when N is less than or equal to K,
Figure BDA0002573023050000052
Figure BDA0002573023050000053
when the N is greater than the K, the N is more than the K,
Figure BDA0002573023050000054
Figure BDA0002573023050000055
in the formula, AD represents a test parameter acquisition value of a product, and SD represents a test parameter standard value of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
Referring to fig. 2, the present invention further includes a detection method based on the relative quality index pre-warning, which includes the following steps:
step 1, presetting a continuous comparison quantity K, a test parameter standard value and an early warning quality index of a product on detection equipment;
step 2, the detection equipment collects the test parameters of the tested product;
step 3, aiming at the continuously detected Nth product, comparing the test parameter standard values of the previous continuous K products including the Nth product and the product to calculate the relative quality index of the Nth product;
and 4, comparing the relative quality index of the Nth product with the early warning quality index, judging to send out an alarm by the detection equipment when the relative quality index is lower than the early warning quality index, and cancelling the alarm by the detection equipment when the relative quality index is higher than the early warning quality index.
In step 3, the relative quality index of the Nth product
Figure BDA0002573023050000061
Wherein RQI represents the relative quality index, OSM represents the mean value over the standard, USM represents the mean value under the standard,
when N is less than or equal to K,
Figure BDA0002573023050000062
Figure BDA0002573023050000063
when the N is greater than the K, the N is more than the K,
Figure BDA0002573023050000064
Figure BDA0002573023050000065
in the formula, AD represents a test parameter acquisition value of a product, and SD represents a test parameter standard value of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
The invention is built in a modularized way, not only can detect the quality of products, but also can send out an alarm in real time for the product batches lower than the early warning quality index line by building a relative quality index mathematical model and describing a relative quality index curve, and find out the products with greatly fluctuating quality or the product batches with continuously reduced quality, thereby guiding the adjustment of front-end production process parameters in time, and having the advantages of quality evaluation, real-time early warning, improvement of yield, reduction of production cost and the like.
Referring to fig. 3, audio detection of a building intercom system is taken as an example.
Step one, setting the continuous comparison number K to be 10, wherein the input audio test standard fidelity is 80%, and the early warning quality index is 40; secondly, the audio fidelity of 10 products collected by the detection module 1 and the data collection module 2 is as shown in the following table; thirdly, calculating to obtain the relative quality index of each product during testing; and fourthly, judging whether the relative quality index is lower than the early warning quality index or not and whether an alarm is needed or not.
Figure BDA0002573023050000071
Referring to fig. 4, the transmittance of glass is measured as an example.
Step one, setting the continuous comparison number K to be 10, and inputting glass with the standard light transmittance of 90% and the early warning quality index of 40; secondly, the light transmittance of 10 products acquired by the detection module 1 and the data acquisition module 2 is as follows; thirdly, calculating to obtain the relative quality index of each product during testing; and fourthly, judging whether the relative quality index is lower than the early warning quality index or not and whether an alarm is needed or not.
Figure BDA0002573023050000072
Referring to FIG. 5, the detection of apple pesticide residue (chlorothalonil) is taken as an example.
Step one, setting the continuous comparison quantity K to 10, inputting the chlorothalonil standard of 1mg/kg and the early warning quality index of 40; secondly, the chlorothalonil content of 10 apples collected by the detection module 1 and the data collection module 2 is as shown in the following table; thirdly, calculating to obtain the relative quality index of each product during testing; and fourthly, judging whether the relative quality index is lower than the early warning quality index or not and whether an alarm is needed or not.
Figure BDA0002573023050000081
The above embodiments and drawings are not intended to limit the form and style of the present invention, and any suitable changes or modifications thereof by those skilled in the art should be considered as not departing from the scope of the present invention.

Claims (2)

1. The utility model provides a detecting system based on relative quality index early warning which characterized in that: the device comprises a detection module, a data acquisition module, a quality analysis module, a judgment module and an early warning module;
the detection module is used for detecting actual parameters of the product;
the data acquisition module is used for sending the actual parameters detected by the detection module to the quality analysis module;
the quality analysis module is used for calculating a relative quality index of the product according to the actual parameters of the product, wherein the relative quality index is a quality trend of a subsequent product obtained by calculating the deviation amplitude of the actual parameters and the standard parameters of the continuously produced product;
the judgment module is preset with an early warning quality index and is used for comparing the relative quality index of the product with the early warning quality index;
the early warning module is used for giving an alarm when the relative quality index of the product is lower than the early warning quality index;
the quality analysis module is based on an equation
Figure FDA0003638797990000011
Calculating the relative quality index of the product, wherein RQI represents the relative quality index, OSM represents the average value exceeding the standard, USM represents the average value lower than the standard, and for the Nth product to be detected continuously, presetting a continuous comparison quantity K,
when N is less than or equal to K,
Figure FDA0003638797990000012
Figure FDA0003638797990000013
when the N is greater than the K, the N is more than the K,
Figure FDA0003638797990000014
Figure FDA0003638797990000021
in the formula, AD represents a test parameter acquisition value of a product, and SD represents a test parameter standard value of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
2. A detection method based on relative quality index early warning is characterized by comprising the following steps:
step 1, presetting a continuous comparison quantity K, a test parameter standard value and an early warning quality index of a product on detection equipment;
step 2, the detection equipment collects the test parameters of the tested product;
step 3, aiming at the continuously detected Nth product, comparing the test parameter standard values of the previous continuous K products including the Nth product and the product to calculate the relative quality index of the Nth product;
step 4, comparing the relative quality index of the Nth product with the early warning quality index, judging to send out an alarm by the detection equipment when the relative quality index is lower than the early warning quality index, and cancelling the alarm by the detection equipment when the relative quality index is higher than the early warning quality index;
in the step 3, the relative quality index of the Nth product
Figure FDA0003638797990000022
Wherein RQI represents the relative quality index, OSM represents the mean value over the standard, USM represents the mean value under the standard,
when N is less than or equal to K,
Figure FDA0003638797990000023
Figure FDA0003638797990000024
when the N is greater than the K, the N is more than the K,
Figure FDA0003638797990000031
Figure FDA0003638797990000032
in the formula, AD represents a test parameter acquisition value of a product, and SD represents a test parameter standard value of the product; tpn (X) is a value function, and when X > 0, tpn (X) is equal to X, and when X is equal to or less than 0, tpn (X) is equal to 0.
CN202010645951.XA 2020-07-07 2020-07-07 Detection system and method based on relative quality index early warning Active CN111914208B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010645951.XA CN111914208B (en) 2020-07-07 2020-07-07 Detection system and method based on relative quality index early warning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010645951.XA CN111914208B (en) 2020-07-07 2020-07-07 Detection system and method based on relative quality index early warning

Publications (2)

Publication Number Publication Date
CN111914208A CN111914208A (en) 2020-11-10
CN111914208B true CN111914208B (en) 2022-07-12

Family

ID=73227587

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010645951.XA Active CN111914208B (en) 2020-07-07 2020-07-07 Detection system and method based on relative quality index early warning

Country Status (1)

Country Link
CN (1) CN111914208B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114969140B (en) * 2021-12-13 2023-06-13 淮阴师范学院 Method for detecting and analyzing performance data of fluent strip products

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101279882B1 (en) * 2013-02-13 2013-06-28 (주)전략해양 High water warning method and system thereof according to high water reference value based on time
CN105184427A (en) * 2015-10-23 2015-12-23 石河子大学 Method and device for early warning of farmland ecological environment
CN105512466A (en) * 2015-11-30 2016-04-20 华北电力大学 Power grid project implementation phase risk early warning method based on extreme value theory
CN105574342A (en) * 2015-12-17 2016-05-11 中国环境科学研究院 Mixed type rare earth mining area water environment quality early-warning technology
CN106875098A (en) * 2017-01-19 2017-06-20 华北电力大学 A kind of electrically-charging equipment is to electrokinetic cell security incident pre-alerting ability method for quantitatively evaluating
CN107677614A (en) * 2017-10-16 2018-02-09 广东省测试分析研究所(中国广州分析测试中心) Heavy metal pollution risk on-line early warning system and method in a kind of water

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101279882B1 (en) * 2013-02-13 2013-06-28 (주)전략해양 High water warning method and system thereof according to high water reference value based on time
CN105184427A (en) * 2015-10-23 2015-12-23 石河子大学 Method and device for early warning of farmland ecological environment
CN105512466A (en) * 2015-11-30 2016-04-20 华北电力大学 Power grid project implementation phase risk early warning method based on extreme value theory
CN105574342A (en) * 2015-12-17 2016-05-11 中国环境科学研究院 Mixed type rare earth mining area water environment quality early-warning technology
CN106875098A (en) * 2017-01-19 2017-06-20 华北电力大学 A kind of electrically-charging equipment is to electrokinetic cell security incident pre-alerting ability method for quantitatively evaluating
CN107677614A (en) * 2017-10-16 2018-02-09 广东省测试分析研究所(中国广州分析测试中心) Heavy metal pollution risk on-line early warning system and method in a kind of water

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
土壤镉食品卫生安全阈值影响因素及预测模型——以长沙某地水稻土为例;和君强 等;《土壤学报》;20181031;全文 *

Also Published As

Publication number Publication date
CN111914208A (en) 2020-11-10

Similar Documents

Publication Publication Date Title
CN109459993B (en) Online adaptive fault monitoring and diagnosing method for process industrial process
CN104360677B (en) Cigarette processing quality evaluation and diagnosis method
WO2023197461A1 (en) Gearbox fault early warning method and system based on working condition similarity evaluation
CN109772724A (en) A kind of flexible detection and analysis system of casting emphasis surface and internal flaw
CN100517141C (en) System and method for detecting date and diagnosing failure of propylene polymerisation production
CN109741927B (en) Intelligent prediction system for equipment faults and potential defective products of miniature transformer production line
CN106656669B (en) A kind of device parameter abnormality detection system and method based on threshold adaptive setting
CN111914208B (en) Detection system and method based on relative quality index early warning
CN112487708A (en) Resistance spot welding quality prediction method based on ensemble learning
CN111337767B (en) Fault analysis method for resonant wave speed reducer
CN116184950B (en) Multisource data extraction and analysis system for automobile production line
CN112287988A (en) Method for identifying water pollution source online monitoring data abnormity
CN116339253A (en) Intelligent mechanical production monitoring management and control system based on Internet of things
CN110856437B (en) SMT production process control chart pattern recognition method
CN105082488A (en) Adaptive control system and method of injection molding equipment
CN115829335A (en) Production line execution risk assessment system for aluminum profile machining
CN115780555A (en) Section bar processing risk evaluation system for solar frame porous extrusion
CN111689169A (en) Multi-mode data fusion-based conveyor belt anomaly detection method
CN111985654A (en) Intelligent equipment health management system and method
CN201035377Y (en) Failure diagnosis device of melt index detecting in polymerization of propylene produce
CN116037705A (en) Real-time monitoring system for working state of cold stamping die
CN201017225Y (en) Polymerization of propylene production data detecting and failure diagnosis device
CN109297582A (en) The detection device and detection method of fan abnormal sound
CN117850375B (en) Multi-dimensional monitoring system of production line
CN117123640B (en) Feeding positioning precision detection method and system for die processing equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20231207

Address after: 361000 No. 65 South South Road, Jimei District, Fujian, Xiamen

Patentee after: XIAMEN LEELEN TECHNOLOGY Co.,Ltd.

Address before: No.9 Shigu Road, Jimei District, Xiamen City, Fujian Province 361000

Patentee before: JIMEI University

Patentee before: XIAMEN LEELEN TECHNOLOGY Co.,Ltd.

TR01 Transfer of patent right