CN113703394A - Cutter monitoring and managing method and system based on edge calculation - Google Patents
Cutter monitoring and managing method and system based on edge calculation Download PDFInfo
- Publication number
- CN113703394A CN113703394A CN202110986506.4A CN202110986506A CN113703394A CN 113703394 A CN113703394 A CN 113703394A CN 202110986506 A CN202110986506 A CN 202110986506A CN 113703394 A CN113703394 A CN 113703394A
- Authority
- CN
- China
- Prior art keywords
- edge
- cloud
- monitoring
- computing
- management
- 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.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 52
- 238000004364 calculation method Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000008859 change Effects 0.000 claims abstract description 5
- 238000007726 management method Methods 0.000 claims description 37
- 238000011161 development Methods 0.000 claims description 6
- 238000013473 artificial intelligence Methods 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 abstract description 9
- 238000001514 detection method Methods 0.000 description 17
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000018109 developmental process Effects 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000003062 neural network model Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009411 base construction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
- G05B19/4065—Monitoring tool breakage, life or condition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G05B2219/37—Measurements
- G05B2219/37616—Use same monitoring tools to monitor tool and workpiece
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Manufacturing & Machinery (AREA)
- Automation & Control Theory (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention provides a cutter monitoring and managing system and method based on edge calculation, wherein the managing system comprises: the system comprises an edge computing management platform, a cutter monitoring module, a cloud management center module and edge cloud nodes; the edge computing management platform is used for configuring an edge computing application; the cutter monitoring module is used for monitoring the data change condition; the cloud management center module is connected with the edge computing management platforms and is used for uniformly and centrally managing the edge computing management platforms; the edge cloud node is used for processing the acquired information uploaded by the cutter monitoring equipment in the area to obtain processing result data and sending the processing result data to the cloud management center module. The edge calculation is a distributed open platform which integrates network, calculation, storage and application core capabilities at the edge side of a network close to an object or a data source and provides edge intelligent monitoring service nearby.
Description
Technical Field
The invention relates to the field of edge calculation, in particular to a cutter monitoring and managing method and system based on edge calculation.
Background
Conventional tool machining systems face a number of challenges. At present, cutter monitoring solutions such as a cutter processing counting prediction method, a sensor physical monitoring method and the like exist in the market, and the defects of marginalization of calculation and analysis, high cost, slow deployment, low accuracy and the like exist. The overall maintenance, monitoring and upgrading cost is high, and the manual support cost is high. The software and hardware updating speed is low, hardware such as a workstation and the like is replaced every 3-5 years, and the aging performance of hardware equipment at the later stage is reduced; the design software adopts a permanent license mode, and is difficult to maintain the latest version for a long time. The search for an edge intelligent platform and the provision of an efficient and reliable intelligent Internet of things system for monitoring the cutter is a problem to be solved urgently by current manufacturing enterprises.
Disclosure of Invention
In view of the above, the present invention has been developed to provide an edge-calculation-based tool monitoring management method and system that overcomes or at least partially solves the above-mentioned problems.
According to an aspect of the present invention, there is provided a tool monitoring management system based on edge calculation, the management system comprising: the system comprises an edge computing management platform, a cutter monitoring module, a cloud management center module and edge cloud nodes;
the edge computing management platform is used for configuring an edge computing application;
the cutter monitoring module is used for monitoring the data change condition;
the cloud management center module is connected with the edge computing management platforms and is used for uniformly and centrally managing the edge computing management platforms;
the edge cloud node is used for processing the acquired information uploaded by the cutter monitoring equipment in the area to obtain processing result data and sending the processing result data to the cloud management center module.
Optionally, the monitoring of the data change condition specifically includes data processing, model development, training, management, and deployment.
Optionally, the cloud management center module is used for installation and arrangement of the whole cloud platform, management of the virtual host cloud and the container cloud, pushing of the edge cloud platform through the application arrangement template, automatic application pull-up, and meanwhile, automatic pushing of the artificial intelligence computing model.
Optionally, the edge cloud node is further configured to transmit the structured data back to the cloud management center module, and return a calculation result of the monitoring service to the upper-layer application of the user.
A tool monitoring management method based on edge calculation, the management method comprising:
the cloud management center manages a plurality of edge cloud centers in a unified mode, is responsible for unified arrangement of the application on the container platform, pushes the edge cloud platform application to be automatically pulled up through the arrangement template, and is responsible for automatic pushing of the artificial intelligence computing model;
the edge cloud center manages and collects data of cutter monitoring in the area, local intelligent analysis processing is carried out by utilizing edge node computing resources, preprocessing and pre-judging are carried out on the data, the structured data are transmitted back to the global center, and the computing result of cutter monitoring is returned to the upper application of a user.
The invention provides a cutter monitoring and managing method and system based on edge calculation, wherein the edge calculation is a distributed open platform integrating network, calculation, storage and application core capabilities on the edge side of a network close to an object or a data source, and edge intelligent monitoring service is provided nearby.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a diagram of an intelligent manufacturing edge calculation solution provided by an embodiment of the present invention;
FIG. 2 is a block diagram of a smart IOT system for tool monitoring based on edge cloud computing according to an embodiment of the present invention;
FIG. 3 is a diagram of AI development platform components for application developers according to an embodiment of the present invention;
fig. 4 is an edge cloud computing node component diagram of an intelligent tool monitoring internet of things system based on edge cloud computing according to an embodiment of the present invention;
FIG. 5 is a flowchart of a technique for acquiring and analyzing edge side tool load data according to an embodiment of the present invention;
fig. 6 is a deep convolution long-time memory neural network model adopted in the tool monitoring system according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terms "comprises" and "comprising," and any variations thereof, in the present description and claims and drawings are intended to cover a non-exclusive inclusion, such as a list of steps or elements.
The technical solution of the present invention is further described in detail with reference to the accompanying drawings and embodiments.
As shown in fig. 1, an intelligent manufacturing edge calculation solution diagram is provided by an embodiment of the present invention. As shown in fig. 1, the platform contains edge App lifecycle management, edge PaaS lifecycle management, edge IaaS lifecycle management, edge storage lifecycle management, and so on; the edge computing platform provides elastic computing service based on cloud edge nodes and side nodes of the intelligent tool monitoring system, can meet key requirements of the tool monitoring system in the aspects of response, near data processing, intelligent data analysis, rapid application deployment and the like in business real time, and can flexibly configure and manage large-scale edge computing application.
Fig. 2 is a system architecture diagram of a tool monitoring smart internet of things system based on edge cloud computing according to an embodiment of the present invention. As shown in FIG. 2, the system hierarchy design includes a cloud pipe center (local or off-site) and edge cloud platforms. The cloud pipe center is used for centrally and uniformly managing a plurality of edge cloud platforms. The cloud management center is responsible for installation and arrangement of the whole cloud platform, management of the virtual host cloud and the container cloud, pushing of edge computing nodes through the application arrangement template, automatic application pull-up and automatic pushing of an artificial intelligence computing model;
the edge cloud node is used for managing and collecting cutter monitoring in the area, preprocessing and pre-judging data, transmitting the structured data back to the cloud management center, and returning the calculation result of the monitoring service to the upper application of the user.
FIG. 3 is a developer-oriented one-stop AI development platform providing mass data preprocessing and semi-automated labeling, large-scale distributed tracking, automated model generation, and end-edge-cloud model on-demand deployment capabilities for machine learning and deep learning; and the rapid development and deployment of the service are realized.
The lower side contains infrastructure management, an operating system, heterogeneous hardware. The upper-layer application analyzes the data and predicts the result, the collected data are compared by the rule engine, the calculation of a certain function is triggered when the collected data meet the conditions, and the examples of the function calculation are uniformly managed by the local function calculation and the background engine. After the development is completed, integrating basic configuration files of all components, and verifying the deployability and the security of the package; the state of the package is changed into formal online release; and deploying the formally online package to the edge computing systems of the types corresponding to different application scenes by the deployment operation service.
Fig. 4 is a diagram of an edge cloud computing node component of a smart internet of things system for tool monitoring based on edge cloud computing according to an embodiment of the present invention; as shown in fig. 4, the platform includes an internet of things infrastructure, edge cloud computing nodes, and a cloud management center. Compared with the traditional tool monitoring system, the local computing node is increased.
The infrastructure of the internet of things comprises various internet of things devices (such as an environment sensor, an RFID label, a camera, a smart phone, an industrial billboard and the like), mainly completes the functions of collecting and reporting original data, and takes the form of an event source as the input of application service.
The edge computing node comprises an edge gateway and a service component, wherein the service component comprises local storage, task scheduling, a rule engine, log management and the like. The edge nodes strengthen the computing power of the local computing nodes, and some simple business logics directly process and return information to the user equipment at the edge nodes.
The edge computing node realizes basic service response by reasonably deploying and allocating computing and storage capacity of the network edge side and opening an API.
And the cloud management center calls operation management and equipment management through an API. The cloud management center realizes the uniform resource scheduling and the uniform resource receiving management through the orchestrator and is responsible for the mirror image distribution. The cloud management center remotely manages the local computing nodes by calling the API. The system is mainly responsible for equipment management, component management and node management of local computing nodes, automatic pushing application arrangement and self-adaptive deployment without manual monitoring and deployment.
The edge cloud center and the cloud pipe center are integrated in the form of an open API. And the local computing node and the cloud management center can be conveniently called. Whether the manual operation of the machine tool is standard or not can be remotely monitored and managed, the specific position, the state and the like of the cutter in workshop processing can be monitored and tracked in real time, all data are collected, and real-time planning and scheduling and accurate control are improved.
As shown in fig. 5, the process includes a CNC machine control system data acquisition and integration service, a tool torque monitoring and tool life correlation analysis and prediction service, a tool setting gauge & product measurement data & tool load data integration and intelligent compensation, a tool & parameter information base construction and intelligent tool selection system, and a CNC digital intelligent workshop integration service.
As shown in fig. 6. And preprocessing the acquired cutter data, and selecting a deep convolution long-time memory neural network model as a target detection model. The deep learning-based DeepConvLSTM algorithm integrates Convolution (Convolution) and LSTM operation, and can learn the spatial attribute and the time attribute of a sample. In the convolution operation, the waveform signal is filtered by multiplying the signal with a convolution kernel, preserving the high level information. In LSTM operation, the timing relationship between signals is discovered by remembering or forgetting preamble information.
Edge computing is used as a new computing mode, computing and storage resources are deployed on the side of terminal equipment, and therefore computing capacity with higher real-time performance and response capacity of services are obtained; non-critical data are processed at the edge side and do not need to be uploaded to a data center, so that network overhead and resource pressure of cloud computing are greatly reduced. The edge computing node provides the capability of edge computing based on the technologies of a rule engine, function computing and the like, a service program running on the edge computing node actively acquires data of the terminal equipment and executes partial computing tasks on the edge side so as to reduce transmission of useless data between the edge computing node and a platform. More efficient and reliable tool monitoring can be provided.
Has the advantages that:
the deep convolution long-time memory neural network model is used as a training frame of a target detection model, network structures and loss functions of a plurality of models are modified in a targeted mode, a detection scheme is designed according to detection requirements and detection effects, the target detection network is trained and classified, the advantages and disadvantages of the schemes are compared from the aspects of detection accuracy, recall rate, detection frame rate and the like, and an optimal scheme is selected. After the used model is determined, the flow shown in the block diagram 3 is adopted in the deployment stage, the model structure is optimized, the trained weight is quantized, the model reasoning is accelerated, and the detection efficiency is further improved under the condition that the precision is not lost. Through the model, parameters are modified to achieve on-line deep learning, actions are selected, then on-site feedback is carried out, production is continuously optimized, and quality loss caused by abnormal cutters is effectively avoided.
The platform comprises edge App lifecycle management, edge PaaS lifecycle management, edge IaaS lifecycle management, edge storage lifecycle management, and the like; the rapid deployment of the application is met, the large-scale edge computing application can be flexibly configured and managed, and the landing of the edge application is accelerated; the problems that the software and hardware updating speed is low and the aging performance of hardware equipment is reduced in the later period are effectively solved; the perpetual license mode may be employed to keep newer versions, depending on the actual use of the software version to which the design is to be avoided. The computing and storage resources are deployed on the side of the terminal equipment, so that the computing capability and the service response capability with higher real-time performance are obtained, and the defects of slow data transmission and complex computation are overcome; non-critical data are processed at the edge side and do not need to be uploaded to a data center, so that network overhead and resource pressure of cloud computing are greatly reduced. And the cutter monitoring efficiency can be improved by optimizing resource allocation through calculation resource scheduling. The method is easy to expand, integrates edge virtual machines, storage and edge services nearby, combines system application, and expands and contracts the edge application virtual environment.
The edge device provides the capability of edge calculation based on the technologies of a rule engine, function calculation and the like, a service program running on the edge device actively acquires data of the terminal device, and partial calculation tasks are executed on the edge side, so that transmission of useless data between the edge device and the platform is reduced. The edge data can be uniformly managed on the core cloud platform, so that management and monitoring are facilitated.
The method comprises the steps of adopting a deep convolution long-time memory neural network model as a target detection model training framework, pertinently modifying network structures and loss functions of a plurality of models, designing a detection scheme according to detection requirements and detection effects, training a target detection network and classifying, comparing the advantages and disadvantages of the schemes from the aspects of detection accuracy, recall rate, detection frame rate and the like, and selecting an optimal scheme. After the used model is determined, the model structure is optimized, the trained weight is quantized, model reasoning is accelerated, and the detection efficiency is further improved under the condition that the precision is not lost.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. An edge-computing based tool monitoring and management system, the management system comprising: the system comprises an edge computing management platform, a cutter monitoring module, a cloud management center module and edge cloud nodes;
the edge computing management platform is used for configuring an edge computing application;
the cutter monitoring module is used for monitoring the data change condition;
the cloud management center module is connected with the edge computing management platforms and is used for uniformly and centrally managing the edge computing management platforms;
the edge cloud node is used for processing the acquired information uploaded by the cutter monitoring equipment in the area to obtain processing result data and sending the processing result data to the cloud management center module.
2. The system of claim 1, wherein the monitored data change comprises data processing, model development, training, management, deployment.
3. The system for monitoring and managing the cutting tools based on the edge computing as claimed in claim 1, wherein the cloud management center module is used for installation and arrangement of the whole cloud platform, management of a virtual host cloud and a container cloud, pushing of the edge cloud platform through an application arrangement template, automatic pulling of an application, and automatic pushing of an artificial intelligence computing model.
4. The system for monitoring and managing cutters based on edge computing as claimed in claim 1, wherein the edge cloud node is further configured to transmit structured data back to the cloud management center module, and return the computing result of the monitoring service to the upper layer application of the user.
5. A tool monitoring and managing method based on edge calculation is characterized by comprising the following steps:
the cloud management center manages a plurality of edge cloud centers in a unified mode, is responsible for unified arrangement of the application on the container platform, pushes the edge cloud platform application to be automatically pulled up through the arrangement template, and is responsible for automatic pushing of the artificial intelligence computing model;
the edge cloud center manages and collects data of cutter monitoring in the area, local intelligent analysis processing is carried out by utilizing edge node computing resources, preprocessing and pre-judging are carried out on the data, the structured data are transmitted back to the global center, and the computing result of cutter monitoring is returned to the upper application of a user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110986506.4A CN113703394A (en) | 2021-08-26 | 2021-08-26 | Cutter monitoring and managing method and system based on edge calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110986506.4A CN113703394A (en) | 2021-08-26 | 2021-08-26 | Cutter monitoring and managing method and system based on edge calculation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113703394A true CN113703394A (en) | 2021-11-26 |
Family
ID=78654965
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110986506.4A Pending CN113703394A (en) | 2021-08-26 | 2021-08-26 | Cutter monitoring and managing method and system based on edge calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113703394A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116483019A (en) * | 2023-06-16 | 2023-07-25 | 深圳市汇辰自动化技术有限公司 | Industrial Internet of things cloud management and control system based on PLC |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019082836A (en) * | 2017-10-30 | 2019-05-30 | 株式会社ジェイテクト | Tool life prediction device |
| CN110394688A (en) * | 2019-09-02 | 2019-11-01 | 太原科技大学 | Conditions of machine tool monitoring method based on edge calculations |
| CN110554657A (en) * | 2019-10-16 | 2019-12-10 | 河北工业大学 | Health diagnosis system and diagnosis method for operation state of numerical control machine tool |
| CN110827164A (en) * | 2019-11-14 | 2020-02-21 | 浙江九州云信息科技有限公司 | Intelligent aquaculture management system and method based on edge cloud |
| WO2020207264A1 (en) * | 2019-04-08 | 2020-10-15 | 阿里巴巴集团控股有限公司 | Network system, service provision and resource scheduling method, device, and storage medium |
| CN111813502A (en) * | 2020-07-10 | 2020-10-23 | 重庆邮电大学 | Computing resource management scheduling method for industrial edge nodes |
| CN111890127A (en) * | 2020-08-06 | 2020-11-06 | 南京航空航天大学 | Cutting state edge intelligent monitoring method based on online incremental wear evolution model |
| CN112100777A (en) * | 2020-11-16 | 2020-12-18 | 杭州景业智能科技股份有限公司 | Tool life prediction method and device based on edge calculation and computer equipment |
| WO2021056949A1 (en) * | 2019-09-24 | 2021-04-01 | 厦门网宿有限公司 | Edge application management method and system |
-
2021
- 2021-08-26 CN CN202110986506.4A patent/CN113703394A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2019082836A (en) * | 2017-10-30 | 2019-05-30 | 株式会社ジェイテクト | Tool life prediction device |
| WO2020207264A1 (en) * | 2019-04-08 | 2020-10-15 | 阿里巴巴集团控股有限公司 | Network system, service provision and resource scheduling method, device, and storage medium |
| CN110394688A (en) * | 2019-09-02 | 2019-11-01 | 太原科技大学 | Conditions of machine tool monitoring method based on edge calculations |
| WO2021056949A1 (en) * | 2019-09-24 | 2021-04-01 | 厦门网宿有限公司 | Edge application management method and system |
| CN110554657A (en) * | 2019-10-16 | 2019-12-10 | 河北工业大学 | Health diagnosis system and diagnosis method for operation state of numerical control machine tool |
| CN110827164A (en) * | 2019-11-14 | 2020-02-21 | 浙江九州云信息科技有限公司 | Intelligent aquaculture management system and method based on edge cloud |
| CN111813502A (en) * | 2020-07-10 | 2020-10-23 | 重庆邮电大学 | Computing resource management scheduling method for industrial edge nodes |
| CN111890127A (en) * | 2020-08-06 | 2020-11-06 | 南京航空航天大学 | Cutting state edge intelligent monitoring method based on online incremental wear evolution model |
| CN112100777A (en) * | 2020-11-16 | 2020-12-18 | 杭州景业智能科技股份有限公司 | Tool life prediction method and device based on edge calculation and computer equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116483019A (en) * | 2023-06-16 | 2023-07-25 | 深圳市汇辰自动化技术有限公司 | Industrial Internet of things cloud management and control system based on PLC |
| CN116483019B (en) * | 2023-06-16 | 2023-08-25 | 深圳市汇辰自动化技术有限公司 | Industrial Internet of things cloud management and control system based on PLC |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11947438B2 (en) | Operation and maintenance system and method | |
| US10262019B1 (en) | Distributed management optimization for IoT deployments | |
| CN112579653B (en) | Gradual contextualization and analysis of industrial data | |
| CN112581303A (en) | Artificial intelligence channel for industrial automation | |
| US11422859B2 (en) | Methods and systems for cloud application optimization | |
| KR101825881B1 (en) | Method of managing a manufacturing process and system using the same | |
| CN111181773B (en) | Delay prediction method for multi-component application of heterogeneous border cloud collaborative intelligent system | |
| US11579933B2 (en) | Method for establishing system resource prediction and resource management model through multi-layer correlations | |
| CN112882810B (en) | High-throughput task processing method based on supercomputer | |
| CN111355606A (en) | Web application-oriented container cluster self-adaptive expansion and contraction system and method | |
| US11658878B2 (en) | Systems and methods for automated network state and network inventory tracking | |
| CN118092404B (en) | Method and system for preventive maintenance of PLC (programmable logic controller) network based on artificial intelligence | |
| WO2024027384A1 (en) | Fault detection method, apparatus, electronic device, and storage medium | |
| CN117110748A (en) | Transformer substation main equipment operation state abnormality detection method based on fusion terminal | |
| KR20210147594A (en) | Edge computing devices and methods that provide optimization for energy data collection and management | |
| CN117909083A (en) | Distributed cloud container resource scheduling method and system | |
| CN113703394A (en) | Cutter monitoring and managing method and system based on edge calculation | |
| Gaikwad et al. | A framework design for algorithmic it operations (aiops) | |
| US20230403210A1 (en) | Methods and systems for cloud application optimization | |
| US20210133211A1 (en) | Adaptive Usage of Storage Resources Using Data Source Models and Data Source Representations | |
| CN114819367A (en) | Public service platform based on industrial internet | |
| CN114676002A (en) | PHM technology-based system operation and maintenance method and device | |
| CN116450485B (en) | Detection method and system for application performance interference | |
| WO2024179066A1 (en) | Industrial internet platform for rail transit system, and method and storage medium | |
| Butylin | Predictive maintenance framework for a vehicular iot gateway node using active database rules |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211126 |