CN114338281A - Model distribution application design method and system based on edge computing gateway - Google Patents
Model distribution application design method and system based on edge computing gateway Download PDFInfo
- Publication number
- CN114338281A CN114338281A CN202111332194.1A CN202111332194A CN114338281A CN 114338281 A CN114338281 A CN 114338281A CN 202111332194 A CN202111332194 A CN 202111332194A CN 114338281 A CN114338281 A CN 114338281A
- Authority
- CN
- China
- Prior art keywords
- model
- equipment
- edge computing
- service equipment
- computing gateway
- 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.)
- Granted
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000013461 design Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 48
- 230000002159 abnormal effect Effects 0.000 claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 37
- 230000003993 interaction Effects 0.000 claims abstract description 29
- 238000007726 management method Methods 0.000 claims description 92
- 230000002452 interceptive effect Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 14
- 230000010365 information processing Effects 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000006835 compression Effects 0.000 claims description 7
- 238000007906 compression Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 4
- 238000004806 packaging method and process Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 12
- 238000012423 maintenance Methods 0.000 description 12
- 230000008569 process Effects 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000010295 mobile communication Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/66—Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/40—Network security protocols
-
- Y—GENERAL 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention provides a model distribution application design method and system based on an edge computing gateway. Selecting a preset template according to the equipment model and an application scene through a human-computer interaction interface, and inputting equipment configuration parameters according to the preset template; processing the information of the general model by using an encryption algorithm; processing the information of the specific model by using an encryption algorithm; judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not, and carrying out deployment updating on an equipment computing model through the edge equipment; obtaining model configuration parameters, judging an application scene of equipment, and performing parameter adjustment on a target model; and judging whether the edge computing gateway service equipment has an abnormal state or not, and feeding back the obtained abnormal state to the cloud management service equipment. According to the scheme, the model of the server is obtained in an instruction mode, the upper computer terminal device is physically connected with the edge terminal device, and the upper computer terminal device deploys the model on the edge terminal device.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to a model distribution application design method and system based on an edge computing gateway.
Background
With the rapid development of the internet of things, the interconnection of everything becomes a trend of future development, and the embedded node equipment will show a great increase. The data interaction is carried out between the traditional cloud-side data center and thousands of nodes of the Internet of things, so that the computing resource cost is huge, network congestion exists, and the data interaction is more obvious in peak hours. Although 5G wireless mobile communication with high bandwidth is gradually popularized, for some emerging application scenarios, such as: unmanned, safety monitoring, roadside perception data and the like need low delay, are highly reliable, and traditional node to high in the clouds can't satisfy the demand of these application services. Therefore, the novel edge terminal AI calculation is explored in all the industries at present, and the method has a vital influence on the safety and performance improvement of the Internet of things system. Since the emerging application service needs to customize a specific scene model, or to deploy a general scene model in a large scale, and perform configuration such as parameter quantization, a specific method is urgently needed to solve the problem of difficult frequent replacement and deployment.
Disclosure of Invention
In view of the above problems, the present invention provides a model distribution application design method and system based on an edge computing gateway, which obtains a model of a server in an instruction manner, and performs model deployment on an edge device by an upper computer terminal device through physical connection between the upper computer terminal device and the edge device.
According to a first aspect of the embodiments of the present invention, a model distribution application design method based on an edge computing gateway is provided.
In one or more embodiments, preferably, the method for model distribution application design based on edge computing gateway includes:
selecting a preset template in a model distribution system of an edge computing gateway through a human-computer interaction interface according to the equipment model and an application scene, and inputting equipment configuration parameters according to the preset template;
acquiring the equipment configuration parameters, judging whether a universal model exists or not, and processing the information of the universal model by using an encryption algorithm after the universal model is acquired;
acquiring the equipment configuration parameters, judging whether a specific model exists or not, and processing information of the specific model by using the encryption algorithm after the specific model is acquired;
judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not, and carrying out deployment updating on an equipment computing model through the edge equipment;
obtaining model configuration parameters, judging an application scene of equipment, and performing parameter adjustment on the target model;
judging whether the edge computing gateway service equipment has an abnormal state or not, and feeding back the obtained abnormal state to cloud management service equipment;
the model distribution system of the edge computing gateway comprises cloud management service equipment and edge computing gateway service equipment, wherein the cloud management service equipment comprises interaction management service equipment and processing management service equipment;
the encryption algorithm specifically adopts a symmetric encryption mode, and the specific flow is as follows:
issuing a model distribution request to the edge computing gateway service equipment at the cloud management service equipment;
the edge computing gateway service equipment randomly selects a compression mode and an encryption mode to generate a symmetric encryption key;
sending the symmetric encryption key to the processing management service equipment, analyzing a message, and encrypting a model to generate a target model;
and issuing the target model to the edge computing gateway service equipment.
In one or more embodiments, preferably, the selecting, in the model distribution system of the edge computing gateway, a preset template according to the device model and the application scenario through a human-computer interaction interface, and entering device configuration parameters according to the preset template specifically includes:
connecting the Internet of things equipment node with the edge computing gateway through one or more of R485, network and IO bus modes;
setting equipment models and application scenes through a human-computer interaction interface;
automatically obtaining corresponding equipment configuration parameters in a preset calculation model according to the equipment model and the application scene;
and issuing the corresponding equipment configuration parameters through one or more of R485, network and IO bus modes.
In one or more embodiments, preferably, the obtaining of the device configuration parameters, determining whether a generic model exists, and after obtaining the generic model, performing information processing on the generic model by using an encryption algorithm:
sending a universal scene model request by utilizing the edge computing gateway service equipment;
encrypting and reporting general scene model information to the processing management service equipment by using the encryption algorithm according to the general scene model request, wherein the general scene model information comprises equipment model, performance, a compiling chain, an application scene type and application scene adaptability corresponding to a general scene;
reporting the general scene model information to the interactive management service equipment by using the processing management service equipment;
retrieving all the general target models from a model database according to the equipment models, the application scenes and the compiling chains corresponding to the general scenes;
and sending the general target model to edge computing gateway management service equipment through the processing management service equipment.
In one or more embodiments, preferably, the obtaining the device configuration parameters, determining whether a specific model exists, and after obtaining the specific model, performing information processing on the specific model by using the encryption algorithm specifically includes:
acquiring a specific scene model request, and reporting specific scene model information by the edge computing gateway service equipment by using the encryption algorithm, wherein the specific scene model information comprises equipment model, performance, compiling chain, application scene type and application scene adaptability corresponding to a specific scene;
replying a first reply request to the edge computing gateway service equipment through the processing management service equipment;
after the edge computing gateway service equipment receives the first reply request, the specific scene model information is transmitted back to the processing management service equipment and then transmitted to the interaction management service equipment;
screening, marking and selecting a specific target model for data according to a specific scene through the interactive management service equipment;
and the interactive management service equipment feeds back a second reply request to the edge computing gateway service equipment through the processing management service equipment.
In one or more embodiments, preferably, the determining whether the edge computing gateway service device has a device active model acquisition request, and performing deployment update of a device computing model through the edge device specifically includes:
judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not;
acquiring a model request actively acquired by the equipment, and uploading a corresponding equipment parameter value to the cloud management service equipment;
obtaining a corresponding model according to a preset model library and the corresponding equipment parameter values, further packaging, and generating an active acquisition model through an encryption algorithm;
and obtaining the active acquisition model and issuing the active acquisition model to the edge computing gateway service equipment.
In one or more embodiments, preferably, the obtaining of the model configuration parameters, determining an application scenario of the device, and performing parameter adjustment on the target model specifically include:
obtaining the model configuration parameters in the interaction management service equipment;
issuing model parameters to the corresponding edge computing gateway service equipment through the processing management service equipment according to the model configuration parameters;
and modifying the target model according to the model configuration parameters.
In one or more embodiments, preferably, the determining whether the edge computing gateway service device has an abnormal state and feeding back the obtained abnormal state to a cloud management service device includes:
uploading the parameters and abnormal data of the equipment to the processing management service equipment according to the edge computing gateway service equipment;
the processing management service equipment feeds back the abnormal condition to the interactive management service equipment;
in the interactive management service equipment, a worker analyzes according to condition feedback and adjusts the target model;
and re-issuing the target model to the edge computing gateway service equipment.
According to a second aspect of the embodiments of the present invention, a model distribution application design system based on an edge computing gateway is provided.
In one or more embodiments, preferably, the model distribution application design system based on the edge computing gateway includes:
the device configuration module is used for selecting a preset template according to the device model and the application scene through a human-computer interaction interface in a model distribution system of the edge computing gateway, and inputting device configuration parameters according to the preset template;
the universal model information module is used for acquiring the equipment configuration parameters, judging whether a universal model exists or not, and processing the information of the universal model by using an encryption algorithm after the universal model is acquired;
the specific model information module is used for acquiring the equipment configuration parameters, judging whether a specific model exists or not, and processing the information of the specific model by using the encryption algorithm after the specific model is acquired;
the active model updating module is used for judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not and carrying out deployment updating on the equipment computing model through the edge equipment;
the passive model updating module is used for acquiring model configuration parameters, judging an application scene of equipment and adjusting parameters of the target model;
and the abnormal state analysis module is used for judging whether the edge computing gateway service equipment has an abnormal state or not and feeding back the obtained abnormal state to the cloud management service equipment.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
According to a fourth aspect of embodiments of the present invention, there is provided an electronic device, comprising a memory and a processor, the memory being configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the steps of any one of the first aspect of embodiments of the present invention.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
1) the scheme of the invention has the characteristic of high automation degree, particularly realizes the unified management of a unified cloud center, reduces manual intervention, realizes automatic data monitoring and eliminates abnormal faults;
2) the scheme of the invention has the characteristics of high flexibility and strong expansibility of equipment, specifically, the model can be remotely deployed, issued and upgraded in a targeted manner according to different actual scene requirements, so that the flexibility and the application range of the edge calculation at the end side are improved, and different application scenes can be adapted to different hardware peripherals according to the function, so that different functions are realized;
3) the scheme of the invention has the characteristics of convenient operation and maintenance and low cost, particularly facilitates the upgrade and maintenance of software, does not need to carry out model deployment on the edge computing gateway on site, reduces the manual maintenance, and particularly realizes large-scale general model deployment through cloud service when the number of the edge computing gateways reaches a certain magnitude, thereby reducing the cost and expense;
4) the scheme of the invention has the characteristic of high safety, and particularly, compared with the field deployment of an upper computer, the method of directly adopting a cloud end-to-edge gateway mode is adopted, so that the data packet leakage condition is prevented to a great extent, and the information safety problem is solved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be 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 flowchart of a model distribution application design method based on an edge computing gateway according to an embodiment of the present invention.
Fig. 2 is a flowchart of selecting a preset template according to the device model and the application scenario through a human-computer interface in a model distribution system of an edge computing gateway in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention, and inputting device configuration parameters according to the preset template.
Fig. 3 is a flowchart of acquiring the device configuration parameters, determining whether a generic model exists, and performing information processing of the generic model by using an encryption algorithm after acquiring the generic model in the model distribution application design method based on the edge computing gateway according to an embodiment of the present invention.
Fig. 4 is a flowchart of obtaining the device configuration parameters, determining whether a specific model exists, and performing information processing on the specific model by using the encryption algorithm after obtaining the specific model in the model distribution application design method based on the edge computing gateway according to an embodiment of the present invention.
Fig. 5 is a flowchart of determining whether a device active model acquisition request exists in the edge computing gateway service device in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention, and performing deployment update of the device computing model by using the edge device.
Fig. 6 is a flowchart of obtaining model configuration parameters, determining an application scenario of a device, and performing parameter adjustment on the target model in a model distribution application design method based on an edge computing gateway according to an embodiment of the present invention.
Fig. 7 is a flowchart of determining whether an abnormal state exists in the edge computing gateway service device and feeding back the obtained abnormal state to the cloud management service device in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention.
Fig. 8 is a block diagram of a model distribution application design system based on an edge computing gateway in accordance with an embodiment of the present invention.
Fig. 9 is a block diagram of an electronic device in one embodiment of the invention.
Detailed Description
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the rapid development of the internet of things, the interconnection of everything becomes a trend of future development, and the embedded node equipment will show a great increase. The data interaction is carried out between the traditional cloud-side data center and thousands of nodes of the Internet of things, so that the computing resource cost is huge, network congestion exists, and the data interaction is more obvious in peak hours. Although 5G wireless mobile communication with high bandwidth is gradually popularized, for some emerging application scenarios, such as: unmanned, safety monitoring, roadside perception data and the like need low delay, are highly reliable, and traditional node to high in the clouds can't satisfy the demand of these application services. Therefore, the novel edge terminal AI calculation is explored in all the industries at present, and the method has a vital influence on the safety and performance improvement of the Internet of things system. Since the emerging application service needs to customize a specific scene model, or to deploy a general scene model in a large scale, and perform configuration such as parameter quantization, a specific method is urgently needed to solve the problem of difficult frequent replacement and deployment.
Before the technology of the present invention, in the prior art, for model deployment of edge computing gateway devices, a manual deployment scheme is still adopted, and the following problems exist:
1) the operation requirement on workers is high. Because the equipment is deployed on the site, and the model of the equipment is deployed in a physical connection mode, sometimes, the operation is inconvenient because the position environment for installing the edge computing gateway is too severe, and extremely strict use requirements are provided for the site support.
2) High operation and maintenance cost and low efficiency. The upper computer terminal equipment is required to be close to the edge terminal equipment geographically so as to be connected physically, and extremely strict use requirements are provided for field support of the upper computer terminal equipment, so that specific training needs to be carried out on workers. And the staff can only carry out the operation and maintenance to an edge end equipment, need consume a large amount of human costs and time cost, and the operation and maintenance efficiency is extremely low.
3) Central data is difficult to aggregate statistics. Due to the fact that the deployment process of the edge computing gateway is complex, configurations such as parameter quantification and the like are needed, different application scenes are added, different models are needed to be distributed, and detailed information is difficult to count in a manual deployment mode and is fed back to the server in real time.
4) Error prone. Due to the complexity of the deployment environment and a large number of devices, deployment errors are easily caused, and problems of abnormal faults, difficulty in removal and the like are caused.
5) The safety is not high. Because on-site manual deployment is adopted, the upper computer is adopted for deployment, a calculation model data packet of the edge calculation gateway is needed, safety certification is lacked, and the condition of data packet leakage is easily caused.
The embodiment of the invention provides a model distribution application design method and system based on an edge computing gateway. According to the scheme, the model of the server is obtained in an instruction mode, the upper computer terminal device is physically connected with the edge terminal device, and the upper computer terminal device deploys the model on the edge terminal device.
According to a first aspect of the embodiments of the present invention, a model distribution application design method based on an edge computing gateway is provided.
Fig. 1 is a flowchart of a model distribution application design method based on an edge computing gateway according to an embodiment of the present invention.
As shown in fig. 1, in one or more embodiments, preferably, the method for model distribution application design based on edge computing gateway includes:
s101, selecting a preset template in a model distribution system of an edge computing gateway through a human-computer interaction interface according to the model number and the application scene of equipment, and inputting equipment configuration parameters according to the preset template;
s102, obtaining the equipment configuration parameters, judging whether a universal model exists or not, and after the universal model is obtained, performing information processing on the universal model by using an encryption algorithm;
s103, acquiring the equipment configuration parameters, judging whether a specific model exists or not, and after the specific model is acquired, performing information processing on the specific model by using the encryption algorithm;
s104, judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not, and carrying out deployment updating on an equipment computing model through the edge equipment;
s105, obtaining model configuration parameters, judging an application scene of equipment, and adjusting parameters of the target model;
s106, judging whether the edge computing gateway service equipment has an abnormal state or not, and feeding back the obtained abnormal state to the cloud management service equipment;
the model distribution system of the edge computing gateway comprises cloud management service equipment and edge computing gateway service equipment, wherein the cloud management service equipment comprises interaction management service equipment and processing management service equipment;
the encryption algorithm specifically adopts a symmetric encryption mode, and the specific flow is as follows:
issuing a model distribution request to the edge computing gateway service equipment at the cloud management service equipment;
the edge computing gateway service equipment randomly selects a compression mode and an encryption mode to generate a symmetric encryption key;
sending the symmetric encryption key to the processing management service equipment, analyzing a message, and encrypting a model to generate a target model;
and issuing the target model to the edge computing gateway service equipment.
In the embodiment of the invention, the edge computing gateway collects and summarizes information of nodes of the Internet of things, such as point cloud data of a laser radar, road side images of a camera, weather temperature and humidity and the like, as original data of a training model. A worker filters and screens data at a cloud end through a human-computer interaction interface, after training, the data are compiled to form a corresponding model according to the model number of the edge computing gateway and an application scene, corresponding parameters are configured and assigned to the corresponding edge computing gateway, and the problem that a general model can be distributed to the edge computing gateway in a large scale or the model can be assigned to a specific edge computing gateway to be difficult to deploy is solved. Meanwhile, when the model of the edge computing gateway is abnormal, the data can be transmitted back to the cloud, so that the recording and statistics are facilitated, and the problem that the troubleshooting is difficult during model distribution is solved. The management system for distributing the model is deployed at the cloud end, so that the model of the remote edge computing gateway is managed, frequently deployed and updated, the problem of complex operation and maintenance of the edge computing gateway is solved, and the operation cost is reduced.
Specifically, the encryption adopts a symmetric encryption mode, a message protocol in the transmission process comprises an encryption key, a compression method and an encryption type, the encryption type adopts common AES, DES and the like, the compression method zip, rar and the like, the server analyzes message data, obtains a corresponding value, encrypts and compresses the model firmware, then sends the encrypted and compressed value to the client, and finally the client decompresses, decrypts and deploys the model. Finally, the distribution process of the specific model of the universal model machine is realized.
Fig. 2 is a flowchart of selecting a preset template according to the device model and the application scenario through a human-computer interface in a model distribution system of an edge computing gateway in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention, and inputting device configuration parameters according to the preset template.
As shown in fig. 2, in one or more embodiments, preferably, selecting a preset template according to a device model and an application scenario in the model distribution system of the edge computing gateway through a human-computer interaction interface, and entering device configuration parameters according to the preset template, specifically includes:
s201, connecting the Internet of things equipment node with an edge computing gateway through one or more of R485, network and IO bus modes;
s202, setting equipment models and application scenes through a human-computer interaction interface;
s203, automatically obtaining corresponding equipment configuration parameters in a preset calculation model according to the equipment model and the application scene;
and S204, issuing the corresponding equipment configuration parameters through one or more of R485, network and IO bus modes.
In the embodiment of the invention, based on a cloud deployment model distribution technology, the deployment, operation and maintenance and technical iteration costs of a deep learning model are reduced.
The cloud end comprises the following services: the cloud terminal mainly comprises model distribution business and other business as auxiliary modules for realizing the model distribution.
Specifically, the OTA implements a technology for remotely managing data of the mobile terminal device and the SIM card through an air interface of mobile communication.
Specifically, the edge computing gateway is a gateway deployed at the edge side of the network, and is connected with the physical and digital worlds through functions such as network connection, protocol conversion and the like, so that light-weight connection management, real-time data analysis and application management functions are provided.
Specifically, different application scenarios have different computational models. The specific description is as follows: the method comprises the following steps of traffic flow analysis by using a camera, object identification and tracking by using a laser radar, regional weather prediction by using temperature and humidity sensor nodes, intelligent dynamic regulation and control of traffic lights, pedestrian identification by using the camera, red light running identification and other algorithms.
Fig. 3 is a flowchart of acquiring the device configuration parameters, determining whether a generic model exists, and performing information processing of the generic model by using an encryption algorithm after acquiring the generic model in the model distribution application design method based on the edge computing gateway according to an embodiment of the present invention.
As shown in fig. 3, in one or more embodiments, preferably, the obtaining the device configuration parameters, determining whether a generic model exists, and after obtaining the generic model, performing information processing on the generic model by using an encryption algorithm:
s301, sending a universal scene model request by using the edge computing gateway service equipment;
s302, encrypting and reporting general scene model information to the processing management service equipment by using the encryption algorithm according to the general scene model request, wherein the general scene model information comprises equipment model, performance, compiling chain, application scene type and application scene adaptability corresponding to a general scene;
s303, reporting the general scene model information to the interactive management service equipment by using the processing management service equipment;
s304, retrieving all the general target models from a model database according to the equipment models, the application scenes and the compiling chains corresponding to the general scenes;
s305, the general target model is sent to the edge computing gateway management service equipment through the processing management service equipment.
In the embodiment of the invention, after the device is initialized successfully, the information of the general scene model is registered and reported (basic information such as the model number, the performance, the compiling chain, the type of the application scene, the applicability of the application scene and the like), the management service carries out protocol analysis on the configured model parameters and types, a certain application scene of the device is identified to be the general type, a large number of models are prestored in the cloud server, a proper light-weight library is selected mainly according to the performance of the device through a database matching mode, the scene is applied, the compiling parameters are generated, and then the compiling is carried out selectively through the compiling chain. And if the compiling error occurs, the staff can process the early warning after the early warning. The scheme is based on an automatic distribution mode, the cloud stores information, the problem of frequently deployed software updating records is solved, meanwhile, an abnormal information reporting mode is adopted, fault analysis is facilitated, and flexibility and high expansibility of the pull scheme application are achieved.
Fig. 4 is a flowchart of obtaining the device configuration parameters, determining whether a specific model exists, and performing information processing on the specific model by using the encryption algorithm after obtaining the specific model in the model distribution application design method based on the edge computing gateway according to an embodiment of the present invention.
As shown in fig. 4, in one or more embodiments, preferably, the obtaining the device configuration parameters, determining whether a specific model exists, and after obtaining the specific model, performing information processing on the specific model by using the encryption algorithm specifically includes:
s401, acquiring a specific scene model request, and reporting specific scene model information by the edge computing gateway service equipment by using the encryption algorithm, wherein the specific scene model information comprises equipment model, performance, a compiling chain, an application scene type and application scene adaptability corresponding to a specific scene;
s402, replying a first reply request to the edge computing gateway service equipment through the processing management service equipment;
s403, after receiving the first reply request, the edge computing gateway service device returns the specific scene model information to the processing management service device, and further transmits the specific scene model information to the interaction management service device;
s404, screening, marking and selecting a specific target model for data according to a specific scene through the interactive management service equipment;
s405, the interaction management service device feeds back a second reply request to the edge computing gateway service device through the processing management service device.
In the embodiment of the invention, the process of equipment side registration is the same as a general model, information collected by the equipment side, such as images and point clouds, is uploaded to a server, and a worker conducts screening, marking and model selection training on data according to a specific scene to form a model of the specific scene. The distribution process of the model relates to a safe encryption mode, the encryption adopts a symmetric encryption mode, a message protocol in the transmission process comprises an encryption key, a compression method and an encryption type, the encryption type adopts common AES, DES and the like, the compression method zip, rar and the like, a server analyzes message data, obtains a corresponding value, encrypts and compresses the model firmware, then sends the encrypted and compressed message data to a client, and finally the client decompresses, decrypts and deploys the model. The scheme has a safety certification function, and can better protect intellectual property and prevent information leakage.
Fig. 5 is a flowchart of determining whether a device active model acquisition request exists in the edge computing gateway service device in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention, and performing deployment update of the device computing model by using the edge device.
As shown in fig. 5, in one or more embodiments, preferably, the determining whether the edge computing gateway service device has a device active model acquisition request, and performing deployment update of a device computing model through the edge device specifically includes:
s501, judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not;
s502, acquiring a model active acquisition request of the equipment, and uploading a corresponding equipment parameter value to the cloud management service equipment;
s503, obtaining a corresponding model according to a preset model base and the corresponding equipment parameter value, further packaging, and generating an active acquisition model through an encryption algorithm;
and S504, obtaining the active acquisition model and sending the active acquisition model to the edge computing gateway service equipment.
In the embodiment of the invention, the visual operation interface is provided, and the operability and maintainability are easy.
Fig. 6 is a flowchart of obtaining model configuration parameters, determining an application scenario of a device, and performing parameter adjustment on the target model in a model distribution application design method based on an edge computing gateway according to an embodiment of the present invention.
As shown in fig. 6, in one or more embodiments, preferably, the obtaining of the model configuration parameters, determining an application scenario of a device, and performing parameter adjustment on the target model specifically include:
s601, obtaining the model configuration parameters in the interactive management service equipment;
s602, issuing model parameters to the corresponding edge computing gateway service equipment through the processing management service equipment according to the model configuration parameters;
s603, modifying the target model according to the model configuration parameters.
In the embodiment of the invention, a worker configures the parameters of the specified equipment through a human-computer interaction interface, the management service model generates configuration information according to the parameters and sends the configuration information to the specified equipment through a network, and the edge computing gateway modifies the configuration information according to the parameters of the sent model and is used for identifying the corresponding application scene. Finally, based on a cloud deployment model mode and an edge gateway information interaction mode, the functions of large-scale deployment and specific deployment can be performed on universalization.
Fig. 7 is a flowchart of determining whether an abnormal state exists in the edge computing gateway service device and feeding back the obtained abnormal state to the cloud management service device in the edge computing gateway-based model distribution application design method according to an embodiment of the present invention.
As shown in fig. 7, in one or more embodiments, preferably, the determining whether the edge computing gateway service device has an abnormal state, and feeding back the obtained abnormal state to the cloud management service device includes:
s701, uploading the parameters and abnormal data of the equipment to the processing management service equipment according to the edge computing gateway service equipment;
s702, the processing management service equipment feeds back an abnormal condition to the interactive management service equipment;
s703, in the interactive management service equipment, a worker analyzes according to condition feedback and adjusts the target model;
s704, the target model is issued to the edge computing gateway service equipment again.
In the embodiment of the invention, a worker analyzes according to an abnormal condition through a human-computer interaction interface, reconfigures and appoints equipment parameters, a management service model regenerates a model according to the parameters, the model is issued to appointed equipment through a network, and an edge computing gateway deploys according to the issued model, so that the problem of abnormity of different applications is solved.
According to a second aspect of the embodiments of the present invention, a model distribution application design system based on an edge computing gateway is provided.
Fig. 8 is a block diagram of a model distribution application design system based on an edge computing gateway in accordance with an embodiment of the present invention.
In one or more embodiments, as shown in fig. 8, preferably, the model distribution application design system based on the edge computing gateway includes:
the device configuration module 801 is used for selecting a preset template according to the device model and the application scene through a human-computer interaction interface in the model distribution system of the edge computing gateway, and inputting device configuration parameters according to the preset template;
a general model information module 802, configured to obtain the device configuration parameters, determine whether a general model exists, and perform information processing on the general model by using an encryption algorithm after the general model is obtained;
a specific model information module 803, configured to obtain the device configuration parameter, determine whether a specific model exists, and perform information processing on the specific model by using the encryption algorithm after the specific model is obtained;
an active model update module 804, configured to determine whether the edge computing gateway service device has a device active model acquisition request, and perform deployment update of a device computing model through the edge device;
a passive model updating module 805, configured to obtain model configuration parameters, determine an application scenario of the device, and perform parameter adjustment on the target model;
the abnormal state analysis module 806 is configured to determine whether the edge computing gateway service device has an abnormal state, and feed back the obtained abnormal state to the cloud management service device.
In the embodiment of the invention, a general model and a specific model are deployed, a device actively acquires the model, and a direct parameter issuing modification model, exception handling and the like are configured. The unified management edge gateway of the unified cloud center is realized, manual intervention is reduced, automatic data monitoring is realized, and abnormal faults are eliminated. According to different actual scene requirements, the model is remotely deployed, issued and upgraded in a targeted manner, so that the flexibility and the application range of the edge calculation at the end side are improved, different application scenes are adapted, and different calculation model functions are realized; the deployment of a model for the edge computing gateway on site is not needed, the manual maintenance is reduced, and the cost and the expense are greatly reduced. On the other hand, the mode that the cloud end directly reaches the equipment is adopted, and the problem of safe leakage of the data packet is greatly avoided through the mode of safe encryption authentication.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
According to a fourth aspect of the embodiments of the present invention, there is provided an electronic apparatus. Fig. 9 is a block diagram of an electronic device in one embodiment of the invention. The electronic device shown in fig. 9 is a generic edge computing gateway model distribution apparatus comprising a generic computer hardware structure comprising at least a processor 901 and a memory 902. The processor 901 and the memory 902 are connected by a bus 903. The memory 902 is adapted to store instructions or programs executable by the processor 901. Processor 901 may be a stand-alone microprocessor or a collection of one or more microprocessors. Thus, the processor 901 implements the processing of data and the control of other devices by executing instructions stored by the memory 902 to perform the method flows of embodiments of the present invention as described above. The bus 903 connects the above components together, as well as to the display controller 904 and display devices and input/output (I/O) devices 905. Input/output (I/O) devices 905 may be a mouse, keyboard, modem, network interface, touch input device, motion-sensing input device, printer, and other devices known in the art. Typically, the input/output devices 905 are connected to the system through an input/output (I/O) controller 906.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
1) the scheme of the invention has the characteristic of high automation degree, particularly realizes the unified management of a unified cloud center, reduces manual intervention, realizes automatic data monitoring and eliminates abnormal faults;
2) the scheme of the invention has the characteristics of high flexibility and strong expansibility of equipment, specifically, the model can be remotely deployed, issued and upgraded in a targeted manner according to different actual scene requirements, so that the flexibility and the application range of the edge calculation at the end side are improved, and different application scenes can be adapted to different hardware peripherals according to the function, so that different functions are realized;
3) the scheme of the invention has the characteristics of convenient operation and maintenance and low cost, particularly facilitates the upgrade and maintenance of software, does not need to carry out model deployment on the edge computing gateway on site, reduces the manual maintenance, and particularly realizes large-scale general model deployment through cloud service when the number of the edge computing gateways reaches a certain magnitude, thereby reducing the cost and expense;
4) the scheme of the invention has the characteristic of high safety, and particularly, compared with the field deployment of an upper computer, the method of directly adopting a cloud end-to-edge gateway mode is adopted, so that the data packet leakage condition is prevented to a great extent, and the information safety problem is solved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A model distribution application design method based on an edge computing gateway is characterized by specifically comprising the following steps:
selecting a preset template in a model distribution system of an edge computing gateway through a human-computer interaction interface according to the equipment model and an application scene, and inputting equipment configuration parameters according to the preset template;
acquiring the equipment configuration parameters, judging whether a universal model exists or not, and processing the information of the universal model by using an encryption algorithm after the universal model is acquired;
acquiring the equipment configuration parameters, judging whether a specific model exists or not, and processing information of the specific model by using the encryption algorithm after the specific model is acquired;
judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not, and carrying out deployment updating on an equipment computing model through the edge equipment;
obtaining model configuration parameters, judging an application scene of equipment, and performing parameter adjustment on the target model;
judging whether the edge computing gateway service equipment has an abnormal state or not, and feeding back the obtained abnormal state to cloud management service equipment;
the model distribution system of the edge computing gateway comprises cloud management service equipment and edge computing gateway service equipment, wherein the cloud management service equipment comprises interaction management service equipment and processing management service equipment;
the encryption algorithm specifically adopts a symmetric encryption mode, and the specific flow is as follows:
issuing a model distribution request to the edge computing gateway service equipment at the cloud management service equipment;
the edge computing gateway service equipment randomly selects a compression mode and an encryption mode to generate a symmetric encryption key;
sending the symmetric encryption key to the processing management service equipment, analyzing a message, and encrypting a model to generate a target model;
and issuing the target model to the edge computing gateway service equipment.
2. The method according to claim 1, wherein the model distribution application design method based on the edge computing gateway selects a preset template according to the device model and the application scene through a human-computer interface in the model distribution system of the edge computing gateway, and inputs device configuration parameters according to the preset template, and specifically comprises:
connecting the Internet of things equipment node with the edge computing gateway through one or more of R485, network and IO bus modes;
setting equipment models and application scenes through a human-computer interaction interface;
automatically obtaining corresponding equipment configuration parameters in a preset calculation model according to the equipment model and the application scene;
and issuing the corresponding equipment configuration parameters through one or more of R485, network and IO bus modes.
3. The method according to claim 1, wherein the obtaining of the device configuration parameters, the determining of whether the generic model exists, and after obtaining the generic model, performing information processing of the generic model using an encryption algorithm:
sending a universal scene model request by utilizing the edge computing gateway service equipment;
encrypting and reporting general scene model information to the processing management service equipment by using the encryption algorithm according to the general scene model request, wherein the general scene model information comprises equipment model, performance, a compiling chain, an application scene type and application scene adaptability corresponding to a general scene;
reporting the general scene model information to the interactive management service equipment by using the processing management service equipment;
retrieving all the general target models from a model database according to the equipment models, the application scenes and the compiling chains corresponding to the general scenes;
and sending the general target model to edge computing gateway management service equipment through the processing management service equipment.
4. The method according to claim 1, wherein the obtaining of the device configuration parameters, the determining of whether a specific model exists, and after obtaining the specific model, performing information processing on the specific model by using the encryption algorithm specifically includes:
acquiring a specific scene model request, and reporting specific scene model information by the edge computing gateway service equipment by using the encryption algorithm, wherein the specific scene model information comprises equipment model, performance, compiling chain, application scene type and application scene adaptability corresponding to a specific scene;
replying a first reply request to the edge computing gateway service equipment through the processing management service equipment;
after the edge computing gateway service equipment receives the first reply request, the specific scene model information is transmitted back to the processing management service equipment and then transmitted to the interaction management service equipment;
screening, marking and selecting a specific target model for data according to a specific scene through the interactive management service equipment;
and the interactive management service equipment feeds back a second reply request to the edge computing gateway service equipment through the processing management service equipment.
5. The method according to claim 1, wherein the determining whether the edge computing gateway service device has a device active model acquisition request includes:
judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not;
acquiring a model request actively acquired by the equipment, and uploading a corresponding equipment parameter value to the cloud management service equipment;
obtaining a corresponding model according to a preset model library and the corresponding equipment parameter values, further packaging, and generating an active acquisition model through an encryption algorithm;
and obtaining the active acquisition model and issuing the active acquisition model to the edge computing gateway service equipment.
6. The method according to claim 1, wherein the obtaining of the model configuration parameters, the determining of the application scenario of the device, and the parameter adjustment of the target model specifically comprise:
obtaining the model configuration parameters in the interaction management service equipment;
issuing model parameters to the corresponding edge computing gateway service equipment through the processing management service equipment according to the model configuration parameters;
and modifying the target model according to the model configuration parameters.
7. The method according to claim 1, wherein the determining whether the edge computing gateway service device has an abnormal state and feeding back the obtained abnormal state to a cloud management service device specifically includes:
uploading the parameters and abnormal data of the equipment to the processing management service equipment according to the edge computing gateway service equipment;
the processing management service equipment feeds back the abnormal condition to the interactive management service equipment;
in the interactive management service equipment, a worker analyzes according to condition feedback and adjusts the target model;
and re-issuing the target model to the edge computing gateway service equipment.
8. A model distribution application design system based on an edge computing gateway, the system comprising:
the device configuration module is used for selecting a preset template according to the device model and the application scene through a human-computer interaction interface in a model distribution system of the edge computing gateway, and inputting device configuration parameters according to the preset template;
the universal model information module is used for acquiring the equipment configuration parameters, judging whether a universal model exists or not, and processing the information of the universal model by using an encryption algorithm after the universal model is acquired;
the specific model information module is used for acquiring the equipment configuration parameters, judging whether a specific model exists or not, and processing the information of the specific model by using the encryption algorithm after the specific model is acquired;
the active model updating module is used for judging whether the edge computing gateway service equipment has an equipment active model acquisition request or not and carrying out deployment updating on the equipment computing model through the edge equipment;
the passive model updating module is used for acquiring model configuration parameters, judging an application scene of equipment and adjusting parameters of the target model;
and the abnormal state analysis module is used for judging whether the edge computing gateway service equipment has an abnormal state or not and feeding back the obtained abnormal state to the cloud management service equipment.
9. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-8.
10. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the steps of any of claims 1-8.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111332194.1A CN114338281B (en) | 2021-11-11 | 2021-11-11 | Model distribution application design method and system based on edge computing gateway |
| PCT/CN2022/094696 WO2023082596A1 (en) | 2021-11-11 | 2022-05-24 | Edge computing gateway-based model distribution application design method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111332194.1A CN114338281B (en) | 2021-11-11 | 2021-11-11 | Model distribution application design method and system based on edge computing gateway |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114338281A true CN114338281A (en) | 2022-04-12 |
| CN114338281B CN114338281B (en) | 2023-04-07 |
Family
ID=81045414
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111332194.1A Active CN114338281B (en) | 2021-11-11 | 2021-11-11 | Model distribution application design method and system based on edge computing gateway |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114338281B (en) |
| WO (1) | WO2023082596A1 (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114884960A (en) * | 2022-04-13 | 2022-08-09 | 北京科技大学 | Edge computing gateway system with heterogeneous equipment interaction function |
| CN114979139A (en) * | 2022-05-05 | 2022-08-30 | 苏州思萃工业互联网技术研究所有限公司 | Management system and method of heterogeneous virtual gateway in edge computing scene |
| CN114978738A (en) * | 2022-06-01 | 2022-08-30 | 中国银行股份有限公司 | Encryption processing method of interface message, client and server |
| CN114997062A (en) * | 2022-06-15 | 2022-09-02 | 同济大学 | Industrial water pump fault diagnosis light-weight method and system based on edge calculation |
| CN115776680A (en) * | 2022-11-30 | 2023-03-10 | 西部科学城智能网联汽车创新中心(重庆)有限公司 | Dynamic deployment method and device of computing model suitable for edge computing equipment |
| WO2023082596A1 (en) * | 2021-11-11 | 2023-05-19 | 广州软件应用技术研究院 | Edge computing gateway-based model distribution application design method and system |
| CN116911406A (en) * | 2023-07-05 | 2023-10-20 | 上海数禾信息科技有限公司 | Wind control model deployment method and device, computer equipment and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117176513B (en) * | 2023-10-31 | 2024-01-12 | 湖南承希科技有限公司 | Internet of things data acquisition gateway equipment based on 5G-R technology |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111199279A (en) * | 2019-10-30 | 2020-05-26 | 山东浪潮人工智能研究院有限公司 | Cloud edge calculation and artificial intelligence fusion method and device for police service industry |
| US20200327371A1 (en) * | 2019-04-09 | 2020-10-15 | FogHorn Systems, Inc. | Intelligent Edge Computing Platform with Machine Learning Capability |
| CN111901173A (en) * | 2020-07-30 | 2020-11-06 | 山东超越数控电子股份有限公司 | Cloud edge cooperative system construction method, device, equipment and readable medium |
| CN112016465A (en) * | 2020-08-28 | 2020-12-01 | 北京百度网讯科技有限公司 | Scene recognition method, device and system |
| CN112995287A (en) * | 2021-02-04 | 2021-06-18 | 中国科学院计算技术研究所 | Keyword detection task scheduling method facing edge calculation |
| CN113112086A (en) * | 2021-04-22 | 2021-07-13 | 北京邮电大学 | Intelligent production system based on edge calculation and identification analysis |
| CN113329358A (en) * | 2021-04-29 | 2021-08-31 | 广州中国科学院软件应用技术研究所 | RISC-V instruction set-based AIOT multi-system edge gateway communication system and equipment |
| WO2021216637A1 (en) * | 2020-04-22 | 2021-10-28 | University Of Florida Research Foundation, Incorporated | Cloud-based framework for processing, analyzing, and visualizing imaging data |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11082258B1 (en) * | 2020-01-14 | 2021-08-03 | Cisco Technology, Inc. | Isolation and segmentation in multi-cloud interconnects |
| CN111416845B (en) * | 2020-03-12 | 2023-09-26 | 腾讯云计算（北京）有限责任公司 | Method, device and storage medium for configuring edge equipment |
| CN111752556B (en) * | 2020-06-28 | 2021-04-30 | 自然资源部信息中心 | Data prediction system and method based on double-layer model structure |
| CN112180897B (en) * | 2020-09-25 | 2021-12-17 | 国网湖南省电力有限公司 | Relay protection/measurement and control device automatic test system and method based on universal test template |
| CN113381511B (en) * | 2021-06-02 | 2022-12-27 | 深圳市莱达四维信息科技有限公司 | Online intelligent inspection system of transformer substation |
| CN113487156A (en) * | 2021-06-30 | 2021-10-08 | 同济大学 | Worker behavior monitoring and identifying method and device based on cloud-edge architecture |
| CN114338281B (en) * | 2021-11-11 | 2023-04-07 | 广州软件应用技术研究院 | Model distribution application design method and system based on edge computing gateway |
-
2021
- 2021-11-11 CN CN202111332194.1A patent/CN114338281B/en active Active
-
2022
- 2022-05-24 WO PCT/CN2022/094696 patent/WO2023082596A1/en unknown
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200327371A1 (en) * | 2019-04-09 | 2020-10-15 | FogHorn Systems, Inc. | Intelligent Edge Computing Platform with Machine Learning Capability |
| CN111199279A (en) * | 2019-10-30 | 2020-05-26 | 山东浪潮人工智能研究院有限公司 | Cloud edge calculation and artificial intelligence fusion method and device for police service industry |
| WO2021216637A1 (en) * | 2020-04-22 | 2021-10-28 | University Of Florida Research Foundation, Incorporated | Cloud-based framework for processing, analyzing, and visualizing imaging data |
| CN111901173A (en) * | 2020-07-30 | 2020-11-06 | 山东超越数控电子股份有限公司 | Cloud edge cooperative system construction method, device, equipment and readable medium |
| CN112016465A (en) * | 2020-08-28 | 2020-12-01 | 北京百度网讯科技有限公司 | Scene recognition method, device and system |
| CN112995287A (en) * | 2021-02-04 | 2021-06-18 | 中国科学院计算技术研究所 | Keyword detection task scheduling method facing edge calculation |
| CN113112086A (en) * | 2021-04-22 | 2021-07-13 | 北京邮电大学 | Intelligent production system based on edge calculation and identification analysis |
| CN113329358A (en) * | 2021-04-29 | 2021-08-31 | 广州中国科学院软件应用技术研究所 | RISC-V instruction set-based AIOT multi-system edge gateway communication system and equipment |
Non-Patent Citations (3)
| Title |
|---|
| HAITHAM H等: "Dynamic Traffic Model With Optimal Gateways Placement in IP Cloud Heterogeneous CRAN", 《IEEE ACCESS》 * |
| 杨绍光等: "云边协同关键技术及应用场景研究", 《信息通信技术》 * |
| 邹萍等: "面向边缘计算的制造资源感知接入与智能网关技术研究", 《计算机集成制造系统》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023082596A1 (en) * | 2021-11-11 | 2023-05-19 | 广州软件应用技术研究院 | Edge computing gateway-based model distribution application design method and system |
| CN114884960A (en) * | 2022-04-13 | 2022-08-09 | 北京科技大学 | Edge computing gateway system with heterogeneous equipment interaction function |
| CN114884960B (en) * | 2022-04-13 | 2023-05-19 | 北京科技大学 | Edge computing gateway system with heterogeneous equipment interaction function |
| CN114979139A (en) * | 2022-05-05 | 2022-08-30 | 苏州思萃工业互联网技术研究所有限公司 | Management system and method of heterogeneous virtual gateway in edge computing scene |
| CN114979139B (en) * | 2022-05-05 | 2024-05-28 | 苏州思萃工业互联网技术研究所有限公司 | Management system and method for heterogeneous virtual gateway in edge computing scene |
| CN114978738A (en) * | 2022-06-01 | 2022-08-30 | 中国银行股份有限公司 | Encryption processing method of interface message, client and server |
| CN114978738B (en) * | 2022-06-01 | 2024-04-16 | 中国银行股份有限公司 | Encryption processing method of interface message, client and server |
| CN114997062A (en) * | 2022-06-15 | 2022-09-02 | 同济大学 | Industrial water pump fault diagnosis light-weight method and system based on edge calculation |
| CN115776680A (en) * | 2022-11-30 | 2023-03-10 | 西部科学城智能网联汽车创新中心(重庆)有限公司 | Dynamic deployment method and device of computing model suitable for edge computing equipment |
| CN116911406A (en) * | 2023-07-05 | 2023-10-20 | 上海数禾信息科技有限公司 | Wind control model deployment method and device, computer equipment and storage medium |
| CN116911406B (en) * | 2023-07-05 | 2024-02-02 | 上海数禾信息科技有限公司 | Wind control model deployment method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023082596A1 (en) | 2023-05-19 |
| CN114338281B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114338281B (en) | Model distribution application design method and system based on edge computing gateway | |
| CA2824337C (en) | System and method for modeling and optimizing the performance of transportation networks | |
| US20180083982A1 (en) | Secure command execution from a cloud monitoring system to a remote cloud agent | |
| JP2018055665A (en) | Software definition realization method based on protection control system of smart substation | |
| GB2606628A (en) | Centralized knowledge repository and data mining system | |
| JP2018106686A5 (en) | ||
| CN111314339B (en) | Data transmission method and device | |
| CN105487500A (en) | Streaming data for analytics in process control systems | |
| CN101286954A (en) | Remote diagnostic system for robots | |
| CN111611054B (en) | Application deployment method and device | |
| Nayak et al. | A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications | |
| CN115576289A (en) | Reconfigurable high-fidelity large-scale industrial internet simulation platform based on virtualization technology | |
| CN117596755B (en) | Intelligent control method and system for street lamp of Internet of things | |
| CN116112175A (en) | Service processing method, device and medium of digital twin network based on block chain | |
| KR102476392B1 (en) | Smart factory operation analysis system and method | |
| CN114124956A (en) | Block chain edge calculation system | |
| CN102238571B (en) | Apparatus of Internet of Things machine to machine/man (M2M) business processing, system and method thereof | |
| CN117519005A (en) | Workshop equipment data acquisition system based on MES | |
| CN117632478A (en) | Method, system and device for fusing distributed edge computing and cloud computing | |
| KR102330347B1 (en) | System for Operating Smart Factory based on Block Chain | |
| CN112769620A (en) | Network deployment method, equipment and computer readable storage medium | |
| CN103389690B (en) | Supervisory system, Monitor And Control Subsystem, monitor node equipment, control center's equipment | |
| CN115085921B (en) | Model training method, node detection method, device, equipment and medium | |
| CN107888438B (en) | Flow table technology-based system for automatically sensing and adapting to cloud environment change | |
| CN105208117B (en) | A kind of ADC centralized management and data analysis system and its method based on cloud service |
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 |