CN112380428B - Cloud-edge coordinated edge computing method, system, electronic device and storage medium - Google Patents
Cloud-edge coordinated edge computing method, system, electronic device and storage medium Download PDFInfo
- Publication number
- CN112380428B CN112380428B CN202011179190.XA CN202011179190A CN112380428B CN 112380428 B CN112380428 B CN 112380428B CN 202011179190 A CN202011179190 A CN 202011179190A CN 112380428 B CN112380428 B CN 112380428B
- Authority
- CN
- China
- Prior art keywords
- edge
- cloud
- data
- gateway
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer And Data Communications (AREA)
Abstract
The application relates to a cloud-edge coordinated edge computing method, a cloud-edge coordinated edge computing system, an electronic device and a storage medium, wherein the cloud-edge coordinated edge computing method comprises the following steps: acquiring data acquired by a device sensor, and inputting the data into an edge calculation model; filtering and processing the data at the local gateway through the edge calculation model, and sending the processed result to the cloud; the cloud sends a command to the gateway, and data output object model parameters are converted into binary streams through the edge calculation model; the gateway sends the binary stream to the device sensor for control. According to the method and the device, the problem that the edge computing system of the Internet of things lacks a custom general edge computing framework which is unified and centrally managed, so that development is repeated and complex, the cost is high and the reusability is poor is solved, the data processing efficiency is improved, the cloud load is reduced, faster response is provided for users, and the development cost can be reduced.
Description
Technical Field
The application relates to the field of internet of things, in particular to a cloud-edge coordinated edge computing method, a cloud-edge coordinated edge computing system, an electronic device and a storage medium.
Background
Along with the rapid development of information technology, the internet of things technology is widely applied, and edge computing occurs along with the development of the internet of things. The edge computing refers to providing services for large-scale computing equipment and small-scale computing equipment nearby by adopting an open platform integrating network, computing, storage and application core capabilities on one side close to an object or data source. For the Internet of things, the edge computing technology breaks through, meaning that data analysis and control can be realized through local equipment without delivering cloud processing, the data processing efficiency is greatly improved, the cloud load is lightened, faster response is provided for users, and the basic requirements of the Internet of things industry in the aspects of real-time service, application intelligence, safety, privacy protection and the like are met.
In the related art, the implementation of the conventional edge computing gateway is in an underlying open Linux system, and a user is required to perform coding according to needs to implement data analysis and code debugging, or develop specific dedicated edge devices according to specific scenes, however, these do not provide a customizable general edge computing framework for unified and centralized management.
At present, aiming at the problems of repeated and complex development, high cost and poor reusability caused by the lack of a custom general edge computing framework which is uniformly and intensively managed by an edge computing system of the Internet of things in the related technology, an effective solution is not proposed.
Disclosure of Invention
The embodiment of the application provides a cloud-edge coordinated edge computing method, a cloud-edge coordinated edge computing system, an electronic device and a storage medium, which at least solve the problems of repeated and complex development, high cost and poor reusability caused by the lack of a custom general edge computing framework which is uniformly and intensively managed by an edge computing system of the Internet of things in the related technology.
In a first aspect, an embodiment of the present application provides a method for edge computation with cloud edge coordination, where the method includes:
acquiring data acquired by a device sensor, and inputting the data into an edge calculation model;
filtering and processing the data at a local gateway through the edge calculation model, and sending the processed result to a cloud;
the cloud sends a command to a gateway, and data output object model parameters are converted into binary streams through the edge calculation model;
the gateway sends the binary stream to the device sensor for control.
In some of these embodiments, before the inputting the data into the edge computation model, the method includes:
and the user self-defines the edge calculation model through a scripting language at the cloud, takes the data acquired by the equipment sensor as an input source, and takes a self-defined calculation result as a data output source of the edge calculation model.
In some of these embodiments, after the user completes the edge computing model custom settings, the method includes:
and distributing and configuring the calculation model to the gateway, and locally filtering and processing the data acquired by the equipment sensor by the gateway.
In some embodiments, the sending the result of the processing to the cloud comprises:
and sending the result to the cloud according to the defined data output object model.
In a second aspect, an embodiment of the present application provides a system for edge computing with cloud edge coordination, where the system includes: the device sensor, the gateway and the cloud;
the input module is used for acquiring data acquired by the equipment sensor and inputting the data into an edge calculation model;
the output module is used for filtering and processing the data at a local gateway through the edge calculation model and sending the processing result to the cloud;
the sending module is used for sending a command to the gateway by the cloud, and converting the data output object model parameters into binary streams by the edge calculation model;
and the control module is used for sending the binary stream to the equipment sensor by the gateway for control.
In some of these embodiments, before the inputting the data into the edge computation model, the system comprises:
and the user self-defines the edge calculation model through a scripting language at the cloud, takes the data acquired by the equipment sensor as an input source, and takes a self-defined calculation result as a data output source of the edge calculation model.
In some of these embodiments, after the user completes the edge computation model custom settings, the system includes:
and the input module distributes and configures the calculation model to the gateway, and the gateway locally filters and processes the data acquired by the equipment sensor.
In some embodiments, the sending the result of the processing to the cloud comprises:
and the output module sends the result to the cloud according to the defined data output object model.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, and a processor, where the memory stores a computer program, and the processor is configured to run the computer program to perform the method of cloud-edge coordinated edge calculation described in any one of the above.
In a fourth aspect, embodiments of the present application provide a storage medium having a computer program stored therein, where the computer program is configured to perform, when executed, a method of cloud-edge coordinated edge computation as described in any one of the above.
Compared with the related art, the cloud-edge coordinated edge computing method provided by the embodiment of the application acquires the data acquired by the equipment sensor and inputs the data into the edge computing model; filtering and processing the data at the local gateway through the edge calculation model, and sending the processed result to the cloud; the cloud sends a command to the gateway, and data output object model parameters are converted into binary streams through the edge calculation model; the gateway sends the binary stream to the equipment sensor for control, thereby solving the problems of repeated and complex development, high cost and poor reusability caused by the lack of a custom general edge computing framework for unified and centralized management of an edge computing system of the Internet of things, improving the data processing efficiency, relieving the cloud load, providing faster response for users and reducing the development cost.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic view of an application environment of a method of edge computing with cloud-edge coordination according to an embodiment of the present application;
FIG. 2 is a flow chart of a cloud-edge coordinated edge computing method according to an embodiment of the present application;
fig. 3 is a schematic diagram of processing and converting uplink collected data of a device according to an embodiment of the present application;
fig. 4 is a schematic diagram of data conversion of cloud command output control according to an embodiment of the present application;
FIG. 5 is a data transformation diagram of a user-defined edge computation model according to an embodiment of the present application;
FIG. 6 is an example schematic diagram of an edge computation model for intelligent open testing according to an embodiment of the present application;
FIG. 7 is a block diagram of a cloud-edge coordinated edge computing system according to an embodiment of the present application;
fig. 8 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means greater than or equal to two. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The cloud-edge coordinated edge computing method provided by the application can be applied to an application environment as shown in fig. 1, and fig. 1 is an application environment schematic diagram of the cloud-edge coordinated edge computing method according to an embodiment of the application, as shown in fig. 1, where a system of the application environment includes a device sensor 10, a gateway 11 and a cloud 12, and a specific implementation method is as follows: acquiring data acquired by the equipment sensor 10, inputting the data into an edge calculation model, filtering and processing the data at the local gateway 11 through the edge calculation model, and transmitting the processed result to the cloud 12 to realize the uplink acquisition of the equipment data; the cloud 12 sends a command to the gateway 11, converts data output object model parameters into binary streams through an edge computing model, and then the gateway 11 sends the binary streams to the device sensor 10 for control, so that downlink device control is realized, the problems of repeated and complex development, high cost and poor reusability caused by the lack of a custom general edge computing framework which is uniformly and intensively managed by an edge computing system of the Internet of things are solved, the data processing efficiency is improved, the cloud load is lightened, faster response is provided for users, and the development cost can be reduced.
The embodiment provides a cloud-edge coordinated edge computing method, and fig. 2 is a flowchart of the cloud-edge coordinated edge computing method according to an embodiment of the application, as shown in fig. 2, where the flowchart includes the following steps:
in step S201, the data collected by the device sensor 10 is acquired and input into an edge calculation model, where the edge calculation is a technology developed under the background of high bandwidth, time sensitivity and integration of the internet of things, and it refers to providing a nearest service by adopting an open platform with integrated network, calculation, storage and application core capabilities on one side of the object or data source, and the edge calculation is located between a physical entity and an industrial connection or at the top of the physical entity, where the physical entity is mounted with an edge calculation platform with integrated network, calculation, storage and application core capabilities, so as to provide real-time, dynamic and intelligent service calculation for the end user. Optionally, in this embodiment, binary stream raw data, for example, '010310012a021B002F022B000D0011FFFFFFFF59B7', is read from the RS485 interface of the device, and then the obtained raw data is input into the edge calculation model;
in step S202, the obtained raw data is filtered and processed at the local gateway 11 through the edge calculation model, and the processed result is sent to the cloud 12, and optionally, the embodiment uses the edge calculation model to obtain the raw data at the local gateway 11, for example: the '010310012a021B002F022B000D0011FFFFFFFF59B7' is filtered and processed to obtain a json object corresponding to the data output object model, and sends the json object to the cloud 12, wherein the json character string describes the original data returned from which service of which device. Fig. 3 is a schematic diagram of processing and converting uplink collected data of a device according to an embodiment of the present application, as shown in fig. 3, optionally, in this embodiment, data flow from raw data collected by the device to a json object sent to the cloud 12 is specifically: the binary stream original data '010310012A021B002F022B000D0011FFFFFFFF59B7' is read from an RS485 interface of the device, assembled into json_obj, then a local network Guan Jiaoben function protocol_decode () is called, json_obj is transferred to a script language Lua, then Lua returns json objects to c language, finally c sends json objects to a Cloud (Cloud), data conversion is carried out on the mixed language, c stores the json objects obtained after the original data conversion in a data output object model of the Cloud, the output data is { "zaoyin":55.5, "PM10":17, "identifier": "post": ",
"wendu" 25.6, "zhaodu" 188, "shifushidu" 53.9, "term_addr" 01"," PM2_5 "13 }, wherein protocol_decode () functions to convert a binary stream into json objects corresponding to object models of devices according to device protocols, lua is a compact, lightweight, extensible scripting language. According to the method, the original data is filtered and processed at the local gateway through the edge calculation model, the data processing is closer to a data source, and is not performed at an external data center or a cloud, so that delay time can be reduced, data processing and analysis efficiency can be greatly improved, and cloud load is reduced;
in step S203, the cloud end 12 sends a command to the gateway 11 to convert the data output object model parameters into binary streams through the edge calculation model, and optionally, in this embodiment, the cloud end 12 sends an input command, for example: 'term_addr' 01 'and' identifier 'post' to gateway 11, then converting the data output object model parameter json object data into corresponding binary stream data '0010300000008440c 16' through edge calculation model, wherein 'term_addr' 01 'in input command refers to 485 equipment address code, and' identifier 'post' refers to 485 equipment object model defined service identifier;
in step S204, the gateway 11 sends the converted binary stream data to the device sensor 10 for control, and optionally, in this embodiment, the gateway 11 sends the converted binary stream data '0010300000008440c 16' to the device sensor 10, and the RS485 interface receives the data, so as to realize downlink control of the device 485. Fig. 4 is a schematic diagram of data conversion of a cloud command output control according to an embodiment of the present application, as shown in fig. 4, optionally, in this embodiment, a data flow from a cloud command output parameter to a process of sending to a device is specifically: the cloud 12 sends command parameters such as: json object data ' { "term_addr": "01", "identifier": "post" } "to gateway 11, then transferring json object data to scripting language Lua, and calling scripting function protocol_encode () to convert json data into binary stream data, then Lua converts binary stream data into ASCII string, and returns to c, c sends binary stream ASCII string '0010300000008440c 16' to 485 device, realizing downlink control of device 485, wherein protocol_encode () functions to convert json data into binary stream data of device node according to protocol description. According to the embodiment, the command is sent to the gateway through the edge calculation model, and data conversion is carried out locally on the gateway, so that the equipment is controlled locally, delay time can be reduced, and the equipment can stably run offline.
Through the steps S201 to S204, compared with the prior art, the edge computing system of the internet of things lacks a customizable general edge computing framework which is uniformly and centrally managed, so that the problems of repeated and complex development, high cost and poor reusability are caused. In the embodiment, a customizable edge calculation model is adopted, original data acquired by the equipment sensor 10 is acquired, the data is input into the edge calculation model, the acquired original data is filtered and processed at the local gateway 11 through the edge calculation model, parameters corresponding to a data output object model are obtained, and the parameters are sent to the cloud 12, so that uplink acquisition of the equipment data is realized; the cloud 12 sends command parameters to the gateway 11, converts data output object model parameters into binary streams through an edge computing model, then the gateway 11 sends the binary streams obtained through conversion to the device sensor 10 for control, and downstream device control is achieved, so that the problems of repeated and complex development, high cost and poor reusability caused by the fact that an edge computing system of the Internet of things lacks a custom general edge computing frame which is uniformly and intensively managed in the prior art are solved, data processing efficiency is improved, cloud load is reduced, faster response is provided for users, development cost can be reduced, and software reusability is improved.
In some embodiments, before inputting the data collected by the device into the edge computing model, the user customizes the edge computing model at the cloud end 12 through a scripting language, wherein the data collected by the device sensor 10 is used as an input source, the customization computing result is used as a data output source of the edge computing model, fig. 5 is a schematic diagram of data conversion of the user customization edge computing model according to the embodiment of the present application, as shown in fig. 5, optionally, the data conversion of the user customization edge computing model in the embodiment is specifically: object model parameter json object data corresponding to each device data source selected by the user is set at the cloud 12, for example: { "sn": "6543211232E", "time":1574213209, "state":0, "quality":0, "identifier": post "," tags "{" PM25":29," PM10":36," shidu ":36.2," zhaodu ":564," zaoyin ":52.2," wendu ":19.2 }) is sent and transferred to c of the local gateway, then a script function protocol_decode (), and json object data are transferred to Lua, related data are calculated and cached in Lua, returned to user-defined object model parameters, converted from Lua language to c, finally the local gateway sends corresponding object model data to cloud 12, and corresponding data output object model parameters are output as: { "zaoyin":55.5, "PM10":17, "identifier": post "," wendu ":25.6," zhaodu ":188," shimushidu ":53.9," term_addr ": 01", "PM25":13}, wherein the function of the protocol_decode () function is to calculate and map new object model data based on the user selected data source. Fig. 6 is an example schematic diagram of an intelligent open test edge calculation model according to an embodiment of the present application, as shown in fig. 6, optionally, in this embodiment, a user self-defines the edge calculation model in a calculation unit through a scripting language, where negative oxygen ion data collected by an equipment sensor 10 is used as an input source, and processing data in an online gateway on a self-defined FRP, an online gateway on a platform and all gateways on the platform are used as output sources of the edge calculation model, so that a set of cloud configurable edge calculation model is built, a self-defined tool for data processing and conversion is provided for the user, the reusability of software tools is improved, and development complexity and cost are reduced.
In some embodiments, after the user completes the custom setting of the edge computing model, the custom computing model in the computing unit is issued and configured to the gateway 11, and the gateway 11 locally filters and processes the data collected by the device sensor 10.
In some of these embodiments, sending the results of the processing to the cloud 12 includes: the results of the processing are sent to the cloud 12 in accordance with the defined data output model. Optionally, parameters of the data output object model are obtained from service identifiers defined according to different equipment attributes, and during downlink control, the parameters of the downlink parameter identifiers come from control parameters input when a user clicks the added equipment control; when the uplink response is carried out, the parameters of the uplink data identifier are read from the downlink equipment, and a unified output model frame is provided for a user by setting a data output object model, so that the reusability of the model can be improved, and the use efficiency is improved.
It should be noted that the steps illustrated in the above-described flow or flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment also provides a cloud-edge coordinated edge computing system, which is used for implementing the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the terms "module," "unit," "sub-unit," and the like may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 7 is a block diagram of a cloud-edge coordinated edge computing system according to an embodiment of the present application, and as shown in fig. 7, the system includes an input module 71, an output module 72, a sending module 73, and a control module 74:
an input module 71, configured to acquire data acquired by the device sensor, and input the data into the edge calculation model; the output module 72 is configured to filter and process, at the local gateway, the data collected by the device sensor through the edge calculation model, and send the processed result to the cloud; the sending module 73 is configured to send a command to the gateway by the cloud end, and convert the data output object model parameters into binary streams through the edge calculation model; and the control module 74 is used for sending the converted binary stream to the device sensor by the gateway for control.
Through the above system, the input module 71 acquires data acquired by the device sensor, and inputs the data into the edge calculation model; the output module 72 filters and processes the original data at the local gateway through the edge calculation model, the data processing is closer to the data source, and is not performed at an external data center or the cloud, so that the delay time can be reduced, the processing and analysis efficiency of the data can be greatly improved, and the cloud load is reduced; the sending module 73 sends a command to the gateway in a cloud end, and converts the data output object model parameters into binary streams through the edge calculation model; the control module 74 sends the command to the gateway through the edge calculation model, performs data conversion locally at the gateway, and realizes local control of the device, so that delay time can be reduced, and the device can stably run offline. The system solves the problems of repeated and complex development, high cost and poor reusability caused by the lack of a custom general edge computing framework for unified and centralized management of an edge computing system of the Internet of things, improves the data processing efficiency, reduces the cloud load, provides faster response for users, reduces the development cost and improves the reusability of software.
In some embodiments, before inputting data collected by a device into an edge computing model, a user customizes the edge computing model through a scripting language at a cloud end, wherein data collected by a device sensor is used as an input source, a customization computing result is used as a data output source of the edge computing model, and optionally, in this embodiment, data conversion of the user customization edge computing model is specifically: and (3) enabling object model parameter json object data corresponding to each equipment data source selected by a user to be in cloud, wherein the object model parameter json object data comprise: { "sn": "6543211232E", "time":1574213209, "state":0, "quality":0, "identifier": post "," tags "{" PM25":29," PM10":36," shidu ":36.2," zhaodu ":564," zaoyin ":52.2," wendu ":19.2 }) is sent and transferred to c of the local gateway, then a script function protocol_decode (), and json object data are transferred to Lua, related data are calculated and cached in Lua, return to user-defined object model parameters, convert from Lua language to c, finally the local gateway sends corresponding object model data to the cloud, and output corresponding data output object model parameters are: { "zaoyin":55.5, "PM10":17, "identifier": post "," wendu ":25.6," zhaodu ":188," shimushidu ":53.9," term_addr ": 01", "PM25":13}, and the function of the protocol_decode () function is to calculate and map new object model data according to the data source selected by the user. Optionally, in this embodiment, the user customizes the edge computing model in the computing unit through the scripting language, where negative oxygen ion data acquired by the device sensor is used as an input source, and processing data in an online gateway on the custom FRP, an online gateway on the platform, and all gateways on the platform are used as output sources of the edge computing model, so that a set of cloud configurable edge computing model is built, a customizable tool for data processing and conversion is provided for the user, reusability of software tools is improved, and development complexity and cost are reduced.
In some embodiments, the input module is further configured to issue and configure the custom computing model in the computing unit to the gateway after the user completes the custom setting of the edge computing model, and the gateway locally filters and processes the data collected by the device sensor.
In some embodiments, the output module is further configured to send the processed result to the cloud according to the defined data output object model. Optionally, parameters of the data output object model are obtained from service identifiers defined according to different equipment attributes, and during downlink control, the parameters of the downlink parameter identifiers come from control parameters input when a user clicks the added equipment control; when the uplink response is carried out, the parameters of the uplink data identifier are read from the downlink equipment, and a unified output model frame is provided for a user by setting a data output object model, so that the reusability of the model can be improved, and the use efficiency is improved.
The present invention is described in detail below in connection with application scenarios.
The invention aims to provide a cloud configurable edge calculation model, data collected by equipment are collected and then provided as a data input source for the edge calculation model, and a calculation result is output to a cloud through a user-defined data output model. For example, monitoring house inclination requires four corners with level sensors, tens of pieces of raw data per second for each level, and if raw data is directly put on the platform, great stress is placed on the platform. And the user uses the cloud platform custom edge calculation model, takes the original data of the level meter as a data input source, and defines whether the house is inclined or not as a data output source. And then describing an algorithm of original data calculation by using a Lua and Python script language, and issuing the algorithm to a gateway, so that the original data does not need to be uploaded to a cloud end, the calculation script is executed locally on the gateway, and the output result is a final data result of whether a house needed by a service platform is inclined or not.
The present embodiment also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
In addition, in combination with the cloud-edge coordinated edge computing method in the above embodiment, the embodiment of the application may provide a storage medium for implementation. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements the method of edge computation of any of the above embodiments.
In one embodiment, fig. 8 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application, and as shown in fig. 8, an electronic device is provided, which may be a server, and an internal structure diagram thereof may be as shown in fig. 8. The electronic device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the electronic device is for storing data. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a cloud-edge coordinated edge computing method.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the electronic device to which the present application is applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be understood by those skilled in the art that the technical features of the above-described embodiments may be combined in any manner, and for brevity, all of the possible combinations of the technical features of the above-described embodiments are not described, however, they should be considered as being within the scope of the description provided herein, as long as there is no contradiction between the combinations of the technical features.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (6)
1. A method of edge computation for cloud-edge coordination, the method comprising:
acquiring data acquired by a device sensor, customizing an edge calculation model by a user through a scripting language at a cloud end, taking the data acquired by the device sensor as an input source, taking a custom calculation result as a data output source of the edge calculation model, and inputting the data into the edge calculation model;
filtering and processing the data at a local gateway through the edge computing model, and sending the processed result to a cloud, wherein the method comprises the following specific steps of: transmitting the result to the cloud according to the defined data output object model, wherein parameters of the data output object model are obtained from service identifiers defined according to different equipment attributes, and in the downlink control, the parameters of the downlink parameter identifiers come from control parameters input when a user clicks the added equipment control; reading parameters of an uplink data identifier from the downlink equipment when uplink response is carried out;
the cloud sends a command to a gateway, and data output object model parameters are converted into binary streams through the edge calculation model;
the gateway sends the binary stream to the device sensor for control.
2. The method of claim 1, wherein after the user completes the edge computation model custom setup, the method comprises:
and distributing and configuring the calculation model to the gateway, and locally filtering and processing the data acquired by the equipment sensor by the gateway.
3. A system of cloud-edge coordinated edge computing, the system comprising: the device sensor, the gateway and the cloud;
the input module is used for acquiring data acquired by the equipment sensor, customizing the edge calculation model at the cloud end by a user through a scripting language, taking the data acquired by the equipment sensor as an input source, taking a self-defined calculation result as a data output source of the edge calculation model, and inputting the data into the edge calculation model;
the output module is used for filtering and processing the data at the local gateway through the edge calculation model and sending the processed result to the cloud, and the specific steps comprise: transmitting the result to the cloud according to the defined data output object model, wherein parameters of the data output object model are obtained from service identifiers defined according to different equipment attributes, and in the downlink control, the parameters of the downlink parameter identifiers come from control parameters input when a user clicks the added equipment control; reading parameters of an uplink data identifier from the downlink equipment when uplink response is carried out;
the sending module is used for sending a command to the gateway by the cloud, and converting the data output object model parameters into binary streams by the edge calculation model;
and the control module is used for sending the binary stream to the equipment sensor by the gateway for control.
4. The system of claim 3, wherein after the user completes the edge computation model custom setup, the system comprises:
and the input module distributes and configures the calculation model to the gateway, and the gateway locally filters and processes the data acquired by the equipment sensor.
5. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the cloud-edge coordinated edge computing method of any of claims 1 to 2.
6. A storage medium having stored therein a computer program, wherein the computer program is arranged to perform the cloud-edge coordinated edge computing method of any of claims 1 to 2 at run-time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011179190.XA CN112380428B (en) | 2020-10-29 | 2020-10-29 | Cloud-edge coordinated edge computing method, system, electronic device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011179190.XA CN112380428B (en) | 2020-10-29 | 2020-10-29 | Cloud-edge coordinated edge computing method, system, electronic device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112380428A CN112380428A (en) | 2021-02-19 |
| CN112380428B true CN112380428B (en) | 2023-07-18 |
Family
ID=74576962
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011179190.XA Active CN112380428B (en) | 2020-10-29 | 2020-10-29 | Cloud-edge coordinated edge computing method, system, electronic device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112380428B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112911024B (en) * | 2021-05-07 | 2021-08-06 | 浙江简捷物联科技有限公司 | Method and system for collecting data of Internet of things, electronic device and storage medium |
| CN113285999A (en) * | 2021-05-18 | 2021-08-20 | 中云汇(成都)物联科技有限公司 | Edge calculation system and control method |
| CN114327689B (en) * | 2022-03-15 | 2022-07-12 | 浙江云针信息科技有限公司 | Strategy scheduling method and system for complex edge computing environment |
| CN115378787A (en) * | 2022-08-19 | 2022-11-22 | 深圳前海环融联易信息科技服务有限公司 | Cloud edge cooperation-based equipment management method, device, equipment and medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108023952A (en) * | 2017-12-04 | 2018-05-11 | 西安电子科技大学 | A kind of modularization Internet of Things application rapid build platform combined based on cloud and mist |
| CN110650084A (en) * | 2019-08-26 | 2020-01-03 | 山东省科学院自动化研究所 | Intelligent gateway, networking system and data processing method for industrial Internet of things |
| WO2020113256A1 (en) * | 2018-12-07 | 2020-06-11 | Fleet Space Technologies Pty Ltd | Remote lpwan gateway with backhaul over a high-latency communication system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103024059B (en) * | 2012-12-19 | 2015-08-26 | 北京时代凌宇科技有限公司 | A kind of middleware system for Internet of things |
| US10007513B2 (en) * | 2015-08-27 | 2018-06-26 | FogHorn Systems, Inc. | Edge intelligence platform, and internet of things sensor streams system |
| AU2016310529B2 (en) * | 2015-08-27 | 2020-12-17 | Johnson Controls Tyco IP Holdings LLP | Edge intelligence platform, and internet of things sensor streams system |
-
2020
- 2020-10-29 CN CN202011179190.XA patent/CN112380428B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108023952A (en) * | 2017-12-04 | 2018-05-11 | 西安电子科技大学 | A kind of modularization Internet of Things application rapid build platform combined based on cloud and mist |
| WO2020113256A1 (en) * | 2018-12-07 | 2020-06-11 | Fleet Space Technologies Pty Ltd | Remote lpwan gateway with backhaul over a high-latency communication system |
| CN110650084A (en) * | 2019-08-26 | 2020-01-03 | 山东省科学院自动化研究所 | Intelligent gateway, networking system and data processing method for industrial Internet of things |
Non-Patent Citations (1)
| Title |
|---|
| 基于边缘计算的健康数据管理与分析***研究;贺乐平;;电子设计工程(09);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112380428A (en) | 2021-02-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112380428B (en) | Cloud-edge coordinated edge computing method, system, electronic device and storage medium | |
| US10334048B2 (en) | On-premise data collection and ingestion using industrial cloud agents | |
| CN106374439B (en) | Software definition implementation method based on intelligent substation Protection control system | |
| CN111464627B (en) | Data processing method, edge server, center server and processing system | |
| CN110704518B (en) | Business data processing method and device based on Flink engine | |
| CN108769121A (en) | Intelligent industrial equips the method for uploading of internet of things data acquisition system and gathered data | |
| CN109510723B (en) | Gateway equipment, and transaction management and control system and method of Internet of things | |
| US20150276208A1 (en) | Cloud-level analytics for boiler networks | |
| CN108833267A (en) | Intelligent industrial gateway | |
| CN108021710A (en) | A kind of dynamic socket conversion method, device, terminal device and storage medium | |
| CN105760272B (en) | Monitoring backstage business customizing method and its system based on plug-in unit | |
| CN108400993A (en) | The Internet of things system and storage medium that intelligent industrial apparatus components formula is set up | |
| CN113055478A (en) | Method, device, equipment and medium for processing equipment data | |
| CN110191138A (en) | A kind of edge calculations intelligent adapter and implementation method towards industry manufacture | |
| CN112615746B (en) | Edge calculation method, device and system | |
| CN108363588B (en) | Method for realizing interaction between web and native function, electronic device and readable storage medium | |
| WO2020207105A1 (en) | Destination message determination method and apparatus, storage medium and electronic apparatus | |
| KR102457759B1 (en) | Method for collecting data using the Asset Administration Shell(AAS) in a smart manufacturing system | |
| CN113014617A (en) | Gateway configuration method and device based on cloud data management platform | |
| CN115333943B (en) | Deterministic network resource allocation system, method, device and storage medium | |
| CN115857910A (en) | Programming method, device, server and medium of edge computing collector | |
| CN114071515B (en) | Network optimization method, device, equipment and storage medium | |
| CN114511100A (en) | Graph model task implementation method and system supporting multi-engine framework | |
| CN113965599A (en) | Equipment management system and method based on universal management service model | |
| CN110134086B (en) | Data acquisition and control device for realizing middleware function in digital workshop |
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 |