CN113438311A - Environment inspection realization method under edge computing scene - Google Patents
Environment inspection realization method under edge computing scene Download PDFInfo
- Publication number
- CN113438311A CN113438311A CN202110703895.5A CN202110703895A CN113438311A CN 113438311 A CN113438311 A CN 113438311A CN 202110703895 A CN202110703895 A CN 202110703895A CN 113438311 A CN113438311 A CN 113438311A
- Authority
- CN
- China
- Prior art keywords
- edge
- node
- sensor
- edge node
- environment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000007613 environmental effect Effects 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 10
- 238000013459 approach Methods 0.000 claims abstract description 4
- 240000007651 Rubus glaucus Species 0.000 claims description 12
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 12
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 12
- 238000013480 data collection Methods 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 8
- 239000000428 dust Substances 0.000 claims description 7
- 238000007405 data analysis Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000005265 energy consumption Methods 0.000 description 5
- ZBMRKNMTMPPMMK-UHFFFAOYSA-N 2-amino-4-[hydroxy(methyl)phosphoryl]butanoic acid;azane Chemical compound [NH4+].CP(O)(=O)CCC(N)C([O-])=O ZBMRKNMTMPPMMK-UHFFFAOYSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 210000004210 tooth component Anatomy 0.000 description 1
Images
Classifications
-
- 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
- H04L67/10—Protocols in which an application is distributed across nodes in the network
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
- G06F9/45533—Hypervisors; Virtual machine monitors
- G06F9/45558—Hypervisor-specific management and integration aspects
- G06F2009/45595—Network integration; Enabling network access in virtual machine instances
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/502—Proximity
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an environment inspection implementation method under an edge computing scene, which relates to the technical field of edge computing scenes and comprises the following implementation processes: designing the inspection vehicle as a master control node of kubernets; designing an environment collector provided with sensors as an edge node of kubernets, wherein the edge node runs an application program for receiving each sensor and runs an intelligent environment data alarm application program; the inspection vehicle automatically traverses each edge node, when the inspection vehicle approaches one edge node, the inspection vehicle establishes connection with the edge node, collects environmental data on the edge node, issues or updates an alarm application program on the edge node, and at the moment, other edge nodes far away from the inspection vehicle automatically run in an offline manner. The invention solves the requirement that the edge environment collector needs to perform offline autonomy for a long time under the condition of unstable network or no network, and simultaneously ensures that the whole environment inspection process can still uniformly manage operation and maintenance in a kubernets cluster mode.
Description
Technical Field
The invention relates to the technical field of edge computing scenes, in particular to an environment inspection implementation method in an edge computing scene.
Background
Edge computing refers to providing computing services nearby, on the side near the source of the object or data. The application program is initiated at the edge side, so that a faster network service response is generated, and the basic requirements of the industry in the aspects of real-time business, application intelligence, safety, privacy protection and the like are met. When the computing resources are deployed close to the edge side, a great deal of challenges are brought to the traditional cluster management mode, for example, the pressure of the sharp increase of the number of edge nodes on cost and energy consumption, or the pressure of cluster management when the edge environmental conditions are not good.
Environmental routing inspection is an edge computing scenario application. In a large warehouse plant or a suburb of a garden, sensors are required to be deployed to collect environmental information, such as current temperature, humidity, dust concentration, fire and the like, and an environment collector provided with various sensors is an edge node. In some cases, limited by environmental or cost considerations, these numerous edge nodes may not have a stable network connection to the central control cluster, requiring a polling car to cycle through the various edge nodes to collect environmental data. The edge scene of the node and the control weak connection brings new problems to the management of the cluster, for example, the control node and the working node in the kubernets cluster are stably connected, and when the network connection is unstable, the control node considers that the working node is in failure and stops the application on the working node.
In an edge computing or internet of things scene, as an edge ubiquitous intelligent carrier, the scale of an edge node is very large, and low energy consumption and low cost of the edge node are important requirements. In an environment inspection scene, a carrier collector of a sensor needs to perform long-time offline autonomous work as an edge node, so that low cost and low energy consumption are considered.
Kubernetes (k 8s for short) is an open source container orchestration and management engine, supporting large-scale container deployment. KubeeEdge is a kubernets extension technology which is made on kubernets aiming at an edge computing scene, and extends containerized application program arrangement capacity of a local cluster to a host of an edge, so that core infrastructure support is provided for networking, application deployment and metadata synchronization between a cloud and the edge, the transmission of cloud edge messages can be solved under the scene that a network between a control (cloud) and a node (edge) is unstable, and when the network between the control (cloud) and the node (edge) is unstable or offline, applications on the edge node and the edge node can operate autonomously.
Based on the above, the edge environment collector needs to perform offline autonomy for a long time under the condition that the network is unstable or no network exists, and an environment inspection implementation method in an edge computing scene is designed and researched.
Disclosure of Invention
Aiming at the requirements and the defects of the prior art development, the invention provides an environment inspection implementation method in an edge computing scene, which solves the requirement that an edge environment collector needs to perform offline autonomy for a long time under the condition of unstable network or no network, and simultaneously ensures that the whole environment inspection process can still uniformly manage operation and maintenance in a kubernets cluster mode, thereby reducing the operation and maintenance cost.
The invention discloses an environment inspection implementation method under an edge computing scene, which adopts the following technical scheme for solving the technical problems:
an environment inspection implementation method under an edge computing scene specifically comprises the following steps:
designing the inspection vehicle as a master control node of kubernets;
designing an environment collector provided with sensors as an edge node of kubernets, wherein the edge node runs an application program for receiving each sensor and runs an intelligent environment data alarm application program;
the inspection vehicle automatically traverses each edge node, when the inspection vehicle approaches one edge node, the inspection vehicle establishes connection with the edge node, collects environmental data on the edge node, issues or updates an alarm application program on the edge node, and at the moment, other edge nodes far away from the inspection vehicle automatically run in an offline manner.
Specifically, in the implementation process of the related environment inspection implementation method, a KubeEdge technology is utilized to abstract each sensor device of an edge node into a device model, and deployment is performed in a CDR (complementary digital radio) self-defined resource mode of kubernets, so that each sensor and sensor data thereof can be uniformly managed through a standard kubernets api server.
Specifically, the related patrol vehicle adopts a standard raspberry group 4B as a master control node, a RaspdianOS buster 32bit operating system is installed on the patrol vehicle, and a master component and a kubbeernets 1.18 system peripheral 1.4 edge computing component are also deployed on the patrol vehicle in a cloud primary container mode.
More specifically, in the master control node, a sensor data collection container and an environment data analysis container are deployed, the sensor data collection container reads sensor data on the edge node by accessing a standard kubernets api server, and the environment data analysis container analyzes the read sensor data by accessing the standard kubernets api server.
More specifically, a standby sensor environment data collection container is deployed in the master control node, and if the inspection vehicle inspects a certain edge node with abnormal work, the inspection vehicle can temporarily stay there, and the standby sensor on the inspection vehicle is used for collecting environment data until the abnormal edge node or the abnormal sensor is replaced.
Specifically, the related edge nodes adopt raspberry zero W1.1, a Raspiana jsessie 32bit operating system is installed on the edge nodes, and node components of a kubberenets node1.18 system and kubbeedge edgegetcore 1.4 edge computing components are deployed on the edge nodes in a cloud native container mode;
the edge node is provided with a temperature sensor, a humidity sensor, a dust sensor and a flame sensor through GPIO interfaces carried by the raspberry Pi zero W1.1, and the temperature sensor, the humidity sensor, the dust sensor and the flame sensor are used for collecting environmental information.
Preferably, the raspberry Pi zero W1.1 is provided with a Bluetooth module;
the method comprises the steps that blue Z and blue man Bluetooth components are installed on a master control node and edge nodes, when the master control node and the adjacent edge nodes form a Group Ad-hoc NetWork, namely a GN NetWork, the master control node is configured to be a Bluetooth PAN GN role, and each edge node is configured to be a Bluetooth PANU role.
More specifically, the kubbedge clodcore 1.4 edge compute component of the master control node includes a device controller component and an edge controller component, the edge controller component is used for deploying container application on the edge node;
the kubbedge edgecore 1.4 edge computing component of the edge node comprises a Devicetwin component and an edge component, wherein the edge component is used for starting and running a container application deployed on the edge node;
after sensors on each edge node are deployed through a kubernets api server, a device controller component in a master control node is used for controlling the sensors on each edge node, and at the moment, a corresponding sensor control container is created by a DeviceTwin component on the edge node;
when the edge node is offline, the DeviceTwin component caches the environmental data acquired by the sensor, and when the edge node is in network communication with the master control node, the sensor data is read through the kubbernees api server, the device controller component and the DeviceTwin component, or the opening and closing of the sensor is controlled through a sensor control container of the DeviceTwin component.
More specifically, through the kubernetes api server of the master control node,
(1) the sensor control container established on the edge node is used for completing the environmental information acquisition work of each sensor;
(2) deploying an edge data processing container on the edge node, and performing advanced preprocessing on the environmental data acquired by the sensor by using the edge data processing container;
(3) and deploying an intelligent alarm application container on the edge node, and acquiring real-time environment data by using the intelligent alarm application container and carrying out reasoning to generate alarm information.
Compared with the prior art, the environment inspection implementation method under the edge computing scene has the beneficial effects that:
(1) the invention solves the requirement that the edge environment collector needs to be offline autonomous for a long time under the condition of unstable network or no network, ensures that the whole environment inspection process can still uniformly manage operation and maintenance in a kubernets cluster mode, reduces the operation and maintenance cost, and can meet the requirements of an edge computing scene on low power consumption and low cost of edge nodes;
(2) according to the invention, the inspection vehicle and the edge nodes are constructed into a weakly connected cluster, the environment information collector as the edge node can not only perform the tasks of collecting and intelligently analyzing the environment information for a long time in an off-line autonomous mode, but also can be connected with the environment information collector when the inspection vehicle inspects a certain environment information collector, collect the information of the collector, issue or update the application program running on the edge collector, and the problem that the edge node collector cannot be connected with the master control node in real time in the environment of a large warehouse, the field of a garden and the like is solved;
(3) the invention can construct a weakly connected cluster between the edge node and the master control node under the condition that the edge node is relatively dispersed and cannot establish stable strong connection with the master control node, and uniformly manages the sensors on each edge node through the kubernetes api server to carry out the environmental data of the edge node.
Drawings
FIG. 1 is a schematic diagram of the environmental inspection method of the present invention;
fig. 2 is a logical architecture diagram of the environment inspection method of the present invention.
Detailed Description
In order to make the technical scheme, the technical problems to be solved and the technical effects of the present invention more clearly apparent, the following technical scheme of the present invention is clearly and completely described with reference to the specific embodiments.
The first embodiment is as follows:
with reference to fig. 1 and 2, the present embodiment provides an environment inspection implementation method in an edge computing scenario, where the implementation process specifically includes:
designing the inspection vehicle as a master control node of kubernets;
designing an environment collector provided with sensors as an edge node of kubernets, wherein the edge node runs an application program for receiving each sensor and runs an intelligent environment data alarm application program;
the inspection vehicle automatically traverses each edge node, when the inspection vehicle approaches one edge node, the inspection vehicle establishes connection with the edge node, collects environmental data on the edge node, issues or updates an alarm application program on the edge node, and at the moment, other edge nodes far away from the inspection vehicle automatically run in an offline manner.
In the implementation process of the method of this embodiment, each sensor device of the edge node is abstracted into a device model by using a KubeEdge technology, and is deployed in a CDR custom resource manner of kubernets, so that each sensor and sensor data thereof can be uniformly managed by a standard kubernets api server, as indicated by reference number (r) in fig. 2.
In this embodiment, as shown by the reference numeral (r) in fig. 2, the inspection vehicle adopts a standard raspberry group 4B as a master control node, a RaspbianOS buster 32bit operating system is installed on the inspection vehicle, a master component and a kubbered clodcore 1.4 edge computing component of a kubberenets 1.18 system are also deployed on the inspection vehicle in a cloud native container manner, the kubbered clodcore 1.4 edge computing component includes a device controller component and an edge controller component, and the container can be deployed at an edge node by the edge controller component, as shown by the reference numeral (r) in fig. 2.
In the master control node, a sensor data collection container and an environmental data analysis container are deployed, as indicated in FIG. 2 by reference number R,
The sensor data collection container reads the sensor data on the edge node by accessing the standard kubernets api server, and the environment data analysis container analyzes the read sensor data by accessing the standard kubernets api server.
In this embodiment, as indicated by reference number @ in fig. 2, an edge node adopts raspberry group zero W1.1, a raspbiana os jessage 32bit operating system is installed on the edge node, a node component of a kubberenets node1.18 system, a kubbedge edgegetcore 1.4 edge computing component are also deployed on the edge node in a cloud native container manner, the kubbedge edgegetcore 1.4 edge computing component includes a DeviceTwin component and an edge component, and the edge component is used to start and run a container application deployed on the edge node, as indicated by reference number @ in fig. 2.
As shown by the symbol (c) in the attached figure 2, the edge node is provided with a temperature sensor, a humidity sensor, a dust sensor and a flame sensor through a GPIO (general purpose input/output) interface on the raspberry Pi zero W1.1, and the temperature sensor, the humidity sensor, the dust sensor and the flame sensor are used for collecting environmental information.
The raspberry Pi zero W1.1 is provided with a Bluetooth module. Installing blue Z and blue man blue tooth components on master control node and edge node, when master control node and edge node close to it form Group Ad-hoc NetWork, namely GN NetWork, as shown in figure 2 with reference number
The master control node is configured to have a Bluetooth PAN GN role, and each edge node is configured to have a Bluetooth PANU role.
In this embodiment, after deploying the sensors on each edge node through the kubernets api server, as indicated by the reference number in fig. 2
Control of the sensors on each edge node is accomplished using the device controller component in the master control node, at which time the DeviceTwin component on the edge node creates a corresponding sensor control container. When the edge node is offline, the reference number in FIG. 2
The DeviceTwin component caches environment data acquired by the sensor; when the edge node is communicated with the master control node network, reading is carried out through a kubernetes api server, a device controller component and a DeviceTwin componentSensor data, or, the sensor control container of the DeviceTwin assembly is used for controlling the opening and closing of the sensor.
By means of a kubernetes api server,
(1) the sensor control container established on the edge node is used for completing the collection work of the environmental information of each sensor, such as the mark (c) in the attached figure 2;
(2) deploying an edge data processing container on the edge node, and performing advanced preprocessing on the environment data acquired by the sensor by using the edge data processing container, as shown by a reference symbol (b) in the attached figure 2;
(3) deploying an intelligent alarm application container on the edge node, acquiring real-time environment data by using the intelligent alarm application container, performing reasoning, and generating alarm information, as indicated by a symbol ninx in fig. 2.
The environment inspection implementation method based on the embodiment comprises the following execution flows:
Step 2, after the inspection vehicle is connected with the edge node A, the edge node is displayed as a ready state in the kubernets cluster, and the sensor data collection container on the master control node can read the sensor data on the edge node A by accessing the standard kubernets api server, such as the number marked in the attached figure 2
As shown.
And 3, updating a sensor control container, an edge data processing container and an intelligent alarm application container which run on the edge node A by the master control node through an edge controller component, and downloadingDeploying new container applications, as indicated by the reference numerals in FIG. 2
As shown.
And 4, performing offline autonomous operation on other edge nodes far away from the inspection vehicle in a low-energy-consumption mode, and acquiring cache environment data, such as the reference numbers in the attached figure 2
As shown.
Example two:
with reference to fig. 1 and 2, on the basis of the first embodiment, with reference to fig. 2, in the implementation process of the environment inspection implementation method in the edge computing scenario of this embodiment, as indicated by the reference number in fig. 2
And (3) deploying a standby sensor environment data collection container in the master control node, and if the inspection vehicle inspects a certain edge node with abnormal work, temporarily stopping the inspection vehicle at the position, and collecting environment data by using the standby sensor on the inspection vehicle until the abnormal edge node or the abnormal sensor is replaced.
The environment inspection implementation method based on the embodiment comprises the following execution flows:
Step 2, after the inspection vehicle is connected with the edge node A, the edge node is displayed as a ready state in the kubernets cluster, and the sensor data collection container on the master control node can access the standard kubernetes api server to read sensor data on edge node A, as indicated by the reference number in FIG. 2
As shown.
And step 3, the master control node further updates a sensor control container, an edge data processing container and an intelligent alarm application container which run on the edge node A through an edge controller component, and issues and deploys a new container application, such as the reference number in the attached figure 2
As shown.
And 5, performing offline autonomous operation on other edge nodes far away from the inspection vehicle in a low-energy-consumption mode, and acquiring cache environment data, such as the reference numbers in the attached figure 2
As shown.
In summary, the method for realizing environment inspection in the edge computing scene solves the requirement that the edge environment collector needs to perform offline autonomy for a long time under the condition of unstable network or no network, ensures that the whole environment inspection process can still uniformly manage operation and maintenance in a kubernets cluster mode, reduces the operation and maintenance cost, and can meet the requirements of the edge computing scene on low power consumption and low cost of edge nodes.
The principles and embodiments of the present invention have been described in detail using specific examples, which are provided only to aid in understanding the core technical content of the present invention. Based on the above embodiments of the present invention, those skilled in the art should make any improvements and modifications to the present invention without departing from the principle of the present invention, and therefore, the present invention should fall into the protection scope of the present invention.
Claims (9)
1. The environment inspection implementation method under the edge computing scene is characterized by specifically comprising the following implementation processes:
designing the inspection vehicle as a master control node of kubernets;
designing an environment collector provided with sensors as an edge node of kubernets, wherein the edge node runs an application program for receiving each sensor and runs an intelligent environment data alarm application program;
the inspection vehicle automatically traverses each edge node, when the inspection vehicle approaches one edge node, the inspection vehicle establishes connection with the edge node, collects environmental data on the edge node, issues or updates an alarm application program on the edge node, and at the moment, other edge nodes far away from the inspection vehicle automatically run in an offline manner.
2. The environment inspection realization method under the edge computing scene according to claim 1, characterized in that, the kubeeedge technology is used to abstract each sensor device of the edge node into a device model, and the device model is deployed in a CDR custom resource manner of kubernets, so that each sensor and the sensor data thereof can be uniformly managed through a standard kubernets api server.
3. The environment inspection realization method under the edge computing scene according to claim 2, characterized in that the inspection vehicle adopts a standard raspberry group 4B as a master control node, a RaspbianOSbuster 32bit operating system is installed on the inspection vehicle, and a master component of a kubberenets 1.18 system and a kubbeelder clodcore 1.4 edge computing component are also deployed on the inspection vehicle in a cloud native container manner.
4. The method for realizing environment inspection in an edge computing scene according to claim 3, wherein a sensor data collection container and an environment data analysis container are deployed in a master control node, the sensor data collection container reads sensor data on the edge node by accessing a standard kubernets api server, and the environment data analysis container analyzes the read sensor data by accessing the standard kubernets api server.
5. The method for realizing environmental inspection under the scene of edge computing according to claim 4, characterized in that a standby sensor environmental data collection container is deployed in the master control node, if the inspection vehicle inspects an edge node with a certain abnormal work, the inspection vehicle can temporarily stay there, and the standby sensor on the inspection vehicle is used to collect environmental data until the abnormal edge node or the abnormal sensor is replaced.
6. The environment inspection realization method under the edge computing scene according to claim 3, characterized in that the edge node adopts raspberry zero W1.1, the edge node is installed with a RaspdianOS jessee 32bit operating system, and the edge node is also deployed with a node component of a kubbeenetes node1.18 system and a kubbeeed edge computing component 1.4 in a cloud native container manner;
the edge node is provided with a temperature sensor, a humidity sensor, a dust sensor and a flame sensor through GPIO interfaces carried by the raspberry Pi zero W1.1, and the temperature sensor, the humidity sensor, the dust sensor and the flame sensor are used for collecting environmental information.
7. The environment inspection implementation method under the edge computing scene is characterized in that a Bluetooth module is arranged on a raspberry Pi zero W1.1;
the method comprises the steps that blue Z and blue man Bluetooth components are installed on a master control node and edge nodes, when the master control node and the adjacent edge nodes form a Group Ad-hoc NetWork, namely a GN NetWork, the master control node is configured to be a Bluetooth PAN GN role, and each edge node is configured to be a Bluetooth PANU role.
8. The environment inspection implementation method under the edge computing scene according to claim 6, wherein the kubbeeldcore 1.4 edge computing component of the master control node includes a device controller component and an edge controller component, and the edge controller component is used for deploying a container application on the edge node;
the kubbedge edgecore 1.4 edge computing component of the edge node comprises a Devicetwin component and an edge component, wherein the edge component is used for starting and running a container application deployed on the edge node;
after sensors on each edge node are deployed through a kubernets api server, a device controller component in a master control node is used for controlling the sensors on each edge node, and at the moment, a corresponding sensor control container is created by a DeviceTwin component on the edge node;
when the edge node is offline, the DeviceTwin component caches the environmental data acquired by the sensor, and when the edge node is in network communication with the master control node, the sensor data is read through the kubbernees api server, the device controller component and the DeviceTwin component, or the opening and closing of the sensor is controlled through a sensor control container of the DeviceTwin component.
9. The method for realizing environmental inspection in an edge computing scene according to claim 8, wherein the map is used to control the kubernetes api server of the node,
(1) the sensor control container established on the edge node is used for completing the environmental information acquisition work of each sensor;
(2) deploying an edge data processing container on the edge node, and performing advanced preprocessing on the environmental data acquired by the sensor by using the edge data processing container;
(3) and deploying an intelligent alarm application container on the edge node, and acquiring real-time environment data by using the intelligent alarm application container and carrying out reasoning to generate alarm information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110703895.5A CN113438311A (en) | 2021-06-24 | 2021-06-24 | Environment inspection realization method under edge computing scene |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110703895.5A CN113438311A (en) | 2021-06-24 | 2021-06-24 | Environment inspection realization method under edge computing scene |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113438311A true CN113438311A (en) | 2021-09-24 |
Family
ID=77753981
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110703895.5A Pending CN113438311A (en) | 2021-06-24 | 2021-06-24 | Environment inspection realization method under edge computing scene |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113438311A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509367A (en) * | 2011-10-28 | 2012-06-20 | 江苏省电力公司常州供电公司 | Three-dimensional (3D) simulation polling system of electric power automation motor room |
| CN112099951A (en) * | 2020-09-16 | 2020-12-18 | 济南浪潮高新科技投资发展有限公司 | KubeEdge component-based local edge device collaborative computing method |
| CN112162829A (en) * | 2020-10-29 | 2021-01-01 | 杭州谐云科技有限公司 | Resource monitoring data preprocessing system under edge computing scene |
| CN112910981A (en) * | 2021-01-27 | 2021-06-04 | 联想(北京)有限公司 | Control method and device |
-
2021
- 2021-06-24 CN CN202110703895.5A patent/CN113438311A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102509367A (en) * | 2011-10-28 | 2012-06-20 | 江苏省电力公司常州供电公司 | Three-dimensional (3D) simulation polling system of electric power automation motor room |
| CN112099951A (en) * | 2020-09-16 | 2020-12-18 | 济南浪潮高新科技投资发展有限公司 | KubeEdge component-based local edge device collaborative computing method |
| CN112162829A (en) * | 2020-10-29 | 2021-01-01 | 杭州谐云科技有限公司 | Resource monitoring data preprocessing system under edge computing scene |
| CN112910981A (en) * | 2021-01-27 | 2021-06-04 | 联想(北京)有限公司 | Control method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10880362B2 (en) | Virtual electrical networks | |
| EP3342137B1 (en) | Edge intelligence platform, and internet of things sensor streams system | |
| EP3285127A1 (en) | Remote industrial automation site operation in a cloud platform | |
| Milenkovic | Internet of Things: Concepts and System Design | |
| CN113612820A (en) | Cloud edge device platform control architecture and method based on Superedge and edgeXFioutry | |
| CN111142414A (en) | Intelligent power plant management and control system based on edge cloud cooperation | |
| CN115174621B (en) | Edge internet of things proxy device for intelligent inspection of hydropower station | |
| CN112202851A (en) | Intelligent terminal plug and play method based on Internet of things technology | |
| CN112653770A (en) | Distributed engineering machinery monitoring system based on block chain | |
| Shi et al. | Construction of industrial internet of things based on mqtt and opc ua protocols | |
| CN112291728A (en) | Private industry application platform implementation architecture based on 5G network | |
| Gomes et al. | Industrial and artificial Internet of Things with augmented reality | |
| KR102411824B1 (en) | Factory energy management system based on artifical intelligence and method thereof | |
| CN111077851A (en) | Chemical process fault diagnosis system based on gas chromatography fog calculation framework | |
| CN113438311A (en) | Environment inspection realization method under edge computing scene | |
| CN112486666A (en) | Model-driven reference architecture method and platform | |
| CN114363164A (en) | Cloud network service arrangement control method and system, storage medium and electronic equipment | |
| Abraham | Internet of things (IoT) with cloud computing and machine-to-machine (M2M) communication | |
| Gilani et al. | Alternative controls for soft real-time industrial control services in case of broken cloud links | |
| CN112491962A (en) | Model-driven intelligent distributed architecture method and platform | |
| Xia et al. | Key Technologies of Internet of Things (IoT) Terminal in Intelligent Energy | |
| Deshpande et al. | A survey on internet of things (iot), industrial iot (iiot) and industry 4.0 | |
| Christou et al. | Revolutionizing Connectivity: The Power of AI, IoT, and Edge Computing for Smart and Autonomous Systems | |
| Sharma et al. | Adaptability of IoT and Cloud for Enabling the Smart City: Applications and Challenges | |
| Lin et al. | Power distribution network management based on edge computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210924 |
|
| RJ01 | Rejection of invention patent application after publication |