CN113810792B - Edge data acquisition and analysis system based on cloud computing - Google Patents
Edge data acquisition and analysis system based on cloud computing Download PDFInfo
- Publication number
- CN113810792B CN113810792B CN202111372713.7A CN202111372713A CN113810792B CN 113810792 B CN113810792 B CN 113810792B CN 202111372713 A CN202111372713 A CN 202111372713A CN 113810792 B CN113810792 B CN 113810792B
- Authority
- CN
- China
- Prior art keywords
- sensing node
- data
- node
- sensing
- edge
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 94
- 238000004364 calculation method Methods 0.000 claims description 52
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 9
- 238000007405 data analysis Methods 0.000 claims description 7
- 238000013480 data collection Methods 0.000 claims description 7
- 230000005856 abnormality Effects 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000000034 method Methods 0.000 abstract description 5
- 238000001514 detection method Methods 0.000 description 7
- 230000009471 action Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010205 computational analysis Methods 0.000 description 2
- 238000012806 monitoring device Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
-
- 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
-
- 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
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
- H04Q2209/84—Measuring functions
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Environmental & Geological Engineering (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
Abstract
The invention discloses an edge data acquisition and analysis system based on cloud computing, which comprises a sensing node classification module, a cloud computing center analysis module, a plurality of edge equipment analysis modules, an acquisition period analysis module, a characteristic attribute analysis module and an equipment matching module, wherein the cloud computing center analysis module is used for analyzing the acquired data of sensing nodes by the cloud computing center, and the edge equipment analysis module is used for analyzing the acquired data of the sensing nodes by the edge equipment. The method classifies all the sensing nodes into a first sensing node, a second sensing node and a third sensing node in advance, selects whether edge computing or cloud computing is carried out according to the types of the sensing nodes, and further analyzes the characteristic attribute of the acquired data and selects a proper analysis mode when the sensing node is the third sensing node.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to an edge data acquisition and analysis system based on cloud computing.
Background
The cloud computing is a system formed by decomposing a plurality of data computing processing programs through a network and processing and analyzing the decomposed small programs through a server to obtain results, and the cloud computing is integrated with computer technologies such as distributed computing, utility computing, load balancing, parallel computing, network storage, hot backup redundancy, virtualization and the like, and has the advantages of high virtualization technology, flexibility and analysis precision. The edge computing is that an open platform is adopted near a data source, the nearest service is directly provided nearby, an application program of the edge computing is initiated at the edge side of the data source, the transmission process of data on the network is reduced, the real-time requirement of data analysis can be met, the analysis precision of the edge computing is lower than that of cloud computing, and the time consumed by the cloud computing is longer than that consumed by the edge computing. How to reasonably distribute data to cloud computing and edge computing to ensure the effectiveness and precision of data processing is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide an edge data acquisition and analysis system based on cloud computing to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an edge data acquisition and analysis system based on cloud computing comprises a sensing node classification module, a cloud computing center analysis module, a plurality of edge equipment analysis modules, an acquisition period analysis module, a characteristic attribute analysis module and an equipment matching module, wherein the cloud computing center analysis module analyzes the acquired data of sensing nodes for the cloud computing center, the edge equipment analysis module analyzes the acquired data of the sensing nodes for the edge equipment, the sensing node classification module classifies all the sensing nodes in advance, the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node, when a certain sensing node is the first sensing node, the cloud computing center analysis module is enabled to work, when a certain sensing node is the second sensing node, the acquisition period analysis module judges whether the acquired data of the sensing node is the last acquired data in the acquisition period, if the current time is longer than the preset time, the cloud computing center analysis module is enabled to work, otherwise, the edge device analysis module is enabled to work, when a certain sensing node is a third sensing node, the characteristic attribute analysis module obtains the characteristic attribute of the data acquired by the sensing node at the time, whether the edge computing device or the cloud computing center analyzes the data acquired at the time is judged according to the characteristic attribute, and the device matching module is enabled to match the proper edge computing device to analyze the acquired data when the edge device analysis module works.
Further, the sensing node classification module includes a time interval acquisition module and a time interval comparison module, the time interval acquisition module is configured to acquire a time interval between two adjacent acquired data of a certain sensing node, the time interval comparison module compares the time interval with a first time interval threshold and a second time interval threshold, the sensing node is set as a first sensing node when the time interval of the sensing node is greater than or equal to the first time interval threshold, the sensing node is set as a second sensing node when the time interval of the sensing node is less than or equal to the second time interval threshold, an acquisition cycle of the sensing node is set according to the length of the time interval, the sensing node is set as a third sensing node when the time interval of the sensing node is less than the first time interval threshold and the time interval of the sensing node is greater than the second time interval threshold, wherein the first time interval threshold is greater than the second time interval threshold.
Further, the characteristic attribute analysis module comprises a reference index acquisition module, an observation index acquisition module, a center index acquisition module, an attribute value calculation module and an attribute comparison module, wherein the reference index acquisition module is provided with the sensing node as a center node, a time period between the time of the data acquisition of the center node and the time of the data acquisition of the center node is provided as a reference time period of the data acquisition of the time, the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of the data acquisition of the center node are counted, the times Nc of data transmission to the cloud computing center are counted, then the reference index X = Nc/Nx of the data acquisition of the time, the observation index acquisition module is used for counting the total number Ns of all the sensing nodes and the number Ng of the observation nodes which are the center node, then the observation index Y =1-Ng/Ns of the data acquisition of the time, wherein, if there are two sensor nodes, one of the sensor nodes is a first sensor node, the other sensor node is a second sensor node, when there is an abnormality in the data collected by the first sensor node, and after that, the data collected by the second sensor node for the first time is normal, then the second sensor node is an observation node of the first sensor node, the central index obtaining module obtains the number Nz of times that the central node collects data in the latest preset time period and the number Ne of times that the collected data of the central node is transmitted to the cloud computing center for analysis, then the central index Z of the collected data is = Ne/Nz, the attribute value computing module computes the attribute value U = 0.36X + 0.32Y + 0.32Z of the collected data of the central node, the attribute comparing module compares the attribute value of the collected data with the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data; and when the attribute value of the acquired data is less than or equal to the attribute threshold value, the cloud computing center analyzes the acquired data of the sensing node.
Further, the device matching module comprises a first index calculation module, a second index calculation module, a candidate index calculation module and a candidate index sorting module, wherein the first index calculation module acquires the current data volume to be processed of each edge calculation device, respectively calculates the estimated time length of each edge calculation device after analyzing the current data volume to be processed, and normalizes the estimated time length of a certain edge calculation device to obtain a first index Q of the edge calculation device; the second index calculation module obtains the number Dz of the collected data of the sensing node historically analyzed by a certain edge calculation device and the number Dp of the analysis errors of the collected data of the sensing node, then the second index P = Dp/Dz of the edge calculation device, the candidate index calculation module calculates the candidate index V =0.58 × Q +0.42 × P of the certain edge calculation device, the candidate index sorting module sorts the candidate indexes corresponding to the edge calculation devices in the order from small to large, and the first edge calculation device is selected as the analysis device of the collected data of the sensing node.
Further, the data acquisition and analysis system adopts a data acquisition and analysis method, and the data acquisition and analysis method comprises the following steps:
classifying all sensing nodes in advance, wherein the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node,
when a certain sensing node is a first sensing node, enabling the cloud computing center to analyze the acquired data of the sensing node;
when a certain sensing node is a second sensing node, acquiring whether the acquired data of the sensing node is the last acquired data in the acquisition period, if so, enabling the cloud computing center to analyze the acquired data of the sensing node, otherwise, enabling the edge equipment to analyze the acquired data of the sensing node;
when a certain sensing node is a third sensing node, acquiring the characteristic attribute of the acquired data of the sensing node at the time, judging whether the edge computing equipment or the cloud computing center analyzes the acquired data at the time according to the characteristic attribute,
when the edge device analyzes the collected data of a certain sensing node, the proper edge computing device is matched to analyze the collected data.
Further, the classifying the sensing nodes in advance includes:
acquiring the time interval between two adjacent acquired data of a certain sensing node,
when the time interval of the sensing node is larger than or equal to the first time interval threshold value, the sensing node is a first sensing node,
when the time interval of the sensing node is less than or equal to a second time interval threshold value, the sensing node is a second sensing node, and the acquisition period of the sensing node is set according to the length of the time interval;
when the time interval of the sensing node is smaller than the first time interval threshold value and the time intervals of the sensing node are larger than the second time interval threshold value, the sensing node is a third sensing node, wherein the first time interval threshold value is larger than the second time interval threshold value.
Further, the acquiring the characteristic attribute of the collected data of the sensing node includes:
the sensing node is set as a central node,
setting a time period between the time of the central node for acquiring the data and the time of the central node for acquiring the data last time as a reference time period of the data acquisition, counting the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of the data acquisition of the central node, and counting the times Nc of data transmission to a cloud computing center, wherein the times Nc of the data acquisition are counted, and then the reference index X = Nc/Nx of the data acquisition;
counting the total number Ns of all the sensing nodes and the number Ng of observation nodes which are central nodes, wherein the observation index Y =1-Ng/Ns of the acquired data, if two sensor nodes exist, one sensing node is a first sensing node, the other sensing node is a second sensing node, and when the acquired data of a certain time of the first sensing node is abnormal, and the acquired data of the second sensing node is normal after that, the second sensing node is the observation node of the first sensing node;
acquiring the number Nz of data collected by the central node in the latest preset time period and the number Ne of data collected by the central node and transmitted to the cloud computing center for analysis, wherein the central index Z = Ne/Nz of the data collected,
calculating the attribute value U = 0.36X + 0.32Y + 0.32Z of the data collected at the time of the central node,
when the attribute value of the acquired data is larger than the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data;
and when the attribute value of the acquired data is less than or equal to the attribute threshold value, the cloud computing center analyzes the acquired data of the sensing node.
Further, the analyzing the collected data by the matching appropriate edge computing device comprises:
acquiring the current data volume to be processed of each edge computing device, respectively calculating the estimated time length of each edge computing device after the current data volume to be processed is analyzed, and performing normalization processing on the estimated time length of a certain edge computing device to obtain a first index Q of the edge computing device;
acquiring the times Dz of historical analysis of the collected data of the sensing node and the times Dp of analysis errors of the collected data of the sensing node by a certain edge computing device, and then the second index P = Dp/Dz of the edge computing device,
then the candidate index V =0.58 × Q +0.42 × P for a certain edge computing device;
and sorting the candidate indexes corresponding to each edge computing device from small to large, and selecting the edge computing device with the first sorting as the analysis device of the data acquired by the sensing node at the time.
Compared with the prior art, the invention has the following beneficial effects: the invention classifies each sensing node into a first sensing node, a second sensing node and a third sensing node in advance, selects whether edge computing or cloud computing is carried out according to the type of the sensing node, and further analyzes the characteristic attribute of the acquired data and selects a proper analysis mode when the sensing node is the third sensing node, thereby not only improving the working efficiency of edge computing equipment and a cloud computing center, but also ensuring the high-efficiency monitoring of the equipment of the data acquired by the sensing node.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic block diagram of an edge data acquisition and analysis system based on cloud computing according to the present invention.
Detailed Description
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.
Referring to fig. 1, the present invention provides a technical solution: an edge data acquisition and analysis system based on cloud computing comprises a sensing node classification module, a cloud computing center analysis module, a plurality of edge equipment analysis modules, an acquisition period analysis module, a characteristic attribute analysis module and an equipment matching module, wherein the cloud computing center analysis module analyzes the acquired data of sensing nodes for the cloud computing center, the edge equipment analysis module analyzes the acquired data of the sensing nodes for the edge equipment, the sensing node classification module classifies all the sensing nodes in advance, the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node, when a certain sensing node is the first sensing node, the cloud computing center analysis module is enabled to work, when a certain sensing node is the second sensing node, the acquisition period analysis module judges whether the acquired data of the sensing node is the last acquired data in the acquisition period, if the current time is longer than the preset time, the cloud computing center analysis module is enabled to work, otherwise, the edge device analysis module is enabled to work, when a certain sensing node is a third sensing node, the characteristic attribute analysis module obtains the characteristic attribute of the data acquired by the sensing node at the time, whether the edge computing device or the cloud computing center analyzes the data acquired at the time is judged according to the characteristic attribute, and the device matching module is enabled to match the proper edge computing device to analyze the acquired data when the edge device analysis module works.
The sensing node classification module comprises a time interval acquisition module and a time interval comparison module, wherein the time interval acquisition module is used for acquiring the time interval between two adjacent acquired data of a certain sensing node, the time interval comparison module is used for comparing the time interval with a first time interval threshold value and a second time interval threshold value, the sensing node is set as a first sensing node when the time interval of the sensing node is greater than or equal to the first time interval threshold value, the sensing node is set as a second sensing node when the time interval of the sensing node is less than or equal to the second time interval threshold value, the acquisition period of the sensing node is set according to the length of the time interval, the sensing node is set as a third sensing node when the time interval of the sensing node is less than the first time interval threshold value and the time interval of the sensing node is greater than the second time interval threshold value, wherein the first time interval threshold is greater than the second time interval threshold.
The characteristic attribute analysis module comprises a reference index acquisition module, an observation index acquisition module, a center index acquisition module, an attribute value calculation module and an attribute comparison module, wherein the reference index acquisition module is used for setting the sensing node as a center node, setting a time period between the time of acquiring data and the time of acquiring data last time of the center node as a reference time period of acquiring data, counting the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of acquiring data of the center node, and counting the times Nc of transmitting the acquired data to the cloud computing center, so that the reference index X = Nc/Nx of the acquired data, the observation index acquisition module is used for counting the total number Ns of all the sensing nodes and the number Ng of the observation nodes which are the center node, so that the observation index Y = 1-Ng/Ns/of the acquired data, wherein, if there are two sensor nodes, one of the sensor nodes is a first sensor node, the other sensor node is a second sensor node, when there is an abnormality in the data collected by the first sensor node, and after that, the data collected by the second sensor node for the first time is normal, then the second sensor node is an observation node of the first sensor node, the central index obtaining module obtains the number Nz of times that the central node collects data in the latest preset time period and the number Ne of times that the collected data of the central node is transmitted to the cloud computing center for analysis, then the central index Z of the collected data is = Ne/Nz, the attribute value computing module computes the attribute value U = 0.36X + 0.32Y + 0.32Z of the collected data of the central node, the attribute comparing module compares the attribute value of the collected data with the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data; and when the attribute value of the acquired data is less than or equal to the attribute threshold value, the cloud computing center analyzes the acquired data of the sensing node.
The device matching module comprises a first index calculation module, a second index calculation module, a candidate index calculation module and a candidate index sorting module, wherein the first index calculation module acquires the current data volume to be processed of each edge calculation device, respectively calculates the estimated time length of each edge calculation device after the current data volume to be processed is analyzed, and normalizes the estimated time length of a certain edge calculation device to obtain a first index Q of the edge calculation device; the second index calculation module obtains the number Dz of the collected data of the sensing node historically analyzed by a certain edge calculation device and the number Dp of the analysis errors of the collected data of the sensing node, then the second index P = Dp/Dz of the edge calculation device, the candidate index calculation module calculates the candidate index V =0.58 × Q +0.42 × P of the certain edge calculation device, the candidate index sorting module sorts the candidate indexes corresponding to the edge calculation devices in the order from small to large, and the first edge calculation device is selected as the analysis device of the collected data of the sensing node.
The data acquisition and analysis system adopts a data acquisition and analysis method, and the data acquisition and analysis method comprises the following steps:
classifying all sensing nodes in advance, wherein the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node,
the pre-classifying the sensing nodes comprises:
acquiring the time interval between two adjacent acquired data of a certain sensing node,
when the time interval of the sensing node is greater than or equal to the first time interval threshold, the sensing node is a first sensing node, and when the time interval between two adjacent collected data is longer, the frequency between the collected data is lower, so that the collected data of the sensing node should be transmitted to a cloud computing center for detection, the detection precision is improved, and the abnormity of the collected data of the sensing node is found in time
When the time interval of the sensing node is less than or equal to a second time interval threshold value, the sensing node is a second sensing node, and the acquisition period of the sensing node is set according to the length of the time interval; when the time interval between two adjacent collected data is short, it indicates that the frequency between the collected data is frequent, if the collected data is transmitted to the cloud computing center for detection each time, not only the time consumption is long, but also the burden of the cloud computing center is increased, so under this condition, the collected data is transmitted to the cloud computing center once every a period of time, the data in the period of time is transmitted to the edge computing device for analysis, that is, a collection period is set according to the time interval between two adjacent collected data of the sensing node, for example, the collection period of a certain sensing node is 20s, 10 times of data collection in 20s, the previous 9 times of collected data are analyzed by the edge computing device, and the 10 th time of collected data are analyzed by the cloud computing center; in this embodiment, after the edge computing device analyzes the collected data, the collected data and the analysis result are transmitted to the cloud computing center for storage, and are also used as a reference for a subsequent cloud computing center.
When the time interval of the sensing node is smaller than a first time interval threshold value and the time intervals of the sensing node are larger than a second time interval threshold value, the sensing node is a third sensing node, wherein the first time interval threshold value is larger than the second time interval threshold value;
when a certain sensing node is a first sensing node, enabling the cloud computing center to analyze the acquired data of the sensing node;
when a certain sensing node is a second sensing node, acquiring whether the acquired data of the sensing node is the last acquired data in the acquisition period, if so, enabling the cloud computing center to analyze the acquired data of the sensing node, otherwise, enabling the edge equipment to analyze the acquired data of the sensing node;
when a certain sensing node is a third sensing node, acquiring the characteristic attribute of the acquired data of the sensing node at the time, judging whether the edge computing equipment or the cloud computing center analyzes the acquired data at the time according to the characteristic attribute,
the acquiring of the characteristic attribute of the acquired data of the sensing node comprises:
the sensing node is set as a central node,
setting a time period between the time of the central node for acquiring the data and the time of the central node for acquiring the data last time as a reference time period of the data acquisition, counting the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of the data acquisition of the central node, and counting the times Nc of data transmission to a cloud computing center, wherein the times Nc of the data acquisition are counted, and then the reference index X = Nc/Nx of the data acquisition; the computational analysis of the central node is associated with the computational analysis conditions of other sensing nodes, the analysis is carried out on the whole, the reasonability of the distribution and calculation of the collected data is further improved, when most of the other sensing nodes are transmitted to the cloud computing center, the detection precision is higher, the monitoring on the monitoring equipment is more accurate, and the central node can not be transmitted to the cloud computing center for detection;
counting the total number Ns of all the sensing nodes and the number Ng of observation nodes which are central nodes, wherein the observation index Y =1-Ng/Ns of the acquired data, if two sensor nodes exist, one sensing node is a first sensing node, the other sensing node is a second sensing node, and when the acquired data of a certain time of the first sensing node is abnormal, and the acquired data of the second sensing node is normal after that, the second sensing node is the observation node of the first sensing node; when the number of observation nodes of a certain sensing node is more, it is indicated that when the central node detects abnormality, other sensing nodes do not detect abnormality, and at this time, the central node should be transmitted to the cloud computing center as far as possible for detection, so that the detection precision is improved;
acquiring the number Nz of data collected by the central node in the latest preset time period and the number Ne of data collected by the central node and transmitted to the cloud computing center for analysis, wherein the central index Z = Ne/Nz of the data collected,
calculating the attribute value U = 0.36X + 0.32Y + 0.32Z of the data collected at the time of the central node,
when the attribute value of the acquired data is larger than the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data;
when the attribute value of the acquired data is less than or equal to the attribute threshold value, enabling the cloud computing center to analyze the acquired data of the sensing node;
when the edge device analyzes the acquired data of a certain sensing node, the appropriate edge computing device is matched to analyze the acquired data;
the analyzing the collected data by the edge computing device with the suitable matching comprises:
acquiring the current data volume to be processed of each edge computing device, respectively calculating the estimated time length of each edge computing device after the current data volume to be processed is analyzed, and performing normalization processing on the estimated time length of a certain edge computing device to obtain a first index Q of the edge computing device; the normalization processing of the estimated time length of a certain edge computing device comprises the following steps:
acquiring a maximum value Hmax and a minimum value min in the estimated time length of the current data volume to be processed after each edge computing device analyzes, and then obtaining a first index Q = (H-Hmin)/(Hmax-Hmin) obtained by normalization processing of the estimated time length H of a certain edge computing device; when the calculation analysis processing is carried out on the edge data, the timeliness of the calculation analysis is ensured as much as possible, and if the timeliness cannot be ensured, the calculation by using the edge equipment is not meaningful;
acquiring the number Dz of historical analysis of the acquired data of the sensing node by certain edge computing equipment and the number Dp of analysis errors of the acquired data of the sensing node, wherein the second index P = Dp/Dz of the edge computing equipment, different edge computing equipment has different computing characteristics, the requirements of analysis on the acquired data of different sensing nodes are different, the accuracy of edge computing is guaranteed by matching the acquired data of the sensing node with proper edge computing equipment, and the efficiency of edge computing can be improved; when a certain edge computing device analyzes that the data collected by the sensing node is abnormal, but the monitoring device normally operates within a later preset time period, the edge computing device makes an analysis error on the data collected by the sensing node, and when a certain edge computing device analyzes that the data collected by the sensing node is normal, but the monitoring device makes an abnormality within the later preset time period, the edge computing device makes an analysis error on the data collected by the sensing node;
then the candidate index V =0.58 × Q +0.42 × P for a certain edge computing device;
and sorting the candidate indexes corresponding to each edge computing device from small to large, and selecting the edge computing device with the first sorting as the analysis device of the data acquired by the sensing node at the time.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The cloud computing-based edge data acquisition and analysis system is characterized by comprising a sensing node classification module, a cloud computing center analysis module, a plurality of edge equipment analysis modules, an acquisition period analysis module, a characteristic attribute analysis module and an equipment matching module, wherein the cloud computing center analysis module is used for analyzing data acquired by sensing nodes of a cloud computing center, the edge equipment analysis module is used for analyzing data acquired by the sensing nodes of the edge equipment, the sensing node classification module classifies all the sensing nodes in advance, the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node, when a certain sensing node is the first sensing node, the cloud computing center analysis module is enabled to work, and when a certain sensing node is the second sensing node, the acquisition period analysis module judges whether the acquired data of the sensing node is the last acquired data in an acquisition period to which the sensing node belongs to If the acquired data is the third sensing node, the characteristic attribute analysis module acquires the characteristic attribute of the acquired data of the sensing node, and judges whether the edge computing device or the cloud computing center analyzes the acquired data, and the device matching module matches the proper edge computing device to analyze the acquired data when the edge device analysis module works;
the sensing node classification module comprises a time interval acquisition module and a time interval comparison module, wherein the time interval acquisition module is used for acquiring the time interval between two adjacent acquired data of a certain sensing node, the time interval comparison module is used for comparing the time interval with a first time interval threshold value and a second time interval threshold value, the sensing node is set as a first sensing node when the time interval of the sensing node is greater than or equal to the first time interval threshold value, the sensing node is set as a second sensing node when the time interval of the sensing node is less than or equal to the second time interval threshold value, the acquisition period of the sensing node is set according to the length of the time interval, the sensing node is set as a third sensing node when the time interval of the sensing node is less than the first time interval threshold value and the time interval of the sensing node is greater than the second time interval threshold value, wherein the first time interval threshold is greater than the second time interval threshold.
2. The cloud computing-based edge data collection and analysis system of claim 1, wherein: the characteristic attribute analysis module comprises a reference index acquisition module, an observation index acquisition module, a center index acquisition module, an attribute value calculation module and an attribute comparison module, wherein the reference index acquisition module is used for setting the sensing node as a center node, setting a time period between the time of acquiring data and the time of acquiring data last time of the center node as a reference time period of acquiring data, counting the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of acquiring data of the center node, and counting the times Nc of transmitting the acquired data to the cloud computing center, so that the reference index X = Nc/Nx of the acquired data, the observation index acquisition module is used for counting the total number Ns of all the sensing nodes and the number Ng of the observation nodes which are the center node, so that the observation index Y = 1-Ng/Ns/of the acquired data, wherein, if there are two sensor nodes, one of the sensor nodes is a first sensor node, the other sensor node is a second sensor node, when there is an abnormality in the data collected by the first sensor node, and after that, the data collected by the second sensor node for the first time is normal, then the second sensor node is an observation node of the first sensor node, the central index obtaining module obtains the number Nz of times that the central node collects data in the latest preset time period and the number Ne of times that the collected data of the central node is transmitted to the cloud computing center for analysis, then the central index Z of the collected data is = Ne/Nz, the attribute value computing module computes the attribute value U = 0.36X + 0.32Y + 0.32Z of the collected data of the central node, the attribute comparing module compares the attribute value of the collected data with the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data; and when the attribute value of the acquired data is less than or equal to the attribute threshold value, the cloud computing center analyzes the acquired data of the sensing node.
3. The cloud computing-based edge data collection and analysis system of claim 2, wherein: the device matching module comprises a first index calculation module, a second index calculation module, a candidate index calculation module and a candidate index sorting module, wherein the first index calculation module acquires the current data volume to be processed of each edge calculation device, respectively calculates the estimated time length of each edge calculation device after the current data volume to be processed is analyzed, and normalizes the estimated time length of a certain edge calculation device to obtain a first index Q of the edge calculation device; the second index calculation module obtains the number Dz of the collected data of the sensing node historically analyzed by a certain edge calculation device and the number Dp of the analysis errors of the collected data of the sensing node, then the second index P = Dp/Dz of the edge calculation device, the candidate index calculation module calculates the candidate index V =0.58 × Q +0.42 × P of the certain edge calculation device, the candidate index sorting module sorts the candidate indexes corresponding to the edge calculation devices in the order from small to large, and the first edge calculation device is selected as the analysis device of the collected data of the sensing node.
4. The cloud computing-based edge data collection and analysis system of claim 1, wherein: the data acquisition and analysis system adopts a data acquisition and analysis method, and the data acquisition and analysis method comprises the following steps:
classifying all sensing nodes in advance, wherein the types of the sensing nodes comprise a first sensing node, a second sensing node and a third sensing node,
when a certain sensing node is a first sensing node, enabling the cloud computing center to analyze the acquired data of the sensing node;
when a certain sensing node is a second sensing node, acquiring whether the acquired data of the sensing node is the last acquired data in the acquisition period, if so, enabling the cloud computing center to analyze the acquired data of the sensing node, otherwise, enabling the edge equipment to analyze the acquired data of the sensing node;
when a certain sensing node is a third sensing node, acquiring the characteristic attribute of the acquired data of the sensing node at the time, judging whether the edge computing equipment or the cloud computing center analyzes the acquired data at the time according to the characteristic attribute,
when the edge device analyzes the collected data of a certain sensing node, the proper edge computing device is matched to analyze the collected data.
5. The cloud computing-based edge data collection and analysis system of claim 4, wherein: the pre-classifying the sensing nodes comprises:
acquiring the time interval between two adjacent acquired data of a certain sensing node,
when the time interval of the sensing node is larger than or equal to the first time interval threshold value, the sensing node is a first sensing node,
when the time interval of the sensing node is less than or equal to a second time interval threshold value, the sensing node is a second sensing node, and the acquisition period of the sensing node is set according to the length of the time interval;
when the time interval of the sensing node is smaller than the first time interval threshold value and the time intervals of the sensing node are larger than the second time interval threshold value, the sensing node is a third sensing node, wherein the first time interval threshold value is larger than the second time interval threshold value.
6. The cloud computing-based edge data collection and analysis system of claim 5, wherein: the acquiring of the characteristic attribute of the acquired data of the sensing node comprises:
the sensing node is set as a central node,
setting a time period between the time of the central node for acquiring the data and the time of the central node for acquiring the data last time as a reference time period of the data acquisition, counting the times Nx of data acquisition and transmission of other sensing nodes in the reference time period of the data acquisition of the central node, and counting the times Nc of data transmission to a cloud computing center, wherein the times Nc of the data acquisition are counted, and then the reference index X = Nc/Nx of the data acquisition;
counting the total number Ns of all the sensing nodes and the number Ng of observation nodes which are central nodes, wherein the observation index Y =1-Ng/Ns of the acquired data, if two sensor nodes exist, one sensing node is a first sensing node, the other sensing node is a second sensing node, and when the acquired data of a certain time of the first sensing node is abnormal, and the acquired data of the second sensing node is normal after that, the second sensing node is the observation node of the first sensing node;
acquiring the number Nz of data collected by the central node in the latest preset time period and the number Ne of data collected by the central node and transmitted to the cloud computing center for analysis, wherein the central index Z = Ne/Nz of the data collected,
calculating the attribute value U = 0.36X + 0.32Y + 0.32Z of the data collected at the time of the central node,
when the attribute value of the acquired data is larger than the attribute threshold value, enabling the edge device to analyze the acquired data of the sensing node, and matching with a proper edge computing device to analyze the acquired data;
and when the attribute value of the acquired data is less than or equal to the attribute threshold value, the cloud computing center analyzes the acquired data of the sensing node.
7. The cloud computing-based edge data collection and analysis system of claim 4, wherein: the analyzing the collected data by the edge computing device with the suitable matching comprises:
acquiring the current data volume to be processed of each edge computing device, respectively calculating the estimated time length of each edge computing device after the current data volume to be processed is analyzed, and performing normalization processing on the estimated time length of a certain edge computing device to obtain a first index Q of the edge computing device;
acquiring the times Dz of historical analysis of the collected data of the sensing node and the times Dp of analysis errors of the collected data of the sensing node by a certain edge computing device, and then the second index P = Dp/Dz of the edge computing device,
then the candidate index V =0.58 × Q +0.42 × P for a certain edge computing device;
and sorting the candidate indexes corresponding to each edge computing device from small to large, and selecting the edge computing device with the first sorting as the analysis device of the data acquired by the sensing node at the time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111372713.7A CN113810792B (en) | 2021-11-19 | 2021-11-19 | Edge data acquisition and analysis system based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111372713.7A CN113810792B (en) | 2021-11-19 | 2021-11-19 | Edge data acquisition and analysis system based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113810792A CN113810792A (en) | 2021-12-17 |
| CN113810792B true CN113810792B (en) | 2022-02-18 |
Family
ID=78938470
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111372713.7A Active CN113810792B (en) | 2021-11-19 | 2021-11-19 | Edge data acquisition and analysis system based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113810792B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114024967B (en) * | 2022-01-10 | 2022-03-25 | 广东电力信息科技有限公司 | IAAS data processing system and method based on cloud edge and edge cooperative architecture |
| CN117335570B (en) * | 2023-10-09 | 2024-06-21 | 国网河南省电力公司濮阳供电公司 | Visual monitoring system and method for panoramic information of elastic power distribution network |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107172128A (en) * | 2017-04-24 | 2017-09-15 | 华南理工大学 | A kind of manufacturing equipment big data acquisition method of cloud auxiliary |
| CN110334157A (en) * | 2019-07-15 | 2019-10-15 | 南昌诺汇医药科技有限公司 | A kind of cloud computing management system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10931593B2 (en) * | 2016-03-30 | 2021-02-23 | Nec Corporation | Management node, management system, management method and computer-readable recording medium |
| US11025711B2 (en) * | 2019-05-02 | 2021-06-01 | EMC IP Holding Company LLC | Data centric resource management for edge cloud systems |
| CN111427679B (en) * | 2020-03-25 | 2023-12-22 | 中国科学院自动化研究所 | Computing task scheduling method, system and device for edge computing |
| CN112003679A (en) * | 2020-08-27 | 2020-11-27 | 蘑菇物联技术（深圳）有限公司 | Edge monitoring and cloud monitoring based dual-mode monitoring method for general industrial equipment |
| CN112162849A (en) * | 2020-08-28 | 2021-01-01 | 中冶华天工程技术有限公司 | Cloud and edge computing cooperative equipment intelligent control system and method |
| CN112217876B (en) * | 2020-09-22 | 2023-09-19 | 远光软件股份有限公司 | Internet of things system, internet of things communication method and electronic equipment |
-
2021
- 2021-11-19 CN CN202111372713.7A patent/CN113810792B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107172128A (en) * | 2017-04-24 | 2017-09-15 | 华南理工大学 | A kind of manufacturing equipment big data acquisition method of cloud auxiliary |
| CN110334157A (en) * | 2019-07-15 | 2019-10-15 | 南昌诺汇医药科技有限公司 | A kind of cloud computing management system |
Non-Patent Citations (1)
| Title |
|---|
| 基于边缘云计算的人体数据传感架构;孙二华等;《西南师范大学学报(自然科学版)》;20200716(第07期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113810792A (en) | 2021-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111061620B (en) | Intelligent detection method and detection system for server abnormity of mixed strategy | |
| CN109034244B (en) | Line loss abnormity diagnosis method and device based on electric quantity curve characteristic model | |
| CN113810792B (en) | Edge data acquisition and analysis system based on cloud computing | |
| CN111459778A (en) | Operation and maintenance system abnormal index detection model optimization method and device and storage medium | |
| CN116304766B (en) | Multi-sensor-based quick assessment method for state of switch cabinet | |
| CN108173670A (en) | The method and apparatus for detecting network | |
| CN109947815B (en) | Power theft identification method based on outlier algorithm | |
| CN113591393B (en) | Fault diagnosis method, device, equipment and storage medium of intelligent substation | |
| CN105871634A (en) | Method and application for detecting cluster anomalies and cluster managing system | |
| CN115865649B (en) | Intelligent operation and maintenance management control method, system and storage medium | |
| CN113125903A (en) | Line loss anomaly detection method, device, equipment and computer-readable storage medium | |
| CN110413703A (en) | The classification method and relevant device of monitor control index data based on artificial intelligence | |
| CN111984442A (en) | Method and device for detecting abnormality of computer cluster system, and storage medium | |
| CN113592343A (en) | Fault diagnosis method, device, equipment and storage medium of secondary system | |
| CN115936428A (en) | External damage prevention fixed value optimization system for incremental power distribution network | |
| CN113391256B (en) | Electric energy meter metering fault analysis method and system of field operation terminal | |
| CN112418687B (en) | User electricity utilization abnormity identification method and device based on electricity utilization characteristics and storage medium | |
| CN116681186B (en) | Power quality analysis method and device based on intelligent terminal | |
| CN115794578A (en) | Data management method, device, equipment and medium for power system | |
| CN117972618A (en) | Method and system for detecting secondary power failure of hybrid integrated circuit | |
| CN112070180A (en) | Power grid equipment state judgment method and device based on information physical bilateral data | |
| CN116028887A (en) | Analysis method of continuous industrial production data | |
| CN118152836A (en) | Stability evaluation method for operation process of electric energy meter | |
| CN110956281A (en) | Power equipment abnormity detection alarm system based on Log analysis | |
| CN117540225A (en) | Distributed ups system consistency assessment system and method based on DBSCAN clustering |
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 | ||
| CP03 | Change of name, title or address |
Address after: 10th Floor, Building C, Shimaochengpin International Plaza, No. 62 Andemen Street, Yuhuatai District, Nanjing City, Jiangsu Province, 210012 Patentee after: Nanjing Jiangmen Information Technology Co.,Ltd. Address before: 210000 No.2 tulip Road, Yuhuatai District, Nanjing City, Jiangsu Province Patentee before: NANJING REDOOR INFORMATION TECHNOLOGY Co.,Ltd. |
|
| CP03 | Change of name, title or address |