CN112445673A - Edge device processing method and device, storage medium and processor - Google Patents
Edge device processing method and device, storage medium and processor Download PDFInfo
- Publication number
- CN112445673A CN112445673A CN201910817943.6A CN201910817943A CN112445673A CN 112445673 A CN112445673 A CN 112445673A CN 201910817943 A CN201910817943 A CN 201910817943A CN 112445673 A CN112445673 A CN 112445673A
- Authority
- CN
- China
- Prior art keywords
- data
- sliding window
- slope value
- edge device
- sensor data
- 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
- 238000003672 processing method Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 47
- 230000002159 abnormal effect Effects 0.000 claims abstract description 44
- 238000012545 processing Methods 0.000 claims abstract description 41
- 238000004364 calculation method Methods 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 15
- 238000005516 engineering process Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 6
- 238000013459 approach Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- Mathematical Physics (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The application discloses a processing method and device of edge equipment, a storage medium and a processor. The method comprises the following steps: inputting the acquired sensor data of the edge device into a computing engine; performing windowing processing on the sensor data through a stream computing engine to obtain a plurality of sliding windows comprising data; calculating a slope value according to the data in each sliding window; determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value. By the method and the device, the problem that the abnormal condition of the running state of the edge equipment is difficult to determine in time in the related technology is solved.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a processing method and apparatus for an edge device, a storage medium, and a processor.
Background
The edge computing is originated in the field of media, and means that an open platform integrating network, computing, storage and application core capabilities is adopted on one side close to an object or a data source to provide nearest-end service nearby. 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. The edge computation is between the physical entity and the industrial connection, or on top of the physical entity. And the cloud computing still can access the historical data of the edge computing. Real-time analysis is an important application scenario of edge calculation, and aiming at the abnormal condition of the running state of edge equipment, the abnormal condition that the running state of the edge equipment is difficult to determine in time often occurs or the alarm processing aiming at the abnormal condition is not timely, so that subsequent unplanned shutdown is caused.
Aiming at the problem that the abnormal condition of the running state of the edge equipment is difficult to determine in time in the related technology, an effective solution is not provided at present.
Disclosure of Invention
The present application mainly aims to provide a processing method and apparatus for an edge device, a storage medium, and a processor, so as to solve the problem in the related art that it is difficult to determine an abnormal condition of an operating state of the edge device in time.
In order to achieve the above object, according to one aspect of the present application, there is provided a processing method of an edge device. The method comprises the following steps: inputting the acquired sensor data of the edge device into a computing engine; performing windowing processing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows comprising data; calculating a slope value according to the data in each sliding window; determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value.
Further, calculating the slope value from the data in each sliding window comprises: acquiring the maximum value and the minimum value of each sliding window included data; acquiring the window length of each sliding window; the slope value for each sliding window is calculated based on the window length for each sliding window, the maximum and minimum values in the data for each sliding window.
Further, determining whether the operational state of the edge device is in an abnormal state based on the calculated slope value includes: judging whether the slope value calculated by each sliding window is greater than a preset slope value or not; and if the slope value of the sliding window is larger than the preset slope value, determining that the running state of the edge equipment is in an abnormal state.
Further, after determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value, the method further includes: and if the running state of the edge equipment is determined to be in an abnormal state, triggering reminding information to remind a target object.
Further, windowing the sensor data by the stream computation engine to obtain a plurality of sliding windows comprising data comprises: determining a time interval for windowing the sensor data; and performing windowing processing on the sensor data according to the time interval through the stream calculation engine to obtain a plurality of sliding windows comprising data.
Further, the sensor data includes at least one of: vibration characteristic value data and process quantity data.
In order to achieve the above object, according to an aspect of the present application, there is provided a processing apparatus of an edge device, including: the input unit is used for inputting the acquired sensor data of the edge device into the stream computing engine; the acquisition unit is used for performing windowing processing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows comprising data; a calculation unit for calculating a slope value from the data in each sliding window; a determination unit for determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value.
Further, the calculation unit further includes: the first acquisition module is used for acquiring the maximum value and the minimum value of each sliding window included data; the second acquisition module is used for acquiring the window length of each sliding window; and the calculating module is used for calculating the slope value of each sliding window based on the window length of each sliding window and the maximum value and the minimum value in the data of each sliding window.
In order to achieve the above object, according to one aspect of the present application, there is provided a storage medium characterized in that the storage medium includes a stored program, wherein the program executes the processing method of the edge device according to any one of the above.
To achieve the above object, according to one aspect of the present application, there is provided a processor, wherein the processor is configured to execute a program, wherein the program executes to perform the processing method of the edge device according to any one of the above items.
Through the application, the following steps are adopted: inputting the acquired sensor data of the edge device into a computing engine; performing windowing processing on the sensor data through a stream computing engine to obtain a plurality of sliding windows comprising data; calculating a slope value according to the data in each sliding window; whether the running state of the edge equipment is in an abnormal state or not is determined based on the calculated slope value, and the problem that the abnormal condition of the running state of the edge equipment is difficult to determine in time in the related technology is solved. And determining whether the running state of the edge equipment is in an abnormal state or not based on the slope value calculated for the data in the sliding window, thereby achieving the effect of improving the timeliness of determining the abnormal condition of the running state of the edge equipment.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a processing method of an edge device according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a sensor data input stream calculation engine provided in accordance with an embodiment of the present application;
FIG. 3 is a schematic illustration of a windowing process performed on sensor data provided in accordance with an embodiment of the application;
FIG. 4 is a schematic diagram of a processing device of an edge device provided according to an embodiment of the present application; and
fig. 5 is a block diagram of an apparatus provided according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of description, some terms or expressions referred to in the embodiments of the present application are explained below:
flow computation, in a conventional data processing flow, data is always collected first and then put into the DB. When people need to do query to the data through the DB, the answer is obtained or relevant processing is carried out. Although this seems reasonable, the results are very compact, especially in some real-time search application environments where off-line processing like the Map Reduce approach does not solve the problem well. This leads to a new data computation structure-the stream computation approach. It can analyze large-scale flow data in real time in the changing movement process, capture possibly useful information and send the result to the next computing node.
According to an embodiment of the present application, there is provided a processing method of an edge device.
Fig. 1 is a flowchart of a processing method of an edge device according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S101, inputting the collected sensor data of the edge device into a computing engine.
Optionally, in the processing method of the edge device provided in the embodiment of the present application, the sensor data includes at least one of: vibration characteristic value data and process quantity data.
The process amount data includes temperature data, pressure data, and humidity data, for example, when the sensor data is temperature data, the acquired temperature data is: 2,5,9,8,8,3,4,2,2,5,1,1,1,2,5,9. The collected temperatures are input into a schematic diagram of a computational engine, as shown in FIG. 2.
Step S102, performing windowing on the sensor data by the stream calculation engine to obtain a plurality of sliding windows including data.
Optionally, in the processing method of the edge device provided in the embodiment of the present application, performing windowing on the sensor data by the stream calculation engine to obtain a plurality of sliding windows including data includes: determining a time interval for windowing the sensor data; and performing windowing processing on the sensor data according to the time interval through the stream calculation engine to obtain a plurality of sliding windows comprising data.
For example, the time interval for performing the windowing process on the sensor data is 2s, and if 4 pieces of data are acquired within 2s, the windowing process is performed on the acquired temperature data as shown in fig. 3. That is, the stream calculation engine slides the sensing data into different sliding windows according to time intervals to obtain a plurality of sliding windows including data. For example, sliding window 1 includes data 2, 5, 9, 8; the sliding window 2 comprises data 9, 8, 8, 3; the sliding window 3 comprises data 8, 3, 4, 2; and so on.
Step S103, calculating a slope value according to the data in each sliding window.
Optionally, in the processing method of the edge device provided in the embodiment of the present application, calculating a slope value according to the data in each sliding window includes: acquiring the maximum value and the minimum value of each sliding window included data; acquiring the window length of each sliding window; the slope value for each sliding window is calculated based on the window length for each sliding window, the maximum and minimum values in the data for each sliding window.
For example, the sliding window 1 includes data having a maximum value of 9, a minimum value of 2, a window length of 4, and a slope value of (9-2)/4 of 7/4.
And step S104, determining whether the running state of the edge equipment is in an abnormal state or not based on the calculated slope value.
For example, whether the slope value calculated by each sliding window is greater than a preset slope value is judged; and if the slope value of the sliding window is larger than the preset slope value, determining that the running state of the edge equipment is in an abnormal state.
For example, the preset slope value is 2, and if the calculated slope value of the sliding window is greater than 2, it is determined that the running state of the edge device is in an abnormal state.
And triggering reminding information to remind the target object when the running state of the edge device is determined to be in the abnormal state. Therefore, the target object can timely acquire the running state of the edge equipment and timely perform corresponding processing on the edge equipment.
By adopting the technical means and the time sliding window combined with the flow calculation to carry out the slope analysis method, the abnormal condition of the running state of the edge equipment can be determined in time, so that the condition that the subsequent unplanned shutdown is caused by the fact that the abnormal condition of the running state of the edge equipment cannot be obtained is avoided.
To sum up, in the processing method of the edge device provided in the embodiment of the present application, the acquired sensor data of the edge device is input into the computing engine; performing windowing processing on the sensor data through a stream computing engine to obtain a plurality of sliding windows comprising data; calculating a slope value according to the data in each sliding window; whether the running state of the edge equipment is in an abnormal state or not is determined based on the calculated slope value, and the problem that the abnormal condition of the running state of the edge equipment is difficult to determine in time in the related technology is solved. And determining whether the running state of the edge equipment is in an abnormal state or not based on the slope value calculated for the data in the sliding window, thereby achieving the effect of improving the timeliness of determining the abnormal condition of the running state of the edge equipment.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment of the present application further provides a processing apparatus for an edge device, and it should be noted that the processing apparatus for an edge device in the embodiment of the present application may be used to execute the processing method for an edge device provided in the embodiment of the present application. The following describes a processing apparatus of an edge device according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a processing device of an edge device according to an embodiment of the present application. As shown in fig. 4, the apparatus includes: an input unit 201, an acquisition unit 202, a calculation unit 203, and a determination unit 204.
Specifically, the input unit 201 is configured to input the acquired sensor data of the edge device into the stream computation engine;
an acquiring unit 202, configured to perform windowing on the sensor data by using the stream calculation engine to obtain a plurality of sliding windows including data;
a calculating unit 203 for calculating a slope value according to the data in each sliding window;
a determining unit 204, configured to determine whether the operating state of the edge device is in an abnormal state based on the calculated slope value.
Optionally, in the processing apparatus of the edge device provided in the embodiment of the present application, the calculating unit 203 further includes: the first acquisition module is used for acquiring the maximum value and the minimum value of each sliding window included data; the second acquisition module is used for acquiring the window length of each sliding window; and the calculating module is used for calculating the slope value of each sliding window based on the window length of each sliding window and the maximum value and the minimum value in the data of each sliding window.
Optionally, in the processing apparatus of the edge device provided in the embodiment of the present application, the determining unit 204 includes: the judging module is used for judging whether the slope value calculated by each sliding window is greater than a preset slope value or not; and the first determining module is used for determining that the running state of the edge equipment is in an abnormal state under the condition that the slope value of the sliding window is larger than the preset slope value.
Optionally, in the processing apparatus of an edge device provided in this embodiment of the present application, the apparatus further includes: and the reminding unit is used for triggering reminding information to remind a target object under the condition that the running state of the edge equipment is determined to be in the abnormal state after determining whether the running state of the edge equipment is in the abnormal state based on the calculated slope value.
Optionally, in the processing apparatus of the edge device provided in the embodiment of the present application, the obtaining unit 202 further includes: a second determination module to determine a time interval for windowing the sensor data; and the third acquisition module is used for performing windowing processing on the sensor data through the stream calculation engine according to the time interval to obtain a plurality of sliding windows comprising data.
Optionally, in the processing apparatus of the edge device provided in this embodiment of the present application, the sensor data includes at least one of: vibration characteristic value data and process quantity data.
The processing device of the edge device provided by the embodiment of the application inputs the acquired sensor data of the edge device into the flow calculation engine through the input unit 201; the obtaining unit 202 performs windowing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows including data; the calculation unit 203 calculates a slope value according to the data in each sliding window; the determining unit 204 determines whether the running state of the edge device is in an abnormal state based on the calculated slope value, so as to solve the problem that it is difficult to determine the abnormal condition of the running state of the edge device in time in the related art.
The processing device of the edge device comprises a processor and a memory, wherein the input unit 201, the acquisition unit 202, the calculation unit 203, the determination unit 204 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can set one or more, and the edge device is processed by adjusting the kernel parameters.
An embodiment of the present invention provides a storage medium on which a program is stored, the program implementing the processing method of the edge device when being executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the processing method of the edge device is executed when the program runs.
An embodiment of the present invention provides an apparatus, as shown in fig. 5, the apparatus includes at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through a bus; the processor is used for calling the program instructions in the memory so as to execute the processing method of the edge device. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device: inputting the acquired sensor data of the edge device into a computing engine; performing windowing processing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows comprising data; calculating a slope value according to the data in each sliding window; determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value.
When executed on a data processing device, is further adapted to perform a procedure for initializing the following method steps: calculating a slope value from the data in each sliding window comprises: acquiring the maximum value and the minimum value of each sliding window included data; acquiring the window length of each sliding window; the slope value for each sliding window is calculated based on the window length for each sliding window, the maximum and minimum values in the data for each sliding window.
When executed on a data processing device, is further adapted to perform a procedure for initializing the following method steps: determining whether the operational state of the edge device is in an abnormal state based on the calculated slope value includes: judging whether the slope value calculated by each sliding window is greater than a preset slope value or not; and if the slope value of the sliding window is larger than the preset slope value, determining that the running state of the edge equipment is in an abnormal state.
When executed on a data processing device, is further adapted to perform a procedure for initializing the following method steps: after determining whether the operational state of the edge device is in an abnormal state based on the calculated slope value, the method further includes: and if the running state of the edge equipment is determined to be in an abnormal state, triggering reminding information to remind a target object.
When executed on a data processing device, is further adapted to perform a procedure for initializing the following method steps: windowing the sensor data by the stream computation engine to obtain a plurality of sliding windows comprising data comprises: determining a time interval for windowing the sensor data; and performing windowing processing on the sensor data according to the time interval through the stream calculation engine to obtain a plurality of sliding windows comprising data.
When executed on a data processing device, is further adapted to perform a procedure for initializing the following method steps: the sensor data includes at least one of: vibration characteristic value data and process quantity data.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of processing an edge device, comprising:
inputting the acquired sensor data of the edge device into a computing engine;
performing windowing processing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows comprising data;
calculating a slope value according to the data in each sliding window;
determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value.
2. The method of claim 1, wherein calculating a slope value from the data in each sliding window comprises:
acquiring the maximum value and the minimum value of each sliding window included data;
acquiring the window length of each sliding window;
the slope value for each sliding window is calculated based on the window length for each sliding window, the maximum and minimum values in the data for each sliding window.
3. The method of claim 1, wherein determining whether the operational state of the edge device is in an abnormal state based on the calculated slope value comprises:
judging whether the slope value calculated by each sliding window is greater than a preset slope value or not;
and if the slope value of the sliding window is larger than the preset slope value, determining that the running state of the edge equipment is in an abnormal state.
4. The method of claim 1, wherein after determining whether the operational state of the edge device is in an abnormal state based on the calculated slope value, the method further comprises:
and if the running state of the edge equipment is determined to be in an abnormal state, triggering reminding information to remind a target object.
5. The method of claim 1, wherein windowing the sensor data by the stream computation engine to obtain a plurality of sliding windows comprising data comprises:
determining a time interval for windowing the sensor data;
and performing windowing processing on the sensor data according to the time interval through the stream calculation engine to obtain a plurality of sliding windows comprising data.
6. The method of claim 1, wherein the sensor data comprises at least one of: vibration characteristic value data and process quantity data.
7. A processing apparatus for an edge device, comprising:
the input unit is used for inputting the acquired sensor data of the edge device into the stream computing engine;
the acquisition unit is used for performing windowing processing on the sensor data through the stream calculation engine to obtain a plurality of sliding windows comprising data;
a calculation unit for calculating a slope value from the data in each sliding window;
a determination unit for determining whether the operation state of the edge device is in an abnormal state based on the calculated slope value.
8. The apparatus of claim 7, wherein the computing unit further comprises:
the first acquisition module is used for acquiring the maximum value and the minimum value of each sliding window included data;
the second acquisition module is used for acquiring the window length of each sliding window;
and the calculating module is used for calculating the slope value of each sliding window based on the window length of each sliding window and the maximum value and the minimum value in the data of each sliding window.
9. A storage medium characterized by comprising a stored program, wherein the program executes the processing method of the edge device according to any one of claims 1 to 6.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the processing method of the edge device according to any one of claims 1 to 6 when running.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910817943.6A CN112445673A (en) | 2019-08-30 | 2019-08-30 | Edge device processing method and device, storage medium and processor |
PCT/CN2020/098595 WO2021036465A1 (en) | 2019-08-30 | 2020-06-28 | Edge device processing method and apparatus, storage medium, and processor |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910817943.6A CN112445673A (en) | 2019-08-30 | 2019-08-30 | Edge device processing method and device, storage medium and processor |
Publications (1)
Publication Number | Publication Date |
---|---|
CN112445673A true CN112445673A (en) | 2021-03-05 |
Family
ID=74684516
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910817943.6A Pending CN112445673A (en) | 2019-08-30 | 2019-08-30 | Edge device processing method and device, storage medium and processor |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN112445673A (en) |
WO (1) | WO2021036465A1 (en) |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105553787A (en) * | 2016-03-01 | 2016-05-04 | 清华大学 | Edge network exit network flow abnormality detection method and system based on Hadoop |
CN108353090A (en) * | 2015-08-27 | 2018-07-31 | 雾角系统公司 | Edge intelligence platform and internet of things sensors streaming system |
CN108509990A (en) * | 2018-03-29 | 2018-09-07 | 重庆大学 | A kind of sequential key assignments type industrial process data Parallel analytic method |
CN108804668A (en) * | 2018-06-08 | 2018-11-13 | 珠海格力智能装备有限公司 | Data processing method and device |
CN109241129A (en) * | 2018-07-27 | 2019-01-18 | 山东大学 | A kind of Model of Time Series Streaming dimensionality reduction based on Feature Segmentation and simplified representation method |
CN109981372A (en) * | 2019-04-03 | 2019-07-05 | 华南理工大学 | Streaming big data processing method and system based on edge calculations |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104075749A (en) * | 2014-06-30 | 2014-10-01 | 通号通信信息集团有限公司 | Abnormal state detecting method and system for equipment in internet of things |
US10073507B2 (en) * | 2015-09-22 | 2018-09-11 | Intersil Americas LLC | Method and system for reducing transients in DC-DC converters |
CN106909664A (en) * | 2017-02-28 | 2017-06-30 | 国网福建省电力有限公司 | A kind of power equipment data stream failure recognition methods |
CN109800129A (en) * | 2019-01-17 | 2019-05-24 | 青岛特锐德电气股份有限公司 | A kind of real-time stream calculation monitoring system and method for processing monitoring big data |
-
2019
- 2019-08-30 CN CN201910817943.6A patent/CN112445673A/en active Pending
-
2020
- 2020-06-28 WO PCT/CN2020/098595 patent/WO2021036465A1/en active Application Filing
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108353090A (en) * | 2015-08-27 | 2018-07-31 | 雾角系统公司 | Edge intelligence platform and internet of things sensors streaming system |
CN105553787A (en) * | 2016-03-01 | 2016-05-04 | 清华大学 | Edge network exit network flow abnormality detection method and system based on Hadoop |
CN108509990A (en) * | 2018-03-29 | 2018-09-07 | 重庆大学 | A kind of sequential key assignments type industrial process data Parallel analytic method |
CN108804668A (en) * | 2018-06-08 | 2018-11-13 | 珠海格力智能装备有限公司 | Data processing method and device |
CN109241129A (en) * | 2018-07-27 | 2019-01-18 | 山东大学 | A kind of Model of Time Series Streaming dimensionality reduction based on Feature Segmentation and simplified representation method |
CN109981372A (en) * | 2019-04-03 | 2019-07-05 | 华南理工大学 | Streaming big data processing method and system based on edge calculations |
Non-Patent Citations (3)
Title |
---|
屈志坚,王冬: "面向智能调度监测的流计算并行滑动窗口技术", 《电网技术》 * |
张琪等: "边缘计算应用:传感数据异常实时检测算法", 《计算机研究与发展》 * |
翁颖钧,石来德: "《数据挖掘建模及其在电力决策支持中的应用研究》", 31 October 2018, 上海:同济大学出版社 * |
Also Published As
Publication number | Publication date |
---|---|
WO2021036465A1 (en) | 2021-03-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112448861B (en) | Edge device processing method and device, storage medium and processor | |
CN109561052B (en) | Method and device for detecting abnormal flow of website | |
CN109947079A (en) | Region method for detecting abnormality and edge calculations equipment based on edge calculations | |
CN112580914A (en) | Method and device for realizing enterprise-level data middling platform system for collecting multi-source data | |
CN105607986A (en) | Acquisition method and device of user behavior log data | |
CN108306846B (en) | Network access abnormity detection method and system | |
CN113312361B (en) | Track query method, device, equipment, storage medium and computer program product | |
CN109934074B (en) | Action track determining method and device | |
CN112486104B (en) | Method and device for analyzing equipment abnormity based on real-time acquisition of sensing data | |
CN111488835A (en) | Method and device for identifying fellow persons | |
CN109873790A (en) | Network security detection method, device and computer readable storage medium | |
CN112583944B (en) | Processing method and device for updating domain name certificate | |
CN111147313B (en) | Message abnormity monitoring method and device, storage medium and electronic equipment | |
CN112572522A (en) | Early warning method and device for axle temperature fault of vehicle bearing | |
CN113641526A (en) | Alarm root cause positioning method and device, electronic equipment and computer storage medium | |
CN113468384B (en) | Processing method, device, storage medium and processor for network information source information | |
CN110675028A (en) | Block chain-based food safety supervision method, device, equipment and system | |
CN109992470B (en) | Threshold value adjusting method and device | |
CN109976986B (en) | Abnormal equipment detection method and device | |
CN113123955B (en) | Plunger pump abnormity detection method and device, storage medium and electronic equipment | |
CN112445673A (en) | Edge device processing method and device, storage medium and processor | |
CN112526905A (en) | Processing method and system for index abnormity | |
CN112153051A (en) | Information processing method and system based on Internet of things and cloud computing | |
CN109597743B (en) | Page circling method, click rate statistical method and related equipment | |
CN109598525B (en) | Data processing method and device |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210305 |