CN110674124B - Abnormal data detection method and system and intelligent router - Google Patents
Abnormal data detection method and system and intelligent router Download PDFInfo
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Abstract
The invention relates to an abnormal data detection method, a system and an intelligent router, wherein the system comprises the following steps: at least one bottom-level device, and a top-level management system; the edge computing network is used for acquiring the operation data of the bottom layer equipment, identifying abnormal data from the operation data according to a preset algorithm and sending the abnormal data to the top layer management system; wherein the exception data comprises: time domain anomaly data, and frequency domain anomaly data. According to the technical scheme provided by the invention, the edge computing network identifies abnormal data from the operation data by acquiring the operation data of the bottom layer equipment and according to a preset algorithm, so that the detection of time domain and frequency domain abnormal data is realized.
Description
Technical Field
The invention relates to the technical field of intelligent detection, in particular to an abnormal data detection method, an abnormal data detection system and an intelligent router.
Background
The abnormal data refers to a small number of data points in the data set, and the data points often contain important information and cannot be directly ignored or deleted, but the characterization of the data points should be analyzed to obtain the reason for generating the abnormal data for subsequent decision-making and guidance work. The method is also valuable for detecting abnormal data of the energy equipment, because the energy equipment is influenced by various abnormal events such as load, overvoltage, internal insulation aging, natural environment and the like in the actual operation process, and if the abnormality cannot be found in time, equipment defects and faults can be caused.
Disclosure of Invention
In view of the above, the present invention provides an abnormal data detection method, system and intelligent router, so as to solve the problem in the prior art that the abnormal data of the underlying device cannot be automatically detected.
According to a first aspect of embodiments of the present invention, there is provided an abnormal data detecting system, including:
at least one bottom-level device, and a top-level management system;
the edge computing network is used for acquiring the operation data of the bottom layer equipment, identifying abnormal data from the operation data according to a preset algorithm and sending the abnormal data to the top layer management system; wherein the exception data comprises: time domain anomaly data, and frequency domain anomaly data.
Preferably, the edge computing network is further configured to determine an exception level of the underlying device according to the exception data, and select different exception handling schemes according to the exception level.
Preferably, the edge computing network comprises:
at least one project centralized controller, and at least one intelligent router;
the intelligent router is connected with the bottom layer equipment according to the grouping of the bottom layer equipment;
the project centralized controller is connected with the intelligent router according to the division of the project scope;
and all the project centralized controllers are connected with the top-level management system.
Preferably, if there are multiple intelligent routers in the same project, the multiple intelligent routers are connected through a gigabit network.
Preferably, the project centralized controller is connected with the top management system through an optical fiber;
the project centralized controller is connected with the intelligent router through a gigabit network;
the intelligent router is connected with the bottom layer equipment through a CAN bus protocol.
According to a second aspect of the embodiments of the present invention, there is provided an abnormal data detecting method, including:
acquiring operation data of bottom equipment;
according to a preset algorithm, identifying abnormal data from the operating data, and sending the abnormal data to a top management system; the exception data includes: time domain anomaly data, and frequency domain anomaly data.
Preferably, the acquiring the operation data of the underlying device includes:
reading the operation data of the bottom-layer equipment from a database of the top-layer management system; and/or the presence of a gas in the gas,
and receiving the operation data sent by the bottom layer equipment in real time.
Preferably, the identifying abnormal data from the operation data includes:
directly identifying time domain abnormal data of a first preset characteristic parameter from the operation data; and converting the time sequence value of the second preset characteristic parameter in the operating data into a frequency domain value, and identifying frequency domain abnormal data from the frequency domain value.
Preferably, the first preset characteristic parameter includes: voltage, current, power, temperature;
the second preset characteristic parameter includes: voltage, current.
Preferably, the preset algorithm includes: and (5) data dimension-increasing and clustering algorithm.
Preferably, the method further comprises:
performing data preprocessing on the operating data;
the identifying of the abnormal data from the operation data specifically comprises:
and identifying abnormal data from the preprocessed running data.
Preferably, the method further comprises:
sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation;
and if the other intelligent routers with the preset proportional quantity secondarily confirm that the other intelligent routers are abnormal data, finally judging the abnormal data as abnormal data.
Preferably, the method further comprises:
judging the abnormal grade of the bottom layer equipment according to the abnormal data;
and selecting different exception handling schemes according to the exception grades.
Preferably, the selecting different exception handling schemes includes:
if the abnormal grade indicates that the abnormality is serious, directly controlling the power-off renovation of the bottom equipment; and/or the presence of a gas in the gas,
and if the exception level indicates that the exception is not serious, informing the top management system to perform exception handling.
According to a third aspect of the embodiments of the present invention, there is provided an intelligent router, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring operation data of bottom equipment;
according to a preset algorithm, identifying abnormal data from the operating data, and sending the abnormal data to a top management system; the exception data includes: time domain anomaly data, and frequency domain anomaly data.
According to a fourth aspect of the embodiments of the present invention, there is provided an abnormal data detecting system including:
the system comprises a top management system, at least one project centralized controller, at least one intelligent router and at least one bottom device; wherein,
the intelligent router is connected with the bottom layer equipment according to the grouping of the bottom layer equipment;
the project centralized controller is connected with the intelligent router according to the division of the project scope;
and all the project centralized controllers are connected with the top-level management system.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
the edge computing network identifies abnormal data from the operation data by acquiring the operation data of the bottom layer equipment according to a preset algorithm, so that the detection of time domain and frequency domain abnormal data is realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic block diagram illustrating an anomalous data detection system in accordance with an exemplary embodiment;
FIG. 2 is a schematic block diagram illustrating an anomalous data detection system in accordance with another exemplary embodiment;
FIG. 3 is a flow chart illustrating a method of anomalous data detection in accordance with an exemplary embodiment;
FIG. 4 is a flow chart illustrating a method of anomalous data detection in accordance with another exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
FIG. 1 is a schematic block diagram illustrating an anomalous data detection system in accordance with an exemplary embodiment, the system, as shown in FIG. 1, including:
at least one bottom layer device 1, and a top layer management system 2;
the edge computing network 3 is used for acquiring the operation data of the bottom layer equipment 1, identifying abnormal data from the operation data according to a preset algorithm, and sending the abnormal data to the top layer management system 2; wherein the exception data comprises: time domain anomaly data, and frequency domain anomaly data.
It should be noted that, the technical solution provided by this embodiment is applicable to fields including but not limited to: abnormal data detection of energy equipment, abnormal data detection of power generation equipment, abnormal data detection of electric equipment, and the like.
The operational data includes, but is not limited to: current values, voltage values, power values, temperature values, and the like.
It can be understood that, in the technical scheme provided in this embodiment, the edge computing network identifies the abnormal data from the operation data by obtaining the operation data of the bottom layer device and according to the preset algorithm, thereby implementing detection of the time domain and frequency domain abnormal data.
Preferably, the edge computing network is further configured to determine an exception level of the underlying device according to the exception data, and select different exception handling schemes according to the exception level.
It can be understood that, the technical scheme provided by this embodiment not only can detect abnormal data, but also can determine the abnormal level of the fault equipment according to the abnormal data, and select different abnormal handling schemes according to the abnormal level, and the scheme is complete, the intelligent degree is high, the number of manual participation steps is small, the detection and repair efficiency is high, and the user experience degree is good.
Referring to fig. 2, preferably, the edge computing network 3 includes:
at least one project centralized controller 31, and at least one intelligent router 32;
the intelligent router 32 is connected with the bottom layer device 1 according to the grouping of the bottom layer device 1;
the project centralized controller 31 is connected with the intelligent router 32 according to the division of project scope;
all project concentrators 31 are connected to the top level management system 2.
It can be understood that the system architecture provided by the embodiment has a simple structure, is easy to deploy and implement, and has a compact architecture, good robustness and strong portability.
Preferably, if there are multiple intelligent routers 32 in the same project, the multiple intelligent routers 32 are connected through a gigabit network.
Preferably, the project centralized controller 31 is connected with the top management system 2 through an optical fiber;
the project centralized controller 31 is connected with the intelligent router 32 through a gigabit network;
the intelligent router 32 is connected with the bottom layer device 1 through a CAN bus protocol.
It can be understood that the system architecture provided by the embodiment has the advantages of high response speed and high safety and reliability.
FIG. 3 is a flow chart illustrating a method of anomalous data detection, as shown in FIG. 3, in accordance with an exemplary embodiment, the method including:
step S11, acquiring the operation data of the bottom layer equipment;
step S12, according to a preset algorithm, identifying abnormal data from the running data, and sending the abnormal data to a top management system; the exception data includes: time domain anomaly data, and frequency domain anomaly data.
It should be noted that the technical solution provided in this embodiment is applicable to the above abnormal data detection system, and is particularly applicable to the edge computing network of the above abnormal data detection system and the intelligent router of the edge computing network.
The operational data includes, but is not limited to: current values, voltage values, power values, temperature values, and the like.
It can be understood that, in the technical scheme provided in this embodiment, the edge computing network identifies the abnormal data from the operation data by obtaining the operation data of the bottom layer device and according to the preset algorithm, thereby implementing detection of the time domain and frequency domain abnormal data.
Preferably, the acquiring the operation data of the underlying device includes:
reading the operation data of the bottom-layer equipment from a database of the top-layer management system; and/or the presence of a gas in the gas,
and receiving the operation data sent by the bottom layer equipment in real time.
It can be understood that the operation data in the database of the top management system, and also the bottom device, are sent to the database through the intelligent router.
Preferably, the data of the bottom layer equipment in the database for one year is read as the input data of the abnormal data detection of the bottom layer equipment, if the data of the bottom layer equipment in the system for one year does not exist, the data with the longest time continuity as possible should be used as the input data, but the data in one quarter is at least required to be ensured.
Preferably, the identifying abnormal data from the operation data includes:
directly identifying time domain abnormal data of a first preset characteristic parameter from the operation data; and a process for the preparation of a coating,
and converting the time sequence value of the second preset characteristic parameter in the operating data into a frequency domain value, and identifying frequency domain abnormal data from the frequency domain value.
Preferably, the first preset characteristic parameter includes: voltage, current, power, temperature;
the second preset characteristic parameter includes: voltage, current.
Preferably, the method further comprises:
performing data preprocessing on the operating data;
the identifying of the abnormal data from the operation data specifically comprises:
and identifying abnormal data from the preprocessed running data.
Preferably, the data preprocessing includes, but is not limited to: data cleansing, data noise filtering, etc.
It can be understood that, because the data of the underlying device is incomplete, missing values exist and noise values accompany, the originally input underlying device data needs to be cleaned first, and the missing values are padded by using a multiple interpolation method. For the processing method for eliminating the noise, the characteristics of the data of the bottom layer equipment are combined, and the discrete Fourier decomposition is adopted to reduce the noise.
Preferably, the preset algorithm includes: and (5) data dimension-increasing and clustering algorithm.
It should be noted that, preferably, the kernel function is used to perform dimension-increasing processing on the data after data cleaning, the dimension-increasing operation is to subsequently adopt a k-means clustering algorithm to classify the data for bedding, and the specific purpose is to: the data dimensions are extended to facilitate the computational solution using Bregman distance.
And dividing the data into two types by adopting a k-means clustering algorithm, wherein one type is normal data of the bottom layer equipment, and the other type is abnormal data of the bottom layer equipment. In the k-means clustering algorithm, the Bregman distance is used in the embodiment, and the problem that the distance cannot be obtained when the data field is non-convex is solved.
Preferably, the method further comprises:
sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation;
and if the other intelligent routers with the preset proportional quantity secondarily confirm that the other intelligent routers are abnormal data, finally judging the abnormal data as abnormal data.
It should be noted that the preset ratio is set according to the user requirement.
For example, five intelligent routers are shared under a certain project centralized controller, after abnormal data of equipment is detected by a certain intelligent router, the abnormal data is sent to the other four intelligent routers for abnormal data identification, if three intelligent routers in the four intelligent routers secondarily confirm that the abnormal data is abnormal data, the data is finally judged to be abnormal data, the data is labeled, and the labeling result is stored in a database of the top management system.
Preferably, the method further comprises:
judging the abnormal grade of the bottom layer equipment according to the abnormal data;
and selecting different exception handling schemes according to the exception grades.
Preferably, the selecting different exception handling schemes includes:
if the abnormal grade indicates that the abnormality is serious, directly controlling the power-off renovation of the bottom equipment; and/or the presence of a gas in the gas,
and if the exception level indicates that the exception is not serious, informing the top management system to perform exception handling.
It can be understood that, the technical scheme provided by this embodiment not only can detect abnormal data, but also can determine the abnormal level of the fault equipment according to the abnormal data, and select different abnormal handling schemes according to the abnormal level, and the scheme is complete, the intelligent degree is high, the number of manual participation steps is small, the detection and repair efficiency is high, and the user experience degree is good.
Fig. 4 is a flowchart illustrating an abnormal data detecting method according to another exemplary embodiment, as shown in fig. 4, the method including:
step S21, acquiring the operation data of the bottom layer equipment;
step S22, data preprocessing is carried out on the operation data;
step S23, according to a preset algorithm, time domain abnormal data of voltage, current, power and temperature are directly identified from the operation data; converting the time sequence values of the voltage and the current in the operation data into frequency domain values, and identifying frequency domain abnormal data from the frequency domain values;
step S24, sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation; if the other intelligent routers with the preset proportional quantity secondarily confirm abnormal data, the abnormal data is finally judged as abnormal data;
step S25, sending the abnormal data to a top management system;
step S26, judging the abnormal grade of the bottom layer equipment according to the abnormal data;
step S27, if the abnormal grade indicates that the abnormality is serious, directly controlling the power-off renovation of the bottom layer equipment; and/or if the exception level indicates that the exception is not serious, informing the top management system to perform exception handling.
It should be noted that the technical solution provided in this embodiment is applicable to the above abnormal data detection system, and is particularly applicable to the edge computing network of the above abnormal data detection system and the intelligent router of the edge computing network.
The operational data includes, but is not limited to: current values, voltage values, power values, temperature values, and the like.
It can be understood that, in the technical scheme provided in this embodiment, the edge computing network identifies the abnormal data from the operation data by obtaining the operation data of the bottom layer device and according to the preset algorithm, thereby implementing detection of the time domain and frequency domain abnormal data.
An intelligent router provided according to an exemplary embodiment of the present invention includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring operation data of bottom equipment;
according to a preset algorithm, identifying abnormal data from the operating data, and sending the abnormal data to a top management system; the exception data includes: time domain anomaly data, and frequency domain anomaly data.
It can be understood that, in the technical scheme provided in this embodiment, the edge computing network identifies the abnormal data from the operation data by obtaining the operation data of the bottom layer device and according to the preset algorithm, thereby implementing detection of the time domain and frequency domain abnormal data.
An abnormal data detecting system according to an exemplary embodiment of the present invention includes:
the system comprises a top management system, at least one project centralized controller, at least one intelligent router and at least one bottom device; wherein,
the intelligent router is connected with the bottom layer equipment according to the grouping of the bottom layer equipment;
the project centralized controller is connected with the intelligent router according to the division of the project scope;
and all the project centralized controllers are connected with the top-level management system.
It can be understood that, in the technical scheme provided in this embodiment, the edge computing network identifies the abnormal data from the operation data by obtaining the operation data of the bottom layer device and according to the preset algorithm, thereby implementing detection of the time domain and frequency domain abnormal data.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (15)
1. An abnormal data detection system, comprising:
at least one bottom-level device, and a top-level management system;
the edge computing network is used for acquiring the operation data of the bottom layer equipment, identifying abnormal data from the operation data according to a preset algorithm and sending the abnormal data to the top layer management system; wherein the exception data comprises: time domain anomaly data, and frequency domain anomaly data;
the edge computing network, comprising:
at least one project centralized controller, and at least one intelligent router;
the intelligent router is connected with the bottom layer equipment according to the grouping of the bottom layer equipment;
the project centralized controller is connected with the intelligent router according to the division of the project scope;
all the project centralized controllers are connected with the top management system;
the project centralized controller is used for:
sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation;
and if the other intelligent routers with the preset proportional quantity secondarily confirm that the other intelligent routers are abnormal data, finally judging the abnormal data as abnormal data.
2. The system of claim 1,
and the edge computing network is also used for judging the abnormal grade of the bottom layer equipment according to the abnormal data and selecting different abnormal processing schemes according to the abnormal grade.
3. The system of claim 1,
and if a plurality of intelligent routers in the same project are arranged, the intelligent routers are connected through the gigabit network.
4. The system of claim 1,
the project centralized controller is connected with the top management system through optical fibers;
the project centralized controller is connected with the intelligent router through a gigabit network;
the intelligent router is connected with the bottom layer equipment through a CAN bus protocol.
5. An abnormal data detection method applied to the abnormal data detection system according to any one of claims 1 to 4, comprising:
acquiring operation data of bottom equipment;
according to a preset algorithm, identifying abnormal data from the operating data, and sending the abnormal data to a top management system; the exception data includes: time domain anomaly data, and frequency domain anomaly data;
sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation;
and if the other intelligent routers with the preset proportional quantity secondarily confirm that the other intelligent routers are abnormal data, finally judging the abnormal data as abnormal data.
6. The method of claim 5, wherein the obtaining operational data of the underlying device comprises:
reading the operation data of the bottom-layer equipment from a database of the top-layer management system; and/or the presence of a gas in the gas,
and receiving the operation data sent by the bottom layer equipment in real time.
7. The method of claim 5, wherein the identifying anomalous data from the operational data comprises:
directly identifying time domain abnormal data of a first preset characteristic parameter from the operation data; and a process for the preparation of a coating,
and converting the time sequence value of the second preset characteristic parameter in the operating data into a frequency domain value, and identifying frequency domain abnormal data from the frequency domain value.
8. The method of claim 7,
the first preset characteristic parameter includes: voltage, current, power, temperature;
the second preset characteristic parameter includes: voltage, current.
9. The method of claim 5,
the preset algorithm comprises the following steps: and (5) data dimension-increasing and clustering algorithm.
10. The method of claim 5, further comprising:
performing data preprocessing on the operating data;
the identifying of the abnormal data from the operation data specifically comprises:
and identifying abnormal data from the preprocessed running data.
11. The method of claim 5, further comprising:
sending the identified abnormal data to other intelligent routers in the same project for secondary confirmation;
and if the other intelligent routers with the preset proportional quantity secondarily confirm that the other intelligent routers are abnormal data, finally judging the abnormal data as abnormal data.
12. The method of claim 5, further comprising:
judging the abnormal grade of the bottom layer equipment according to the abnormal data;
and selecting different exception handling schemes according to the exception grades.
13. The method of claim 12, wherein selecting a different exception handling scheme comprises:
if the abnormal grade indicates that the abnormality is serious, directly controlling the power-off renovation of the bottom equipment; and/or the presence of a gas in the gas,
and if the exception level indicates that the exception is not serious, informing the top management system to perform exception handling.
14. An intelligent router, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
the abnormal data detecting method according to any one of claims 5 to 13 is performed.
15. An abnormal data detection system, comprising:
a top level management system, at least one project centralized controller, at least one intelligent router of claim 14, and at least one bottom level device; wherein,
the intelligent router is connected with the bottom layer equipment according to the grouping of the bottom layer equipment;
the project centralized controller is connected with the intelligent router according to the division of the project scope;
and all the project centralized controllers are connected with the top-level management system.
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| CN112888010B (en) * | 2021-01-28 | 2022-06-24 | 杭州云深科技有限公司 | Target wifi early warning system based on wifi connected device group information |
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