CN117310394A - Big data-based power failure detection method and device, electronic equipment and medium - Google Patents
Big data-based power failure detection method and device, electronic equipment and medium Download PDFInfo
- Publication number
- CN117310394A CN117310394A CN202311608144.0A CN202311608144A CN117310394A CN 117310394 A CN117310394 A CN 117310394A CN 202311608144 A CN202311608144 A CN 202311608144A CN 117310394 A CN117310394 A CN 117310394A
- Authority
- CN
- China
- Prior art keywords
- data
- fault
- abnormal
- calibration
- determining
- 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
- 238000001514 detection method Methods 0.000 title claims abstract description 104
- 230000002159 abnormal effect Effects 0.000 claims abstract description 144
- 238000000034 method Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims description 54
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012544 monitoring process Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The application relates to a method, a device, electronic equipment and a medium for detecting power faults based on big data, and relates to the field of fault detection; comparing the detection data with the calibration data to determine abnormal data, wherein the abnormal data is the detection data inconsistent with the calibration data; analyzing the abnormal data to determine fault data; and determining the position corresponding to the fault data as a fault position. The method and the device have the effect of quickly and accurately determining the fault position.
Description
Technical Field
The present invention relates to the field of fault detection, and in particular, to a method and an apparatus for detecting a power fault based on big data, an electronic device, and a medium.
Background
The power dispatching is an effective management means for ensuring safe and stable operation of the power grid, external reliable power supply and orderly execution of various power production works. The power dispatching is based on the data information fed back by various information acquisition devices or the information provided by monitoring personnel, and the actual operation parameters of the power grid are combined, so that the continuous safe and stable operation of the power grid is ensured.
The current fault detection method for the data center is video monitoring or sensor alarming. After a fault is found from the video monitoring or the sensor alarms, the staff uses the corresponding troubleshooting instrument to troubleshoot the data center and then repair the fault location.
When a fault alarm occurs or after a fault occurs, only a worker can know the fault, and the specific fault position is inconvenient to know, so that how to know the fault position in time when the data center fails becomes a problem.
Disclosure of Invention
In order to quickly and accurately determine the fault position, the application provides a power fault detection method and device based on big data, electronic equipment and media.
In a first aspect, the present application provides a method for detecting a power failure based on big data, which adopts the following technical scheme:
the power failure detection method based on big data comprises the following steps:
acquiring detection data and calibration data of each device of a data center;
comparing the detection data with the calibration data to determine abnormal data, wherein the abnormal data is detection data inconsistent with the calibration data;
analyzing the abnormal data to determine fault data, wherein the fault data is abnormal data which is larger than a preset score threshold value;
and determining the position corresponding to the fault data as a fault position.
By adopting the technical scheme, as the occupied area of the data center is larger and the data center is provided with a plurality of devices for operation, the detection data and the calibration data of each device of the data center are acquired, so that the operation state of the data center device can be conveniently known, and the abnormal situation can be timely found; by comparing the detection data with the calibration data, abnormal data, namely data which can be failed, is determined, the abnormal data is analyzed, and the failure data can be accurately determined, so that the failure position can be conveniently and rapidly determined according to the failure data.
In another possible implementation manner, the comparing the detection data with the calibration data to determine abnormal data includes:
splitting the detection data and the calibration data respectively to obtain a plurality of sub-detection data and a plurality of sub-calibration data;
the detection data and the sub calibration data are in one-to-one correspondence;
comparing each piece of sub-detection data with the corresponding piece of sub-calibration data;
and if the sub-detection data is inconsistent with the corresponding sub-calibration data, determining that the sub-detection data is abnormal data.
By adopting the technical scheme, the detection data and the calibration data are respectively split to obtain a plurality of sub-detection data and sub-calibration data, and each sub-detection data and the corresponding sub-calibration data can be more accurately compared, so that the accuracy of determining the abnormal data is improved.
In another possible implementation manner, the analyzing the abnormal data, and determining the fault data includes:
acquiring historical anomaly times of the anomaly data in a preset time period;
performing difference calculation on the abnormal data and the calibration data to obtain a data difference;
calculating the historical abnormal times, the data difference values and the weights corresponding to the historical abnormal times and the data difference values respectively to obtain a first score;
and determining the abnormal data with the first score larger than the preset score threshold value as fault data.
By adopting the technical scheme, the influence degree of the historical anomaly times and the difference values on the equipment faults is different, so that different weights are set, the first score of each anomaly data is calculated according to the weights corresponding to the historical anomaly times and the difference values, a quantization evaluation standard is provided by a preset score threshold value, and the probability of the data faults is higher if the preset score threshold value is larger than the preset score threshold value. Accordingly, the abnormal data having the first score greater than the preset score threshold value is determined as the fault data.
In another possible implementation manner, the calculating the difference between the abnormal data and the calibration data to obtain a data difference includes:
and if the data difference value is a negative value, eliminating the abnormal data corresponding to the negative value.
By adopting the technical scheme, when the data difference value is negative, the abnormal data is lower than the calibration data, that is, the equipment corresponding to the abnormal data can be used, the electronic equipment rejects the abnormal data and does not calculate the first score, so that the conditions of excessive abnormal data and complex calculation can be reduced, and the fault data can be conveniently and rapidly determined.
In another possible implementation, the method further includes:
determining a preset score interval in which the first score is located, wherein the preset score interval corresponds to the number of processing personnel;
and determining the number of the corresponding processing personnel among the preset scoring areas as the number of the processing personnel required by the fault position.
By adopting the technical scheme, the first scoring data center equipment has fault severity, and then the preset scoring intervals in which the first scoring is positioned are determined, and the optimal number of processing personnel is corresponding to each preset scoring interval, so that the number of processing personnel required by each fault position can be accurately determined according to the first scoring.
In another possible implementation, the method further includes:
generating alarm information based on the fault location and the number of processors;
and outputting the alarm information.
Through adopting above-mentioned technical scheme, confirm the processing personnel quantity that every fault data corresponds back, generate alarm information according to fault location and required processing personnel quantity, can learn the fault data and the required processing personnel's of every fault location quantity in detail through alarm information, output alarm information, improved the alarm effect, and then the staff of being convenient for arranges the processing according to required processing personnel's quantity and fault data in the alarm information.
In another possible implementation manner, the outputting the alarm information includes:
determining the area where each fault position is located, wherein each area corresponds to at least one processor;
determining target processing personnel with the same number as the processing personnel from the at least one processing personnel;
and sending alarm information to the terminal equipment of the target processing personnel.
By adopting the technical scheme, the data center is divided into a plurality of areas because the area of the data center is large and the structure is complex, each area corresponds to at least one processor, the area where the fault position is located is determined, then the target processor is determined from the at least one processor corresponding to the area according to the number of processors needed by the fault position corresponding to each abnormal data, and then the terminal equipment of the target processor which is directly determined sends alarm information, so that the target processor can respond quickly.
In a second aspect, the present application provides a power failure detection method device based on big data, which adopts the following technical scheme:
a big data based power failure detection apparatus comprising:
the data acquisition module is used for acquiring detection data and calibration data of each device of the data center;
the abnormal data determining module is used for comparing the detection data with the calibration data to determine abnormal data, wherein the abnormal data is detection data inconsistent with the calibration data;
the fault data determining module is used for analyzing the abnormal data and determining fault data, wherein the fault data is abnormal data which is larger than a preset score threshold value;
and the fault position determining module is used for determining the position corresponding to the fault data as a fault position.
By adopting the technical scheme, as the occupied area of the data center is relatively large and the data center is provided with a plurality of devices for operation, the data acquisition module acquires the detection data and the calibration data of each device of the data center, so that the operation state of the data center device can be conveniently known, and the abnormal situation can be timely found; the abnormal data determining module determines abnormal data, namely data with faults, by comparing the detection data with the calibration data, the fault data determining module analyzes the abnormal data and can accurately determine the fault data, and the fault position determining module determines the position corresponding to the abnormal data as the fault position, so that the fault position can be conveniently and rapidly determined according to the fault data.
In another possible implementation manner, the abnormal data determining module is specifically configured to, when comparing the detection data with the calibration data to determine abnormal data:
splitting the detection data and the calibration data respectively to obtain a plurality of sub-detection data and a plurality of sub-calibration data;
the detection data and the sub calibration data are in one-to-one correspondence;
comparing each piece of sub-detection data with the corresponding piece of sub-calibration data;
and if the sub-detection data is inconsistent with the corresponding sub-calibration data, determining that the sub-detection data is abnormal data.
In another possible implementation manner, the fault data determining module is specifically configured to, when analyzing the abnormal data and determining the fault data:
acquiring historical anomaly times of the anomaly data in a preset time period;
performing difference calculation on the abnormal data and the calibration data to obtain a data difference;
calculating the historical abnormal times, the data difference values and the weights corresponding to the historical abnormal times and the data difference values respectively to obtain a first score;
and determining the abnormal data with the first score larger than the preset score threshold value as fault data.
In another possible implementation manner, the fault data determining module is specifically configured to, when performing a difference calculation on the abnormal data and the calibration data to obtain a data difference value:
and if the data difference value is a negative value, eliminating the abnormal data corresponding to the negative value.
In another possible implementation, the apparatus further includes:
the interval determining module is used for determining a preset score interval in which the first score is located, and the preset score interval corresponds to the number of the processing personnel;
and the personnel number determining module is used for determining the number of the corresponding processing personnel in the preset scoring area as the number of the processing personnel required by the fault position.
In another possible implementation, the apparatus further includes:
the information generation module is used for generating alarm information based on the fault data and the number of the processing personnel;
and the output module is used for outputting the alarm information.
In another possible implementation manner, the output module is specifically configured to, when outputting the alarm information:
determining the area where each fault position is located, wherein each area corresponds to at least one processor;
determining target processing personnel with the same number as the processing personnel from the at least one processing personnel;
and sending alarm information to the terminal equipment of the target processing personnel.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
an electronic device, the electronic device comprising:
at least one processor;
a memory;
at least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, the at least one processor configured to: a method of big data based power failure detection as shown in any of the possible implementations according to the first aspect is performed.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer-readable storage medium, which when executed in a computer, causes the computer to perform the big data based power failure detection method of any of the first aspects.
In summary, the present application includes at least one of the following beneficial technical effects:
1. because the occupied area of the data center is large and the data center is provided with a plurality of devices for operation, the detection data and the calibration data of each device of the data center are acquired, so that the operation state of the data center device can be conveniently known, and the abnormal situation can be timely found; the abnormal data, namely the data with faults, can be determined by comparing the detection data with the calibration data, and the abnormal data can be analyzed to accurately determine the fault data, so that the fault position can be conveniently and rapidly determined according to the fault data;
2. the first scoring data center equipment generates fault severity, then a preset scoring area where the first score is located is determined, and the number of the processing personnel needed by each fault position can be accurately determined according to the first score because the optimal processing personnel number corresponds to each preset scoring area.
Drawings
Fig. 1 is a flow chart of a power failure detection method based on big data in an embodiment of the present application.
Fig. 2 is a schematic flow chart of a power failure detection apparatus based on big data in an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Description of the embodiments
The present application is described in further detail below in conjunction with figures 1-3.
Modifications of the embodiments which do not creatively contribute to the invention may be made by those skilled in the art after reading the present specification, but are protected by patent laws only within the scope of claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of 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 apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
The embodiment of the application provides a power failure detection method based on big data, which is executed by electronic equipment, wherein the electronic equipment can be a server or terminal equipment, and the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service. The terminal device may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., and the terminal device and the server may be directly or indirectly connected through wired or wireless communication, which is not limited herein, and as shown in fig. 1, the method includes: step S10, step S11, step S12, step S13, and step S14, wherein,
and S10, acquiring detection data and calibration data of each device of the data center.
In the embodiment of the application, the electronic device can acquire the detection data and the calibration data of the device through network monitoring or active request. The detection data and the calibration data of the equipment can be obtained by reading the interface data, so that the detection data and the calibration data can be conveniently and rapidly obtained. It should be noted that the electronic device has corresponding hardware interface and software protocol parsing capability.
And S11, comparing the detection data with the calibration data to determine abnormal data.
The abnormal data are detection data inconsistent with the calibration data.
In the embodiment of the application, the electronic device establishes the prediction model by performing statistical analysis on the detection data or training the calibration data by using a machine learning or deep learning model. And then taking the monitoring data as input, predicting an output value through a model, and thus accurately determining abnormal data, wherein the embodiment of the application is not particularly limited.
And step S12, analyzing the abnormal data to determine fault data.
The fault data are abnormal data which are larger than a preset score threshold value.
In the embodiment of the application, the preset score threshold is a warning signal, and if the preset score threshold is larger than the warning signal, the potential faults exist in the positions corresponding to the abnormal data, so that the abnormal data larger than the preset score threshold are determined to be the fault data, and the fault data can be determined conveniently and rapidly.
Step S13, determining a fault location based on the fault data.
In the embodiment of the application, the electronic device determines the fault type corresponding to the fault data, such as short circuit, electric leakage, overload and the like, according to the fault data, and then tests the device of the data center by using a proper tool, so that the fault position is conveniently and quickly determined according to the fault data, wherein the description is that the test can be performed by adopting logic analysis.
In one possible implementation manner of the embodiment of the present application, comparing the detection data with the calibration data to determine the abnormal data includes: step S110 (not shown), step S111 (not shown), step S112 (not shown), and step S113 (not shown), wherein,
and step S110, splitting the detection data and the calibration data respectively to obtain a plurality of sub-detection data and a plurality of sub-calibration data.
In the embodiment of the application, the electronic device respectively splits and calculates the detection data and the calibration data through the processing unit to obtain a plurality of sub-detection data and a plurality of sub-calibration data. It should be noted that, the electronic device may split the detection data and the calibration data into a plurality of subsets according to the type. The detection data and calibration data should be stored in a memory inside the electronic device, such as a hard disk, flash memory or other storage medium, before the splitting takes place.
In step S111, the plurality of detection data and the plurality of sub calibration data are in one-to-one correspondence.
Step S112, each piece of sub-detection data is compared with the corresponding piece of sub-calibration data.
In the embodiment of the application, the electronic device extracts the sub calibration data corresponding to the sub detection data from the database based on the data type, and then matches each sub detection data with the sub calibration data, so as to directly compare the sub detection data with the sub calibration data.
In step S113, if the sub-detection data is inconsistent with the corresponding sub-calibration data, the sub-detection data is determined to be abnormal data.
In embodiments of the present application, the sub-calibration data characterizes standard or reference data used by the electronic device in performing the test, which is typically used to compare and verify the accuracy of the test. Therefore, when the sub detection data is inconsistent with the sub calibration data, the abnormal occurrence of the equipment of the data center is indicated, and therefore the fault data can be accurately determined.
In one possible implementation manner of the embodiment of the present application, analyzing the abnormal data to determine the fault data includes: step S120 (not shown), step S121 (not shown), step S122 (not shown), and step S123 (not shown), wherein,
step S120, obtaining the historical anomaly times of the anomaly data in the preset time period.
In the embodiment of the present application, the electronic device may monitor or actively request to obtain, through a network, the abnormal data and the historical abnormal times of the device in the preset time period, or may connect the electronic device to a database, and retrieve the abnormal data and the historical abnormal times in the preset time period by using a query statement. The number of the abnormal data is assumed to be 3, namely, abnormal data 1, abnormal data 2 and abnormal data 3, wherein the number of the historical abnormal times corresponding to the historical abnormal data 1 is 20, the number of the historical abnormal times corresponding to the historical abnormal data 2 is 15, and the number of the historical abnormal times corresponding to the historical abnormal data 3 is 5.
Step S121, difference value calculation is carried out on the abnormal data and the calibration data, and a data difference value is obtained.
In the embodiment of the present application, it is assumed that 3 abnormal data are respectively abnormal data 1, abnormal data 2 and abnormal data 3, where abnormal data 1 is voltage, 190V, abnormal data 2 is voltage, 230V, abnormal data 3 is voltage, 240V, and calibration data is voltage 220V. And calculating to obtain a difference value corresponding to the abnormal data 1 of-30V, a difference value corresponding to the abnormal data 2 of 10V and a difference value corresponding to the abnormal data 3 of 20V.
Step S122, the historical anomaly times, the data difference values and the weights corresponding to the historical anomaly times and the data difference values are calculated to obtain a first score.
In this embodiment of the present application, the greater the number of times of the historical anomaly, the greater the probability that the position corresponding to the historical anomaly data has a fault, and taking step S120 as an example, after determining the number of times of the historical anomaly of the anomaly data, the first score may be calculated using the data difference value corresponding to the anomaly data. The weight of the historical anomaly times is assumed to be 0.6, and the weight of the data difference is assumed to be 0.4.
Taking step S121 and step S120 as an example, the electronic device calculates the first score of the abnormal data 1 as 20×0.6+ (-30) ×0.4=0; the first score of the abnormal data 2 is 15×0.6+10×0.4=13; the first score of the abnormal data 3 is 5×0.6+20×0.4=11.
In step S123, the abnormal data with the first score greater than the preset score threshold is determined as the fault data.
In the embodiment of the present application, the preset score threshold is obtained according to the test data, and if the preset score threshold is greater than the preset score threshold, it is indicated that there is a potential fault at the position corresponding to the abnormal data, and it is assumed that the preset score threshold is 5, and taking step S122 as an example, both the abnormal data 2 and the abnormal data 3 are greater than the preset score threshold, so that the abnormal data 2 is determined as the fault data 2, and the abnormal data 3 is determined as the fault data 3.
In one possible implementation manner of the embodiment of the present application, performing difference calculation on the abnormal data and the calibration data to obtain a data difference value, including:
if the data difference is negative, eliminating abnormal data corresponding to the negative.
In the embodiment of the application, when the data difference is negative, it is indicated that the abnormal data is lower than the calibration data, that is, the equipment corresponding to the abnormal data can be used, the electronic equipment rejects the abnormal data and does not calculate the first score, so that the situations of excessive abnormal data and complex calculation can be reduced, and the fault data can be conveniently and rapidly determined.
One possible implementation manner of the embodiment of the present application, the method further includes: step S14 (not shown in the figure) and step S15 (not shown in the figure), wherein,
step S14, determining a preset scoring area where the first score is located.
Wherein, the preset partition corresponds to the number of the processing personnel.
In the embodiment of the present application, it is assumed that there are a total of 3 preset sections, (0, 10), (10, 15) and (15, 20), respectively, the corresponding handler of the 3 preset sections is 1, 2 and 3, respectively.
And S15, determining the corresponding number of the processing personnel among the preset assigned areas as the number of the processing personnel required by the fault position.
In the embodiment of the present application, taking step 14 as an example, the electronic device determines that 2 persons are required for the processing personnel at the fault location corresponding to the fault data 2, and 2 persons are required for the processing personnel at the fault location corresponding to the fault data 3. The first score of each abnormal data is calculated, so that the severity of the fault of the data center equipment can be more intuitively represented, the preset score interval where the first score is located is determined, and the number of the processing personnel needed by each fault position can be accurately determined according to the first score because the optimal number of the processing personnel corresponds to each preset score interval.
One possible implementation manner of the embodiment of the present application, the method further includes: step S16 (not shown in the figure) and step S17 (not shown in the figure), wherein,
step S16, generating alarm information based on fault data and the number of processing personnel;
in the embodiment of the application, after the fault data is determined, the electronic device integrates the fault position corresponding to the fault data and the number of processing personnel required by the fault position, and generates alarm information.
And S17, outputting alarm information.
In the embodiment of the application, after the electronic equipment generates the alarm information, the alarm information can be sent to the target terminal equipment, so that the staff corresponding to the target terminal equipment can know the alarm information in time, and the response to the fault position is facilitated.
One possible implementation manner of the embodiment of the present application outputs alarm information, including: step S170 (not shown), step S171 (not shown), and step S172 (not shown), wherein,
step S170, determining the area where each fault location is located.
Wherein each zone corresponds to at least one handler.
In this embodiment of the present application, because the area of the data center is large and the structure is complex, the data center is divided into a plurality of areas, each area corresponds to at least one processor, so that it is convenient to manage the data center, the staff divides the data center into a plurality of areas in advance, then the plurality of areas divided into by the data center are stored in the electronic device, so that it is convenient to know the area where each fault position is located, and then it is convenient to determine the personnel that can process the fault position, and it is assumed that the data center is divided into 3 areas, namely, the area a, the area B and the area C, and the fault data 2 is located in the area B.
In step S171, a target processor having the same number as the processor is determined from the at least one processor.
In the embodiment of the present application, if 15 processors are corresponding to the area B, taking step S15 as an example, 2 processors are required for the fault location corresponding to the abnormal data 2. The electronic device may randomly determine 2 processors from the 15 processors, may determine 2 processors according to the life of the 15 processors from high to low, and may determine two processors from the 15 processors in other manners, which is not limited herein.
Step S172, sending alarm information to the terminal device of the target processor.
In the embodiment of the present application, taking step S171 as an example, terminal device information corresponding to each processor is stored in the electronic device, where the terminal device may be a mobile phone, a tablet computer, etc., and the terminal device information is, for example, a mobile phone number. After determining 2 processing personnel required by the fault position corresponding to the abnormal data 2, sending alarm information to terminal equipment corresponding to the two processing personnel respectively in a short message mode, so that the two processing personnel can timely learn the fault condition of the fault position corresponding to the abnormal data 2.
The foregoing embodiments describe the method for detecting a power failure based on big data from the viewpoint of a method flow, and the following embodiments describe the apparatus 20 for detecting a power failure based on big data from the viewpoint of a virtual module or a virtual unit, and specifically the following embodiments are described below.
The embodiment of the application provides a power failure detection method device 20 based on big data, as shown in fig. 2, the device 20 based on big data power failure detection may specifically include:
the data acquisition module 201 is configured to acquire detection data and calibration data of each device in the data center;
the abnormal data determining module 202 is configured to compare the detection data with the calibration data, determine abnormal data, where the abnormal data is detection data inconsistent with the calibration data;
the fault data determining module 203 is configured to analyze the abnormal data, determine the fault data, where the fault data is the abnormal data greater than a preset score threshold;
the fault location determining module 204 is configured to determine a location corresponding to the fault data as a fault location.
The embodiment of the application provides a device 20 for detecting electric power faults based on big data, wherein the data center occupies a larger area, and various devices are operated in the data center, so that a data acquisition module 201 acquires detection data and calibration data of each device in the data center, thereby facilitating the understanding of the operation state of the data center device and timely finding out abnormal conditions; the abnormal data determining module 202 determines abnormal data, namely data which can be failed by comparing the detection data with the calibration data, the fault data determining module 203 analyzes the abnormal data and can accurately determine the fault data, and the fault position determining module 204 determines the position corresponding to the abnormal data as the fault position, so that the fault position can be conveniently and rapidly determined according to the fault data.
In another possible implementation manner, the abnormal data determining module 202 is specifically configured to, when comparing the detection data with the calibration data, determine the abnormal data:
splitting the detection data and the calibration data respectively to obtain a plurality of sub-detection data and a plurality of sub-calibration data;
the detection data and the sub calibration data are in one-to-one correspondence;
comparing each piece of sub-detection data with the corresponding piece of sub-calibration data;
if the sub-detection data is inconsistent with the corresponding sub-calibration data, determining that the sub-detection data is abnormal data.
In another possible implementation manner, the fault data determining module 203 is specifically configured to, when analyzing the abnormal data and determining the fault data:
acquiring historical anomaly times of anomaly data in a preset time period;
performing difference calculation on the abnormal data and the calibration data to obtain a data difference;
calculating the historical abnormal times, the data difference values and the weights corresponding to the historical abnormal times and the data difference values respectively to obtain a first score;
and determining the abnormal data with the first score larger than a preset score threshold value as fault data.
In another possible implementation manner, the fault data determining module 203 is specifically configured to, when performing a difference calculation on the abnormal data and the calibration data to obtain a data difference value:
if the data difference is negative, eliminating abnormal data corresponding to the negative.
In another possible implementation, the apparatus 20 further includes:
the interval determining module is used for determining a preset score interval in which the first score is located, and the preset score interval corresponds to the number of the processing personnel;
and the personnel number determining module is used for determining the number of the corresponding processing personnel in the preset scoring area as the number of the processing personnel required by the fault position.
In another possible implementation, the apparatus 20 further includes:
the information generation module is used for generating alarm information based on the fault data and the number of the processing personnel;
and the output module is used for outputting alarm information.
In another possible implementation manner, the output module is specifically configured to, when outputting the alarm information:
determining the area where each fault position is located, wherein each area corresponds to at least one processor;
determining target processing personnel with the same number as the processing personnel from at least one processing personnel;
and sending alarm information to terminal equipment of the target processing personnel.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In an embodiment of the present application, as shown in fig. 3, an electronic device 30 shown in fig. 3 includes: a processor 301 and a memory 303. Wherein the processor 301 is coupled to the memory 303, such as via a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that, in practical applications, the transceiver 304 is not limited to one, and the structure of the electronic device 30 is not limited to the embodiment of the present application.
The processor 301 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 302 may include a path to transfer information between the components. Bus 302 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect Standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. Bus 302 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or type of bus.
The Memory 303 may be, but is not limited to, a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory ), a CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 303 is used for storing application program codes for executing the present application and is controlled to be executed by the processor 301. The processor 301 is configured to execute the application code stored in the memory 303 to implement what is shown in the foregoing method embodiments.
Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. But may also be a server or the like. The electronic device shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments herein.
The present application provides a computer readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above. Compared with the related art, in the embodiment of the application, the occupied area of the data center is large, and the data center is provided with a plurality of devices for operation, so that the detection data and the calibration data of each device of the data center are acquired, the operation state of the data center device is conveniently known, and the abnormal situation is timely found; by comparing the detection data with the calibration data, abnormal data, namely data which can be failed, is determined, the abnormal data is analyzed, and the failure data can be accurately determined, so that the failure position can be conveniently and rapidly determined according to the failure data.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing is only a partial embodiment of the present application and it should be noted that, for a person skilled in the art, several improvements and modifications can be made without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (9)
1. The power failure detection method based on big data is characterized by comprising the following steps:
acquiring detection data and calibration data of each device of a data center;
comparing the detection data with the calibration data to determine abnormal data, wherein the abnormal data is detection data inconsistent with the calibration data;
analyzing the abnormal data to determine fault data, wherein the fault data is abnormal data which is larger than a preset score threshold value;
acquiring historical anomaly times of the anomaly data in a preset time period;
performing difference calculation on the abnormal data and the calibration data to obtain a data difference;
calculating the historical abnormal times, the data difference values and the weights corresponding to the historical abnormal times and the data difference values respectively to obtain a first score;
determining the abnormal data with the first score larger than the preset score threshold value as fault data;
and determining the position corresponding to the fault data as a fault position.
2. The big data based power failure detection method of claim 1, wherein comparing the detection data with the calibration data, determining abnormal data, comprises:
splitting the detection data and the calibration data respectively to obtain a plurality of sub-detection data and a plurality of sub-calibration data;
the detection data and the sub calibration data are in one-to-one correspondence;
comparing each piece of sub-detection data with the corresponding piece of sub-calibration data;
and if the sub-detection data is inconsistent with the corresponding sub-calibration data, determining that the sub-detection data is the abnormal data.
3. The method for detecting a power failure based on big data according to claim 1, wherein the calculating the difference between the abnormal data and the calibration data to obtain a data difference comprises:
and if the data difference value is a negative value, eliminating the abnormal data corresponding to the negative value.
4. The big data based power failure detection method of claim 1, further comprising:
determining a preset scoring interval in which the first score is located, wherein the preset scoring interval corresponds to the number of processing personnel;
and determining the number of the corresponding processing personnel among the preset scoring areas as the number of the processing personnel required by the fault position.
5. The big data based power failure detection method of claim 4, further comprising:
generating alarm information based on the fault data and the number of the processing personnel;
and outputting the alarm information.
6. The big data based power failure detection method of claim 5, wherein the outputting the alarm information includes:
determining the area where each fault position is located, wherein each area corresponds to at least one processor;
determining target processing personnel with the same number as the processing personnel from the at least one processing personnel;
and sending alarm information to the terminal equipment of the target processing personnel.
7. Apparatus for power failure detection based on big data, comprising:
the data acquisition module is used for acquiring detection data and calibration data of each device of the data center;
the abnormal data determining module is used for comparing the detection data with the calibration data to determine abnormal data, wherein the abnormal data is detection data inconsistent with the calibration data;
the fault data determining module is used for analyzing the abnormal data and determining fault data, wherein the fault data is abnormal data which is larger than a preset score threshold value;
and the fault position determining module is used for determining the position corresponding to the fault data as a fault position.
8. An electronic device, comprising:
at least one processor;
a memory;
at least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, the at least one application program: for performing the big data based power failure detection method according to any of claims 1 to 6.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed in a computer, causes the computer to execute the big data based power failure detection method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311608144.0A CN117310394A (en) | 2023-11-29 | 2023-11-29 | Big data-based power failure detection method and device, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311608144.0A CN117310394A (en) | 2023-11-29 | 2023-11-29 | Big data-based power failure detection method and device, electronic equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117310394A true CN117310394A (en) | 2023-12-29 |
Family
ID=89297644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311608144.0A Pending CN117310394A (en) | 2023-11-29 | 2023-11-29 | Big data-based power failure detection method and device, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117310394A (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108471168A (en) * | 2018-05-23 | 2018-08-31 | 山东广域科技有限责任公司 | A kind of substation's wireless data transmission and inspection base and method |
| CN108501980A (en) * | 2018-03-23 | 2018-09-07 | 固安信通信号技术股份有限公司 | The monitoring method and terminal device of track circuit equipment |
| CN112465165A (en) * | 2020-11-23 | 2021-03-09 | 国网山东省电力公司惠民县供电公司 | Power grid fault processing method, system and device |
| CN112633758A (en) * | 2020-12-31 | 2021-04-09 | 重庆电子工程职业学院 | Intelligent on-line management system for maintainers |
| CN113010394A (en) * | 2021-03-01 | 2021-06-22 | 北京中大科慧科技发展有限公司 | Machine room fault detection method for data center |
| CN113516340A (en) * | 2021-04-01 | 2021-10-19 | 广东电网有限责任公司广州供电局 | Intelligent work order pushing method and device |
| CN113780401A (en) * | 2021-09-06 | 2021-12-10 | 国网山东省电力公司电力科学研究院 | Composite insulator fault detection method and system based on principal component analysis method |
| CN113918376A (en) * | 2021-12-14 | 2022-01-11 | 湖南天云软件技术有限公司 | Fault detection method, device, equipment and computer readable storage medium |
| CN115879697A (en) * | 2022-11-21 | 2023-03-31 | 深圳市计通智能技术有限公司 | Data processing method, system, equipment and storage medium for industrial internet |
| CN116187593A (en) * | 2023-04-27 | 2023-05-30 | 国网山东省电力公司滨州市沾化区供电公司 | Power distribution network fault prediction processing method, device, equipment and storage medium |
| CN116304909A (en) * | 2023-03-13 | 2023-06-23 | 天翼云科技有限公司 | Abnormality detection model training method, fault scene positioning method and device |
| CN116911626A (en) * | 2023-06-25 | 2023-10-20 | 广西电网有限责任公司电力科学研究院 | System and method for managing and analyzing post-disaster rush repair of power grid |
| CN117074849A (en) * | 2023-07-04 | 2023-11-17 | 蒋洁 | Power system fault emergency processing method based on big data |
-
2023
- 2023-11-29 CN CN202311608144.0A patent/CN117310394A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108501980A (en) * | 2018-03-23 | 2018-09-07 | 固安信通信号技术股份有限公司 | The monitoring method and terminal device of track circuit equipment |
| CN108471168A (en) * | 2018-05-23 | 2018-08-31 | 山东广域科技有限责任公司 | A kind of substation's wireless data transmission and inspection base and method |
| CN112465165A (en) * | 2020-11-23 | 2021-03-09 | 国网山东省电力公司惠民县供电公司 | Power grid fault processing method, system and device |
| CN112633758A (en) * | 2020-12-31 | 2021-04-09 | 重庆电子工程职业学院 | Intelligent on-line management system for maintainers |
| CN113010394A (en) * | 2021-03-01 | 2021-06-22 | 北京中大科慧科技发展有限公司 | Machine room fault detection method for data center |
| CN113516340A (en) * | 2021-04-01 | 2021-10-19 | 广东电网有限责任公司广州供电局 | Intelligent work order pushing method and device |
| CN113780401A (en) * | 2021-09-06 | 2021-12-10 | 国网山东省电力公司电力科学研究院 | Composite insulator fault detection method and system based on principal component analysis method |
| CN113918376A (en) * | 2021-12-14 | 2022-01-11 | 湖南天云软件技术有限公司 | Fault detection method, device, equipment and computer readable storage medium |
| CN115879697A (en) * | 2022-11-21 | 2023-03-31 | 深圳市计通智能技术有限公司 | Data processing method, system, equipment and storage medium for industrial internet |
| CN116304909A (en) * | 2023-03-13 | 2023-06-23 | 天翼云科技有限公司 | Abnormality detection model training method, fault scene positioning method and device |
| CN116187593A (en) * | 2023-04-27 | 2023-05-30 | 国网山东省电力公司滨州市沾化区供电公司 | Power distribution network fault prediction processing method, device, equipment and storage medium |
| CN116911626A (en) * | 2023-06-25 | 2023-10-20 | 广西电网有限责任公司电力科学研究院 | System and method for managing and analyzing post-disaster rush repair of power grid |
| CN117074849A (en) * | 2023-07-04 | 2023-11-17 | 蒋洁 | Power system fault emergency processing method based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108564181B (en) | Power equipment fault detection and maintenance method and terminal equipment | |
| CN108445410B (en) | Method and device for monitoring running state of storage battery pack | |
| CN112162878B (en) | Database fault discovery method and device, electronic equipment and storage medium | |
| CN110674009B (en) | Application server performance monitoring method and device, storage medium and electronic equipment | |
| CN112286771B (en) | Alarm method for monitoring global resources | |
| CN109976971B (en) | Hard disk state monitoring method and device | |
| CN116502166B (en) | Method, device, equipment and medium for predicting faults of target equipment | |
| CN113157536A (en) | Alarm analysis method, device, equipment and storage medium | |
| CN114387762A (en) | Building data management method, device, equipment and storage medium | |
| CN113191074A (en) | Machine room power supply parameter detection method for data center | |
| KR102315580B1 (en) | Fire predictive analysis device and method of building | |
| CN115409283A (en) | Equipment failure prediction method, equipment failure prediction device, equipment and storage medium | |
| CN114063582A (en) | Method and device for monitoring a product test process | |
| US20170302506A1 (en) | Methods and apparatus for fault detection | |
| CN115904883B (en) | RPA flow execution visual abnormity monitoring method, device and medium | |
| CN117310394A (en) | Big data-based power failure detection method and device, electronic equipment and medium | |
| CN111614504A (en) | Power grid regulation and control data center service characteristic fault positioning method and system based on time sequence and fault tree analysis | |
| CN111159029A (en) | Automatic testing method and device, electronic equipment and computer readable storage medium | |
| JP2019139556A (en) | Maintenance management device, system, and program | |
| CN114610560A (en) | System abnormity monitoring method, device and storage medium | |
| CN112199207A (en) | Alarm information pushing method, device, system, equipment and medium | |
| CN112966056A (en) | Information processing method, device, equipment, system and readable storage medium | |
| US11881572B1 (en) | Method for fault diagnosis and computer device | |
| CN117608974A (en) | Server fault detection method, device, equipment and medium based on artificial intelligence | |
| CN116526415B (en) | Power equipment protection device and control method thereof |
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 |