CN114167223B - Power supply abnormity detection method and device and computer readable storage medium - Google Patents
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Abstract
The invention discloses a method and a device for detecting power supply abnormity and a computer readable storage medium, which are applied to a power distribution system, wherein the power distribution system comprises a processing end and a plurality of distribution boxes in communication connection with the processing end, and the method comprises the following steps: determining an area image of a power supply area corresponding to each distribution box; determining the similarity among the regional pictures, and determining the regional cluster to which each power supply region belongs according to the similarity; acquiring the power consumption of each power supply area in the target area cluster in a preset time period; and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption. The invention improves the detection efficiency of power supply abnormity.
Description
Technical Field
The present invention relates to the field of power distribution management technologies, and in particular, to a method and an apparatus for detecting power supply abnormality, and a computer-readable storage medium.
Background
When the power consumption in the areas with similar characteristics has too large difference, the abnormal power consumption in one area is often indicated. The reason for the abnormal power utilization is generally due to the failure or aging of the circuit of the power supply network or the power supply facility. In order to determine the abnormal reason of the power supply network, a manager needs to go to the power distribution areas in sequence to inspect the corresponding power distribution boxes until the areas with abnormal power supply are judged according to the power distribution data, so that the existing means has low efficiency in detecting the abnormal power supply.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for detecting a power supply abnormality, and a computer-readable storage medium, so as to solve a technical problem of how to improve efficiency of detecting a power supply abnormality.
The embodiment of the invention provides a method for detecting power supply abnormity, which is applied to a power distribution system, wherein the power distribution system comprises a processing end and a plurality of distribution boxes in communication connection with the processing end, and the method for detecting the power supply abnormity comprises the following steps:
determining an area image of a power supply area corresponding to each distribution box;
determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity;
acquiring the power consumption of each power supply area in a target area cluster in a preset time period;
and determining a power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption.
In an embodiment, the step of determining an area representation of the power supply area corresponding to each distribution box includes:
acquiring historical power consumption of power supply areas corresponding to the distribution boxes in a plurality of historical time periods;
and obtaining the area portrait of the power supply area corresponding to each distribution box according to the historical power consumption.
In an embodiment, the step of obtaining the historical power consumption of the power supply area corresponding to each distribution box in a plurality of historical time periods includes:
acquiring a preset historical time period and a preset historical date interval;
and taking the electricity consumption in the historical time period in the historical date interval as the historical electricity consumption.
In an embodiment, the step of determining an area representation of the power supply area corresponding to each distribution box includes:
acquiring the line layout and line load of a power supply area corresponding to each distribution box;
and obtaining a regional image of the power supply region corresponding to each distribution box according to the circuit layout and the circuit load.
In an embodiment, the determining a similarity between the region representations and the determining a region cluster to which each of the power supply regions belongs according to the similarity includes:
acquiring a characteristic vector of each region image;
clustering the feature vectors according to a preset initial clustering center to obtain clustering centers corresponding to the initial clustering centers in quantity;
and determining the area cluster according to the clustering center.
In an embodiment, the step of determining the power supply abnormality detection result of each power supply area in the target area cluster according to the power consumption amount includes:
acquiring an average value of the electricity consumption;
acquiring a difference value between the electricity consumption and the average value;
and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the difference value.
In an embodiment, the step of determining the power supply abnormality detection result of each power supply area in the target area cluster according to the difference includes:
determining a target difference value which is greater than or equal to a preset value difference value from the difference values;
determining a target power consumption corresponding to the target difference value, and taking a power supply area corresponding to the target power consumption as an abnormal area;
and obtaining a power supply abnormity detection result of each power supply area determined to be in the target area cluster according to the abnormal area.
In an embodiment, after the step of determining the power supply abnormality detection result of each power supply area in the target area cluster according to the power consumption, the method further includes:
and outputting prompt information corresponding to the power supply abnormality detection result.
The embodiment of the present invention further provides a device for detecting power supply abnormality, where the device for detecting power supply abnormality includes: the power supply abnormality detection method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the power supply abnormality detection method.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the steps of the method for detecting a power supply abnormality as described above.
In the technical scheme of the embodiment, a detection device for power supply abnormity determines the area portrait of a power supply area corresponding to each distribution box; determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity; acquiring the power consumption of each power supply area in a target area cluster in a preset time period; and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption. The power supply abnormality detection device can determine the area images of all the power supply areas, compare the power supply areas with the same area images and determine the power supply areas with different power consumption than other areas, so that a power supply abnormality result is obtained, a power distribution manager can quickly know the power supply areas with the same area images and different power consumption in a target area cluster based on the power supply abnormality detection result, and then power saving measures are taken for the power supply areas, so that the power supply abnormality detection efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic hardware architecture diagram of a power supply abnormality detection apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for detecting a power supply abnormality according to a first embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S10 of the method for detecting abnormal power supply according to the present invention;
FIG. 4 is a detailed flowchart of step S10 of the power supply abnormality detection method according to the third embodiment of the present invention;
fig. 5 is a schematic flow chart of a detection apparatus for power supply abnormality according to a fourth embodiment of the present invention.
Detailed Description
In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The main solution of the invention is: determining an area image of a power supply area corresponding to each distribution box by a power supply abnormity detection device; determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity; acquiring the power consumption of each power supply area in a target area cluster in a preset time period; and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption.
The power supply abnormality detection device can determine the area images of all the power supply areas, compare the power supply areas with the same area images and determine the power supply areas with different power consumption than other areas, so that a power supply abnormality result is obtained, a power distribution manager can quickly know the power supply areas with the same area images and different power consumption in a target area cluster based on the power supply abnormality detection result, and then power saving measures are taken for the power supply areas, so that the power supply abnormality detection efficiency is improved.
As an implementation manner, the power supply abnormality detection device may be as shown in fig. 1.
The embodiment of the invention relates to a power supply abnormity detection device, which comprises: a processor 101, e.g. a CPU, a memory 102, a communication bus 103. Wherein a communication bus 103 is used for enabling the connection communication between these components.
The memory 102 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). As in fig. 1, a detection program may be included in the memory 103 as a computer-readable storage medium; and the processor 101 may be configured to call the detection program stored in the memory 102 and perform the following operations:
determining an area image of a power supply area corresponding to each distribution box;
determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity;
acquiring the power consumption of each power supply area in a target area cluster in a preset time period;
and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring historical power consumption of power supply areas corresponding to the distribution boxes in a plurality of historical time periods;
and obtaining the area portrait of the power supply area corresponding to each distribution box according to the historical power consumption.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring a preset historical time period and a preset historical date interval;
and taking the electricity consumption in the historical time period in the historical date interval as the historical electricity consumption.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring the line layout and line load of a power supply area corresponding to each distribution box;
and obtaining a regional image of the power supply region corresponding to each distribution box according to the circuit layout and the circuit load.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring a characteristic vector of each region image;
clustering the feature vectors according to a preset initial clustering center to obtain clustering centers corresponding to the initial clustering centers in number;
and determining the area cluster according to the clustering center.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
acquiring an average value of the electricity consumption;
acquiring a difference value between the electricity consumption and the average value;
and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the difference value.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
determining a target difference value which is greater than or equal to a preset value difference value from the difference values;
determining a target power consumption corresponding to the target difference value, and taking a power supply area corresponding to the target power consumption as an abnormal area;
and obtaining a power supply abnormity detection result of each power supply area determined to be in the target area cluster according to the abnormal area.
In one embodiment, the processor 101 may be configured to call a detection program stored in the memory 102 and perform the following operations:
and outputting prompt information corresponding to the power supply abnormality detection result.
In the technical scheme of the embodiment, the detection device for power supply abnormity determines the area portrait of the power supply area corresponding to each distribution box; determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity; acquiring the power consumption of each power supply area in a target area cluster in a preset time period; and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the power consumption. The power supply abnormality detection device can determine the area images of all the power supply areas, compare the power supply areas with the same area images and determine the power supply areas with different power consumption than other areas, so that a power supply abnormality result is obtained, a power distribution manager can quickly know the power supply areas with the same area images and different power consumption in a target area cluster based on the power supply abnormality detection result, and then power saving measures are taken for the power supply areas, so that the power supply abnormality detection efficiency is improved.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, fig. 2 is a first embodiment of the method for detecting power supply abnormality according to the present invention, the method includes the following steps:
step S10 is to determine an area image of the power supply area corresponding to each distribution box.
In this embodiment, the power distribution system of this embodiment is applied to a power distribution system, and the power distribution system of this embodiment is connected to a plurality of distribution boxes, wherein the detection device for power supply abnormality stores the region images of the power supply regions corresponding to different distribution boxes. The distribution system also comprises a processing end and a plurality of distribution boxes in communication connection with the processing end, wherein the distribution boxes are also named as distribution equipment and are the general names of equipment such as a high-voltage distribution box, a generator, a transformer, a power line, a circuit breaker, a low-voltage switch cabinet, a distribution board, a switch box, a control box and the like in the power system. The power supply area refers to a power supply area in which a single distribution box in a power distribution system controls power supply.
In the present embodiment, the region image is used to represent the characteristics of each power supply region, and is similar to the person image, and the region image corresponds to the attribute tag corresponding to the power distribution region, for example: a region image of a corresponding power supply region is obtained by performing an image based on data which can be acquired in advance such as the number of large power consuming devices corresponding to the power supply region, the size of a resident, a circuit layout, and a line load.
Optionally, the area representation may also be determined according to a development law of a preset object in the power supply area, for example, a historical power consumption law, and when it is determined that the power supply area is in the a section in a plurality of detection periods according to the historical power consumption of a certain power supply area, the power supply area may be represented based on the numerical value of the a section, so as to obtain the area representation of the power supply area.
Step S20, determining the similarity between the region images, and determining the region cluster to which each of the power supply regions belongs according to the similarity.
In this embodiment, the region images of the respective power supply regions are stored in the form of feature vectors, and the similarity comparison between the region images can be determined based on the similarity of the feature vectors.
And step S30, acquiring the electricity consumption of each power supply area in the target area cluster in a preset time period.
In this embodiment, the target area cluster includes a plurality of power supply areas, and the distribution boxes in the power supply areas return the power consumption of the power supply areas within a preset time period.
Step S40, determining a power supply abnormality detection result of each power supply area in the target area cluster according to the power consumption amount.
In this embodiment, when the area images are similar but the power consumption is different from that of other power supply areas, the prompt information corresponding to the situation may be used as the power supply abnormality detection result.
Optionally, obtaining an average value of the electricity consumption; acquiring a difference value between the electricity consumption and the average value; and determining the power supply abnormity detection result of each power supply area in the target area cluster according to the difference value.
Optionally, a target difference greater than or equal to a pre-value difference is determined among the differences; determining a target power consumption corresponding to the target difference value, and taking a power supply area corresponding to the target power consumption as an abnormal area; and obtaining a power supply abnormity detection result of each power supply area determined to be in the target area cluster according to the abnormal area.
Optionally, a prompt message corresponding to the power supply abnormality detection result is output.
In the technical scheme of this embodiment, because the detection device for power supply abnormality can determine the area images of each power supply area, the power supply areas with the same area images can be compared to determine the power supply areas with different power consumptions from other areas, so as to obtain a power supply abnormality result, a power distribution manager can quickly know the power supply areas with the same area images but different power consumptions in the target area cluster based on the power supply abnormality detection result, and then take power saving measures for the power supply areas, thereby improving the detection efficiency of power supply abnormality.
Referring to fig. 3, fig. 3 is a second embodiment of the method for detecting power supply abnormality according to the present invention, and based on any one of the first to second embodiments, step S10 includes:
and step S11, acquiring historical electricity consumption of the power supply area corresponding to each distribution box in a plurality of historical time periods.
In this embodiment, historical power consumption prestores in the unusual detection device of power supply, and wherein, historical power consumption is gathered and is sent to the unusual detection device of power supply by the block terminal that corresponds to make the unusual detection device of power supply save when receiving the power consumption information of block terminal passback.
Optionally, acquiring a preset historical time period and a preset historical date interval; and taking the electricity consumption in the historical time period in the historical date interval as the historical electricity consumption. Here, in consideration of the fact that the data amount of the historical power consumption amount may be large, the data of the preset time period may be taken as the above historical power consumption amount.
And step S12, obtaining an area image of the power supply area corresponding to each distribution box according to the historical power consumption.
In this embodiment, the area image of the power supply area may be determined based on the historical power consumption of each power supply area stored by the power supply abnormality detection device, for example: when the used amount of B power supply areas in the A power supply areas does not exceed the preset power utilization range in N continuous periods, the B areas can be imaged based on the used amount of the B power supply areas.
In the technical solution of this embodiment, because the detection device of power supply abnormality can obtain the historical power consumption of each power supply area and portray based on the historical power consumption, when it is detected that an abnormal power supply area with a large power consumption difference appears in the power supply areas with the same area portrayal, the abnormal power supply area can be determined in the power supply areas with the same area portrayal based on the historical power consumption and the real-time power consumption, so as to realize the detection of power supply abnormality, and compared with the detection of whether the power supply areas are abnormal one by one, this embodiment improves the efficiency of power supply abnormality detection.
Referring to fig. 4, fig. 4 is a third embodiment of the method for detecting a power supply abnormality according to the present invention, and based on any one of the first to third embodiments, step S10 includes:
and step S13, obtaining the line layout and the line load of the power supply area corresponding to each distribution box.
In the present embodiment, the line layout and the line load are data stored in advance in the power supply abnormality detection device.
And step S14, obtaining an area image of the power supply area corresponding to each distribution box according to the circuit layout and the circuit load.
In this embodiment, the power supply abnormality detection device may perform area representation of each power supply area based on the line layout or the line load.
In the technical scheme of this embodiment, because the detection device of the power supply abnormality can portrait based on the line layout or the line load of each power supply area, when the power supply areas with similar line layouts have a larger power consumption difference, the detection device of the power supply abnormality can detect in time, thereby improving the real-time performance of the power supply abnormality detection.
Referring to fig. 5, fig. 5 shows a fourth embodiment of the method for detecting a power supply abnormality according to the present invention, where step S20 includes:
in step S21, a feature vector of each of the area images is acquired.
And step S22, clustering the feature vectors according to preset initial clustering centers to obtain clustering centers with the number corresponding to the initial clustering centers.
And step S23, determining the area cluster according to the clustering center.
Clustering analysis, also known as cluster analysis, is a statistical analysis method for studying (sample or index) classification problems, and is also an important algorithm for data mining. Clustering (Cluster) analysis is composed of several patterns (patterns), which are typically vectors of a metric (measure) or a point in a multidimensional space. Cluster analysis is based on similarity, with more similarity between patterns in one cluster than between patterns not in the same cluster.
In this embodiment, the stored region images are clustered and analyzed by using a K-means clustering algorithm (K-means clustering algorithm), so as to divide the region muscle groups, where the K-means clustering algorithm is a clustering analysis algorithm for iterative solution, and includes the steps of dividing feature vector data into K groups, randomly selecting K objects as initial clustering centers, calculating distances between each object and each seed clustering center, and assigning each object to the nearest clustering center. The cluster centers and the objects assigned to them represent a cluster. The cluster center of a cluster is recalculated for each sample assigned based on the objects existing in the cluster. This process will be repeated until some termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal.
Clustering is a process of categorically organizing data sets into data members that are similar in some way, and is a technique for finding such internal structures, and is often referred to as unsupervised learning. k-means clustering is the most well-known partitional clustering algorithm, making it the most widely used of all clustering algorithms due to its simplicity and efficiency. Given a set of data points and the number of clusters k, k required, specified by the user, the k-means algorithm iteratively groups the data into k clusters according to a certain distance function.
In the technical scheme of the embodiment, the power supply area clusters are divided in a mode of carrying out cluster analysis on the area portrait, so that the area division mode can be digitalized, and the accuracy of muscle group division is improved.
In order to achieve the above object, an embodiment of the present invention further provides a device for detecting a power supply abnormality, where the device for detecting a power supply abnormality includes: the power supply abnormality detection method comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the power supply abnormality detection method.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for detecting a power supply abnormality as described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. The method for detecting the power supply abnormity is applied to a power distribution system, the power distribution system comprises a processing end and a plurality of distribution boxes which are in communication connection with the processing end, and the method for detecting the power supply abnormity comprises the following steps:
determining an area portrait of a power supply area corresponding to each distribution box, wherein historical power consumption of the power supply area corresponding to each distribution box in a plurality of historical time periods is obtained, and the area portrait of the power supply area corresponding to each distribution box is obtained according to the historical power consumption; or acquiring the line layout and the line load of the power supply area corresponding to each distribution box, and obtaining an area portrait of the power supply area corresponding to each distribution box according to the line layout and the line load;
determining the similarity between the region portrait, and determining the region cluster to which each power supply region belongs according to the similarity;
acquiring the power consumption of each power supply area in a target area cluster in a preset time period;
acquiring an average value of the electricity consumption;
acquiring a difference value between the power consumption and the average value;
and determining a power supply abnormality detection result of each power supply area in the target area cluster according to the difference, wherein when the target area cluster has the condition that the area portrait is similar but the power consumption is different from that of other power supply areas, the prompt information corresponding to the condition is used as the power supply abnormality detection result.
2. The method for detecting power supply abnormality according to claim 1, wherein the step of acquiring historical power consumption of the power supply area corresponding to each distribution box in a plurality of historical time periods includes:
acquiring a preset historical time period and a preset historical date interval;
and taking the electricity consumption in the historical time period in the historical date interval as the historical electricity consumption.
3. The method for detecting a power supply abnormality according to claim 1, wherein the step of determining a similarity between the region images and determining a region cluster to which each of the power supply regions belongs based on the similarity includes:
acquiring a characteristic vector of each region image;
clustering the characteristic vectors according to a preset initial clustering center to obtain clustering centers corresponding to the initial clustering centers in number;
and determining the area cluster according to the clustering center.
4. The method according to claim 1, wherein the step of determining the power supply abnormality detection result for each of the power supply areas in the target area cluster based on the difference value includes:
determining a target difference value which is greater than or equal to a preset value difference value from the difference values;
determining a target power consumption corresponding to the target difference value, and taking a power supply area corresponding to the target power consumption as an abnormal area;
and obtaining a power supply abnormity detection result of each power supply area determined to be in the target area cluster according to the abnormal area.
5. The method for detecting a power supply abnormality according to claim 1, wherein after the step of determining a power supply abnormality detection result for each of the power supply areas in the target area cluster based on the used amount of power, the method further comprises:
and outputting prompt information corresponding to the power supply abnormity detection result.
6. A power supply abnormality detection device, characterized by comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of detecting a power supply anomaly according to any one of claims 1 to 5 when executing the computer program.
7. A computer-readable storage medium, characterized in that a computer program is stored thereon, which computer program, when being executed by a processor, carries out the steps of the method of detecting a power supply abnormality according to any one of claims 1 to 5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209260A (en) * | 2019-04-26 | 2019-09-06 | 平安科技（深圳）有限公司 | Power consumption method for detecting abnormality, device, equipment and computer readable storage medium |
| CN111783875A (en) * | 2020-06-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Abnormal user detection method, device, equipment and medium based on cluster analysis |
| WO2020248543A1 (en) * | 2019-06-12 | 2020-12-17 | 杭州萤石软件有限公司 | Abnormal target detection method and device, and storage medium |
| CN113125903A (en) * | 2021-04-20 | 2021-07-16 | 广东电网有限责任公司汕尾供电局 | Line loss anomaly detection method, device, equipment and computer-readable storage medium |
| CN113487051A (en) * | 2021-07-28 | 2021-10-08 | 武汉都梁信息技术有限责任公司 | Power distribution network fault first-aid repair maintenance data processing method and equipment and computer storage medium |
-
2022
- 2022-02-11 CN CN202210127215.4A patent/CN114167223B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110209260A (en) * | 2019-04-26 | 2019-09-06 | 平安科技（深圳）有限公司 | Power consumption method for detecting abnormality, device, equipment and computer readable storage medium |
| WO2020248543A1 (en) * | 2019-06-12 | 2020-12-17 | 杭州萤石软件有限公司 | Abnormal target detection method and device, and storage medium |
| CN111783875A (en) * | 2020-06-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Abnormal user detection method, device, equipment and medium based on cluster analysis |
| CN113125903A (en) * | 2021-04-20 | 2021-07-16 | 广东电网有限责任公司汕尾供电局 | Line loss anomaly detection method, device, equipment and computer-readable storage medium |
| CN113487051A (en) * | 2021-07-28 | 2021-10-08 | 武汉都梁信息技术有限责任公司 | Power distribution network fault first-aid repair maintenance data processing method and equipment and computer storage medium |
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