CN111178556A - Electric quantity abnormality detection method and device, computer equipment and readable storage medium - Google Patents
Electric quantity abnormality detection method and device, computer equipment and readable storage medium Download PDFInfo
- Publication number
- CN111178556A CN111178556A CN201911357916.1A CN201911357916A CN111178556A CN 111178556 A CN111178556 A CN 111178556A CN 201911357916 A CN201911357916 A CN 201911357916A CN 111178556 A CN111178556 A CN 111178556A
- Authority
- CN
- China
- Prior art keywords
- electric quantity
- data
- detection model
- abnormal
- model
- 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 145
- 230000005856 abnormality Effects 0.000 title claims description 8
- 230000002159 abnormal effect Effects 0.000 claims abstract description 51
- 230000005611 electricity Effects 0.000 claims description 43
- 238000000034 method Methods 0.000 claims description 32
- 238000004590 computer program Methods 0.000 claims description 23
- 238000004364 calculation method Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 16
- 238000004806 packaging method and process Methods 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 210000003719 b-lymphocyte Anatomy 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/14—Payment architectures specially adapted for billing systems
-
- 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 an electric quantity abnormity detection method, an electric quantity abnormity detection device, computer equipment and a readable storage medium, wherein the electric quantity abnormity detection method comprises the following steps: acquiring electric quantity data; and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model. According to the electric quantity abnormity detection method, the electric quantity detection model is constructed, abnormal electric quantity data in a large amount of electric quantity data can be rapidly detected according to the output result of the electric quantity detection model, the efficiency is high, and a large amount of human resources are saved.
Description
Technical Field
The present invention relates to the field of power technologies, and in particular, to a method and an apparatus for detecting power anomaly, a computer device, and a readable storage medium.
Background
In recent years, high-voltage remote load measurement and control terminals and low-voltage remote centralized meter reading have all been covered. The realization of full coverage has improved the efficiency and the frequency of electric energy electric quantity data greatly, can gather a large amount of electric energy electric quantity data, very big reduction the human cost. How to better and more efficiently utilize the massive data and exert the potential value of the application of the massive data becomes a new challenge in electric energy and electric quantity management application. Especially, under the background that data are mostly analyzed and utilized manually at present, the necessity and the urgency of intelligently utilizing big data for researching and developing electric energy and electric quantity are more prominent.
Conventionally, management and analysis of electricity and electricity charges still require a large amount of human resources, are inefficient and difficult to comprehensively screen abnormal electricity data, so that electricity charges are wrongly paid out, and subsequently, a large amount of manpower and time are consumed to correct wrong electricity bills, and meanwhile, line loss analysis and management are seriously influenced by wrong electricity.
Disclosure of Invention
The application provides a method and a device for detecting abnormal electric quantity, a computer device and a readable storage medium, which can improve the efficiency of detecting abnormal electric quantity.
A method of power anomaly detection, the method comprising:
acquiring electric quantity data;
and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
In an embodiment, before the inputting the power data into the power detection model, the method further includes:
acquiring electric quantity samples from a preset electric quantity sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
In an embodiment, the constructing the power detection model according to the power samples includes:
classifying the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets;
constructing a corresponding initial detection model for each electric quantity sample subset;
performing iterative training on the initial detection model by using an electric quantity sample subset except for the initial detection model to obtain an electric quantity detection sub-model;
and packaging each electric quantity detection submodel to obtain the electric quantity detection model.
In an embodiment, said constructing, for each of said electric quantity sample subsets, a corresponding initial detection model includes:
acquiring characteristic data and power consumption in the electric quantity sample subset;
constructing the corresponding initial detection model according to the characteristic data and the electricity consumption; the characteristic data includes time information and temperature information.
In an embodiment, the preset classification rule comprises a user type classification rule.
In an embodiment, after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further includes:
and if the electric quantity data is determined to be abnormal, inputting the electric quantity data into an electric charge calculation model to generate an electric charge bill.
In an embodiment, after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further includes:
and if the electric quantity data is determined to be abnormal, outputting prompt information.
An electricity quantity abnormality detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring electric quantity data;
and the detection module is used for inputting the electric quantity data into an electric quantity detection model and determining whether the electric quantity data is abnormal or not according to an output result of the electric quantity detection model.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above.
The method, the device, the computer equipment and the readable storage medium for detecting the abnormal electric quantity, provided by the embodiment of the application, comprise the following steps: acquiring electric quantity data; and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model. According to the electric quantity abnormity detection method, the electric quantity detection model is constructed, abnormal electric quantity data in a large amount of electric quantity data can be rapidly detected according to the output result of the electric quantity detection model, the efficiency is high, and a large amount of human resources are saved.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an abnormal power detection method according to an embodiment;
fig. 2 is a block diagram of an abnormal power detection apparatus according to an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and in the accompanying drawings, preferred embodiments of the present application are set forth. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the specific embodiments disclosed below.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. In the description of the present application, "a number" means at least one, such as one, two, etc., unless specifically limited otherwise.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a flowchart of an abnormal power detection method according to an embodiment, and as shown in fig. 1, the abnormal power detection method includes steps 110 and 120, where:
The electric quantity data of the application can comprise information such as electricity consumption, electric quantity date, user information, a circuit to which the electric quantity data belongs, a transformer substation to which the electric quantity data belongs and the like. The user information includes a user name, an ID number, an identifier, and the like, and the power consumption includes various types of power consumption. After the electric quantity data is acquired, the electric quantity data may be stored in a list format, for example: user information: 001, electricity date: 10 months in 2019, electricity consumption: 10KW, the circuit: a first line of a B cell of Shenzhen city, Guangdong province, wherein the transformer substation: the first transformer substation in Shenzhen city, Guangdong province. The storage format of the electric quantity data may also be other formats, and this embodiment is not particularly limited.
And 120, inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
After the electric quantity data are obtained, the electric quantity data are input into the electric quantity detection model, the electric quantity detection model detects the received electric quantity data and detects the result, and abnormal electric quantity data can be quickly screened out according to the detection result. It can be understood that, because the high-voltage remote load measurement and control terminal and the low-voltage remote centralized meter reading are all fully covered at present, the electric quantity data of a plurality of users of a plurality of transformer substations can be simultaneously input into the electric quantity detection model.
In an embodiment, before the inputting the power data into the power detection model, the method further includes:
acquiring electric quantity samples from a preset electric quantity sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
The preset electric quantity sample set can be pre-stored historical electricity consumption related information, the historical electricity consumption related information needs to be normal data, and if abnormal data exist, the quality of the electric quantity detection model can be affected.
In an embodiment, the constructing the power detection model according to the power samples includes:
and classifying the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets.
In the present application, a plurality is understood to mean at least 2 (2 or more), that is, 2, 3 or even more.
In an embodiment, the preset classification rule comprises a user type classification rule. Since the power demand and the power consumption of different user types are different, for example, the power consumption of the commodity house user and the power consumption of the common house user are obviously different, and the power fee calculation modes of the commodity house user and the common house user are also different, the present application is described by taking the classification of the power consumption data according to the user types as an example.
And classifying the electric quantity samples according to the user types to obtain a plurality of electric quantity sample subsets, wherein each electric quantity sample subset corresponds to different categories. The method comprises the following specific steps:
each piece of electric quantity information carries corresponding identification information, and the identification information uniquely identifies the user type of the electric quantity information. And classifying the electric quantity information through identification information for identifying the electric quantity information. The identification information may be numbers, letters, etc., and the specific form is not limited.
In one embodiment, the identification information is composed of a number for representing category information of the power amount information. For example, the number 1 may be used to indicate that the power information is a type a user and the number 2 is a type B user.
In an embodiment, the preset classification rule includes a classification rule according to the collection time of the electric quantity data. And classifying according to the acquisition time of each electric quantity sample to obtain an electric quantity sample subset corresponding to each year and each month. For example, the information of the first subset of electrical quantity samples is: 6, 1 st of 2019, the power consumption of the xxx office building in Shenzhen city, Guangdong province and the air temperature of 30 ℃, and the information of the second power sample subset is as follows: 6, 2 and 2019, the power consumption of the xxx office building in Shenzhen city, Guangdong province and the air temperature of 30 ℃, and the information of the third power sample subset is as follows: and 3, 3 and 2 in 2019, the positions of the xxx office buildings in Shenzhen city of Guangdong province and the electricity consumption of the air temperature of 15 ℃. Whether the electricity consumption of the xxx office buildings is influenced by whether the electricity consumption is the rest day or not, and the electricity consumption is also influenced by the temperature. Therefore, the electricity data can be classified according to the collection time of the electricity data.
And constructing a corresponding initial detection model for each electric quantity sample subset.
In one embodiment, characteristic data and power consumption in the subset of power samples are obtained, and the characteristic data comprises time information and temperature information.
Constructing the corresponding initial detection model according to the characteristic data and the electricity consumption;
the initial detection model can be constructed using the deep learning framework Pytorch or Caffe. After the initial model is built, the initial model needs to be trained. The number of training samples is not limited in this embodiment, and the greater the number of training samples is, the stronger the robustness of the model obtained by training is.
The initial detection model can be constructed based on a convolution network system of convolution operation, and in addition, a supervision learning mechanism of deep learning is adopted to train the initial detection model to obtain an electric quantity detection sub-model. The convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, has the characteristic learning capacity and can carry out translation invariant classification on input information according to a hierarchical structure. Supervised Learning (Supervised Learning) essentially obtains a model through the training of labeled data, and then adds a specific label to the newly added data through the obtained model. The whole machine learning target is to make the learned model well suitable for new samples.
And performing iterative training on the initial detection model by using the electric quantity sample subset except for the initial detection model to obtain an electric quantity detection submodel.
In an embodiment, the feature data in the electricity quantity data have different degrees of influence on the electricity consumption, and the plurality of feature data and the electricity consumption have a functional relationship, which may be a linear first-order equation, or a nonlinear first-order quadratic equation or a binary second-order equation, and the like. Using a quadratic binary equation f (z) as ax2+by2And for example, + cxy + dx + ey + f, wherein z represents power consumption, x represents time information, and y represents temperature information, the time information, the temperature information and the power consumption in one of the electric quantity sample subsets are subjected to iterative training on the binary quadratic equation to obtain optimal a value, b value, c value, e value and f value, and finally the electric quantity detection submodel corresponding to the electric quantity sample subset is obtained.
And packaging each electric quantity detection submodel to obtain the electric quantity detection model.
The electric quantity detection submodel is encapsulated according to preset classification rules, and the electric quantity detection submodels corresponding to different user types are encapsulated to obtain the electric quantity detection model. After the electric quantity data is obtained, firstly obtaining the user type of the electric quantity data, then inputting the electric quantity data into a corresponding electric quantity detection sub-model according to the user type, firstly calculating theoretical electric quantity according to characteristic data in the electric quantity data by the electric quantity detection sub-model, comparing the theoretical electric quantity with actual electric quantity in the electric quantity data, and determining that the electric quantity data is abnormal if the difference between the theoretical electric quantity and the actual electric quantity is a preset threshold value.
The method, the device, the computer equipment and the readable storage medium for detecting the abnormal electric quantity, provided by the embodiment of the application, comprise the following steps: acquiring electric quantity data; and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model. According to the electric quantity abnormity detection method, the electric quantity detection model is constructed, abnormal electric quantity data in a large amount of electric quantity data can be rapidly detected according to the output result of the electric quantity detection model, the efficiency is high, and a large amount of human resources are saved.
In an embodiment, after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further includes:
and if the electric quantity data is determined to be abnormal, inputting the electric quantity data into an electric charge calculation model to generate an electric charge bill.
The electric charge calculation model can be preset, and after the electric quantity data are determined to be abnormal, the electric charge bill can be directly generated by inputting the electric quantity data into the electric charge calculation model, so that the electric charge calculation efficiency is improved, and the accuracy of electric charge calculation is ensured. It should be noted that the electric charge calculation model may be adjusted according to the unit price of the electric charge, and different types of users may correspond to different electric charge calculation models.
In an embodiment, after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further includes:
and if the electric quantity data is determined to be abnormal, outputting prompt information. The prompt information may be a voice prompt or a prompt box, and the specific prompt mode is not limited in this embodiment and may be set according to actual conditions.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a power abnormality detection apparatus including: an acquisition module 210 and a detection module 220, wherein:
the obtaining module 210 is configured to obtain the electric quantity data.
The electric quantity data of the application can comprise information such as electricity consumption, electric quantity date, user information, a circuit to which the electric quantity data belongs, a transformer substation to which the electric quantity data belongs and the like. The user information includes a user name, an ID number, an identifier, and the like, and the power consumption includes various types of power consumption. After the electric quantity data is acquired, the electric quantity data may be stored in a list format, for example: user information: 001, electricity date: 10 months in 2019, electricity consumption: 10KW, the circuit: a first line of a B cell of Shenzhen city, Guangdong province, wherein the transformer substation: the first transformer substation in Shenzhen city, Guangdong province. The storage format of the electric quantity data may also be other formats, and this embodiment is not particularly limited.
The detection module 220 is configured to input the electric quantity data into an electric quantity detection model, and determine whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
After the electric quantity data are obtained, the electric quantity data are input into the electric quantity detection model, the electric quantity detection model detects the received electric quantity data and detects the result, and abnormal electric quantity data can be quickly screened out according to the detection result. It can be understood that, because the high-voltage remote load measurement and control terminal and the low-voltage remote centralized meter reading are all fully covered at present, the electric quantity data of a plurality of users of a plurality of transformer substations can be simultaneously input into the electric quantity detection model.
In an embodiment, the power anomaly detection device further includes a model establishing module, configured to obtain power samples from a preset power sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
The preset electric quantity sample set can be pre-stored historical electricity consumption related information, the historical electricity consumption related information needs to be normal data, and if abnormal data exist, the quality of the electric quantity detection model can be affected.
In an embodiment, the model establishing module is further configured to classify the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets.
In the present application, a plurality is understood to mean at least 2 (2 or more), that is, 2, 3 or even more.
In an embodiment, the preset classification rule comprises a user type classification rule. Since the power demand and the power consumption of different user types are different, for example, the power consumption of the commodity house user and the power consumption of the common house user are obviously different, and the power fee calculation modes of the commodity house user and the common house user are also different, the present application is described by taking the classification of the power consumption data according to the user types as an example.
And classifying the electric quantity samples according to the user types to obtain a plurality of electric quantity sample subsets, wherein each electric quantity sample subset corresponds to different categories. The method comprises the following specific steps:
each piece of electric quantity information carries corresponding identification information, and the identification information uniquely identifies the user type of the electric quantity information. And classifying the electric quantity information through identification information for identifying the electric quantity information. The identification information may be numbers, letters, etc., and the specific form is not limited.
In one embodiment, the identification information is composed of a number for representing category information of the power amount information. For example, the number 1 may be used to indicate that the power information is a type a user and the number 2 is a type B user.
In an embodiment, the preset classification rule includes a classification rule according to the collection time of the electric quantity data. And classifying according to the acquisition time of each electric quantity sample to obtain an electric quantity sample subset corresponding to each year and each month. For example, the information of the first subset of electrical quantity samples is: 6, 1 st of 2019, the power consumption of the xxx office building in Shenzhen city, Guangdong province and the air temperature of 30 ℃, and the information of the second power sample subset is as follows: 6, 2 and 2019, the power consumption of the xxx office building in Shenzhen city, Guangdong province and the air temperature of 30 ℃, and the information of the third power sample subset is as follows: and 3, 3 and 2 in 2019, the positions of the xxx office buildings in Shenzhen city of Guangdong province and the electricity consumption of the air temperature of 15 ℃. Whether the electricity consumption of the xxx office buildings is influenced by whether the electricity consumption is the rest day or not, and the electricity consumption is also influenced by the temperature. Therefore, the electricity data can be classified according to the collection time of the electricity data.
And constructing a corresponding initial detection model for each electric quantity sample subset.
In an embodiment, the model building module is further configured to obtain feature data and power consumption in the subset of power samples;
constructing the corresponding initial detection model according to the characteristic data and the electricity consumption; the characteristic data includes time information and temperature information.
The initial detection model can be constructed using the deep learning framework Pytorch or Caffe. After the initial model is built, the initial model needs to be trained. The number of training samples is not limited in this embodiment, and the greater the number of training samples is, the stronger the robustness of the model obtained by training is.
The initial detection model can be constructed based on a convolution network system of convolution operation, and in addition, a supervision learning mechanism of deep learning is adopted to train the initial detection model to obtain an electric quantity detection sub-model. The convolutional neural network is a feedforward neural network which comprises convolutional calculation and has a deep structure, has the characteristic learning capacity and can carry out translation invariant classification on input information according to a hierarchical structure. Supervised Learning (Supervised Learning) essentially obtains a model through the training of labeled data, and then adds a specific label to the newly added data through the obtained model. The whole machine learning target is to make the learned model well suitable for new samples.
And performing iterative training on the initial detection model by using the electric quantity sample subset except for the initial detection model to obtain an electric quantity detection submodel.
In an embodiment, the feature data in the electricity quantity data have different degrees of influence on the electricity consumption, and the plurality of feature data and the electricity consumption have a functional relationship, which may be a linear first-order equation, or a nonlinear first-order quadratic equation or a binary second-order equation, and the like. Using a quadratic binary equation f (z)＝ax2+by2And for example, + cxy + dx + ey + f, wherein z represents power consumption, x represents time information, and y represents temperature information, the time information, the temperature information and the power consumption in one of the electric quantity sample subsets are subjected to iterative training on the binary quadratic equation to obtain optimal a value, b value, c value, e value and f value, and finally the electric quantity detection submodel corresponding to the electric quantity sample subset is obtained.
And packaging each electric quantity detection submodel to obtain the electric quantity detection model.
The electric quantity detection submodel is encapsulated according to preset classification rules, and the electric quantity detection submodels corresponding to different user types are encapsulated to obtain the electric quantity detection model. After the electric quantity data is obtained, firstly obtaining the user type of the electric quantity data, then inputting the electric quantity data into a corresponding electric quantity detection sub-model according to the user type, firstly calculating theoretical electric quantity according to characteristic data in the electric quantity data by the electric quantity detection sub-model, comparing the theoretical electric quantity with actual electric quantity in the electric quantity data, and determining that the electric quantity data is abnormal if the difference between the theoretical electric quantity and the actual electric quantity is a preset threshold value.
In an embodiment, the abnormal electric quantity detection device further includes an electric bill generation module, configured to input the electric quantity data into an electric bill calculation model to generate an electric bill if it is determined that the electric quantity data is not abnormal.
The electric charge calculation model can be preset, and after the electric quantity data are determined to be abnormal, the electric charge bill can be directly generated by inputting the electric quantity data into the electric charge calculation model, so that the electric charge calculation efficiency is improved, and the accuracy of electric charge calculation is ensured. It should be noted that the electric charge calculation model may be adjusted according to the unit price of the electric charge, and different types of users may correspond to different electric charge calculation models.
In an embodiment, the power abnormality detection apparatus further includes a prompt module, configured to output a prompt message if it is determined that the power data is abnormal.
And if the electric quantity data is determined to be abnormal, outputting prompt information. The prompt information may be a voice prompt or a prompt box, and the specific prompt mode is not limited in this embodiment and may be set according to actual conditions.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power abnormality detection apparatus method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring electric quantity data;
and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring electric quantity samples from a preset electric quantity sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
classifying the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets;
constructing a corresponding initial detection model for each electric quantity sample subset;
performing iterative training on the initial detection model by using an electric quantity sample subset except for the initial detection model to obtain an electric quantity detection sub-model;
and packaging each electric quantity detection submodel to obtain the electric quantity detection model.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring characteristic data and power consumption in the electric quantity sample subset;
constructing the corresponding initial detection model according to the characteristic data and the electricity consumption; the characteristic data includes time information and temperature information.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
and if the electric quantity data is determined to be abnormal, inputting the electric quantity data into an electric charge calculation model to generate an electric charge bill.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
and if the electric quantity data is determined to be abnormal, outputting prompt information.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electric quantity data;
and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
In an embodiment, the computer program when executed by the processor performs the steps of:
acquiring electric quantity samples from a preset electric quantity sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
In an embodiment, the computer program when executed by the processor performs the steps of:
classifying the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets;
constructing a corresponding initial detection model for each electric quantity sample subset;
performing iterative training on the initial detection model by using an electric quantity sample subset except for the initial detection model to obtain an electric quantity detection sub-model;
and packaging each electric quantity detection submodel to obtain the electric quantity detection model.
In an embodiment, the computer program when executed by the processor performs the steps of:
acquiring characteristic data and power consumption in the electric quantity sample subset;
constructing the corresponding initial detection model according to the characteristic data and the electricity consumption; the characteristic data includes time information and temperature information.
In an embodiment, the computer program when executed by the processor performs the steps of:
and if the electric quantity data is determined to be abnormal, inputting the electric quantity data into an electric charge calculation model to generate an electric charge bill.
In an embodiment, the computer program when executed by the processor performs the steps of:
and if the electric quantity data is determined to be abnormal, outputting prompt information.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An electricity quantity abnormality detection method, characterized by comprising:
acquiring electric quantity data;
and inputting the electric quantity data into an electric quantity detection model, and determining whether the electric quantity data is abnormal according to an output result of the electric quantity detection model.
2. The method of claim 1, wherein prior to entering the charge data into a charge detection model, the method further comprises:
acquiring electric quantity samples from a preset electric quantity sample set;
and constructing the electric quantity detection model according to the electric quantity sample.
3. The method of claim 2, wherein the constructing the charge detection model from the charge samples comprises:
classifying the electric quantity samples according to a preset classification rule to obtain a plurality of electric quantity sample subsets;
constructing a corresponding initial detection model for each electric quantity sample subset;
performing iterative training on the initial detection model by using an electric quantity sample subset except for the initial detection model to obtain an electric quantity detection sub-model;
and packaging each electric quantity detection submodel to obtain the electric quantity detection model.
4. The method of claim 3, wherein the constructing, for each of the subsets of electrical quantity samples, a corresponding initial detection model comprises:
acquiring characteristic data and power consumption in the electric quantity sample subset;
constructing the corresponding initial detection model according to the characteristic data and the electricity consumption; the characteristic data includes time information and temperature information.
5. The method of claim 3, wherein the preset classification rules comprise user type classification rules.
6. The method according to claim 1, wherein after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further comprises:
and if the electric quantity data is determined to be abnormal, inputting the electric quantity data into an electric charge calculation model to generate an electric charge bill.
7. The method according to claim 1, wherein after the determining whether the power amount data is abnormal according to the output result of the power amount detection model, the method further comprises:
and if the electric quantity data is determined to be abnormal, outputting prompt information.
8. An electricity quantity abnormality detection apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring electric quantity data;
and the detection module is used for inputting the electric quantity data into an electric quantity detection model and determining whether the electric quantity data is abnormal or not according to an output result of the electric quantity detection model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911357916.1A CN111178556A (en) | 2019-12-25 | 2019-12-25 | Electric quantity abnormality detection method and device, computer equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911357916.1A CN111178556A (en) | 2019-12-25 | 2019-12-25 | Electric quantity abnormality detection method and device, computer equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111178556A true CN111178556A (en) | 2020-05-19 |
Family
ID=70654051
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911357916.1A Pending CN111178556A (en) | 2019-12-25 | 2019-12-25 | Electric quantity abnormality detection method and device, computer equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111178556A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112184491A (en) * | 2020-10-13 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Abnormal data identification method and device in power grid, computer equipment and storage medium |
| CN113110961A (en) * | 2021-04-30 | 2021-07-13 | 平安国际融资租赁有限公司 | Equipment abnormality detection method and device, computer equipment and readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106707099A (en) * | 2016-11-30 | 2017-05-24 | 国网上海市电力公司 | Monitoring and locating method based on abnormal electricity consumption detection module |
| CN106780115A (en) * | 2016-11-30 | 2017-05-31 | 国网上海市电力公司 | Abnormal electricity consumption monitoring and alignment system and method |
| CN109636667A (en) * | 2018-12-07 | 2019-04-16 | 国网江苏省电力有限公司扬州供电分公司 | A kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature |
| CN109726885A (en) * | 2018-07-17 | 2019-05-07 | 平安科技（深圳）有限公司 | Electricity consumption anomaly assessment method, apparatus, equipment and computer storage medium |
| CN110045209A (en) * | 2019-05-10 | 2019-07-23 | 广东电网有限责任公司 | Detection method, device, equipment and the readable storage medium storing program for executing of electricity consumption data exception |
| CN110119758A (en) * | 2019-04-01 | 2019-08-13 | 全球能源互联网研究院有限公司 | A kind of electricity consumption data abnormality detection and model training method, device |
-
2019
- 2019-12-25 CN CN201911357916.1A patent/CN111178556A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106707099A (en) * | 2016-11-30 | 2017-05-24 | 国网上海市电力公司 | Monitoring and locating method based on abnormal electricity consumption detection module |
| CN106780115A (en) * | 2016-11-30 | 2017-05-31 | 国网上海市电力公司 | Abnormal electricity consumption monitoring and alignment system and method |
| CN109726885A (en) * | 2018-07-17 | 2019-05-07 | 平安科技（深圳）有限公司 | Electricity consumption anomaly assessment method, apparatus, equipment and computer storage medium |
| CN109636667A (en) * | 2018-12-07 | 2019-04-16 | 国网江苏省电力有限公司扬州供电分公司 | A kind of low-voltage customer multiplexing electric abnormality detection method based on user's week electrical feature |
| CN110119758A (en) * | 2019-04-01 | 2019-08-13 | 全球能源互联网研究院有限公司 | A kind of electricity consumption data abnormality detection and model training method, device |
| CN110045209A (en) * | 2019-05-10 | 2019-07-23 | 广东电网有限责任公司 | Detection method, device, equipment and the readable storage medium storing program for executing of electricity consumption data exception |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112184491A (en) * | 2020-10-13 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Abnormal data identification method and device in power grid, computer equipment and storage medium |
| CN112184491B (en) * | 2020-10-13 | 2023-10-24 | 南方电网数字电网研究院有限公司 | Method, device, computer equipment and storage medium for identifying abnormal data in power grid |
| CN113110961A (en) * | 2021-04-30 | 2021-07-13 | 平安国际融资租赁有限公司 | Equipment abnormality detection method and device, computer equipment and readable storage medium |
| CN113110961B (en) * | 2021-04-30 | 2022-10-21 | 平安国际融资租赁有限公司 | Equipment abnormality detection method and device, computer equipment and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sulzer et al. | The challenge and opportunity of battery lifetime prediction from field data | |
| Xu et al. | Life prediction of lithium-ion batteries based on stacked denoising autoencoders | |
| Chen et al. | Remaining useful life prediction and state of health diagnosis of lithium-ion battery based on second-order central difference particle filter | |
| CN107506868B (en) | Method and device for predicting short-time power load | |
| Kim et al. | Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network | |
| Feng et al. | State of health estimation of large-cycle lithium-ion batteries based on error compensation of autoregressive model | |
| EP3905424B1 (en) | Data processing device, data processing method and computer program | |
| CN111178556A (en) | Electric quantity abnormality detection method and device, computer equipment and readable storage medium | |
| Luo et al. | A simple feature extraction method for estimating the whole life cycle state of health of lithium-ion batteries using transformer-based neural network | |
| CN112949721A (en) | Power equipment static data quality assessment method and system | |
| Radaideh et al. | Efficient analysis of parametric sensitivity and uncertainty of fuel cell models with application to SOFC | |
| Kunz et al. | Early battery performance prediction for mixed use charging profiles using hierarchal machine learning | |
| JP7070224B2 (en) | Battery information processing system and battery information processing method | |
| Ghassemi et al. | Optimal surrogate and neural network modeling for day-ahead forecasting of the hourly energy consumption of university buildings | |
| Chen et al. | State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method | |
| Ly et al. | T-shape data and probabilistic remaining useful life prediction for Li-ion batteries using multiple non-crossing quantile Long Short-Term Memory | |
| Zhang et al. | A method for remaining discharge time prediction of lithium‐ion batteries under dynamic uncertainty | |
| CN113933718B (en) | Retired battery capacity sorting method, retired battery capacity sorting device, retired battery capacity sorting equipment and storage medium | |
| CN115423159A (en) | Photovoltaic power generation prediction method and device and terminal equipment | |
| CN115267586A (en) | Lithium battery SOH evaluation method | |
| CN112256735B (en) | Power consumption monitoring method and device, computer equipment and storage medium | |
| Kim et al. | Bayesian parameter identification in electrochemical model for lithium-ion batteries | |
| CN114660468A (en) | Retired battery state of charge prediction method and device and electronic equipment | |
| CN114219118A (en) | Method and system for predicting service life of intelligent electric meter based on D-S evidence theory | |
| CN111177131A (en) | Electricity consumption data detection method and device, computer equipment and storage medium |
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 |