Power consumption anomaly detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a power consumption anomaly detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring power utilization parameters of a target distribution line; inputting the power utilization parameters of the target distribution line into a pre-trained power utilization anomaly detection model to obtain a power utilization detection result; and if the power utilization detection result is abnormal power utilization, determining the position of an abnormal power utilization line based on the power utilization parameters of the target distribution line. According to the technical scheme, real-time detection and accurate positioning of the abnormal power utilization line are realized through prediction of the power utilization abnormity detection model and determination of the position of the abnormal power utilization line.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply management, and in particular to a method, device, electronic equipment and storage medium for detecting abnormal power consumption. Background Art

[0002] Low-voltage distribution substations are large in number, widely distributed, and have complex power supply networks, which makes daily electricity consumption supervision difficult.

[0003] To address abnormal electricity usage issues such as electricity theft, two methods are currently commonly used: manual inspections and electronic technology detection and alarms. Manual inspections are unable to detect and handle abnormal electricity usage incidents in a timely manner, and electronic technology detection methods are unable to quickly and accurately locate the location of abnormal electricity usage.

[0004] In the process of implementing the present invention, it is found that there are at least the following technical problems in the prior art: the existing technical solutions for detecting abnormal power consumption have the problem that abnormal power lines cannot be detected in real time and accurately located. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for detecting abnormal power consumption, which realizes real-time detection and precise positioning of abnormal power consumption lines through prediction of an abnormal power consumption detection model and determination of the position of abnormal power consumption lines.

[0006] According to one aspect of the present invention, there is provided a method for detecting abnormal power consumption, comprising:

[0007] Obtaining power consumption parameters of the target distribution line;

[0008] Inputting the power consumption parameters of the target power distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result;

[0009] If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target power distribution line.

[0010] According to another aspect of the present invention, there is provided a device for detecting abnormal power consumption, comprising:

[0011] A line power consumption parameter acquisition module is used to obtain the power consumption parameters of the target distribution line;

[0012] A power consumption detection result prediction module, used to input the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result;

[0013] The abnormal power line location determination module is used to determine the abnormal power line location based on the power usage parameters of the target distribution line if the power usage detection result is abnormal power usage.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0015] at least one processor;

[0016] and a memory communicatively coupled to the at least one processor;

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power consumption anomaly detection method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power consumption anomaly detection method described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention obtains the power consumption parameters of the target distribution line, and then inputs the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result. If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target distribution line. The above technical solution realizes real-time detection and precise positioning of abnormal power consumption lines through prediction by the power consumption anomaly detection model and determination of the location of abnormal power consumption lines.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for detecting abnormal power consumption provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a method for detecting abnormal power consumption provided according to Embodiment 2 of the present invention;

[0024] Figure 3is a flow chart of a method for detecting abnormal power consumption provided according to Embodiment 3 of the present invention;

[0025] Figure 4 is a schematic diagram of the structure of a power consumption anomaly detection device provided according to Embodiment 4 of the present invention;

[0026] Figure 5 It is a structural schematic diagram of an electronic device for implementing the power consumption anomaly detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0029] Embodiment 1

[0030] Figure 1 This is a flow chart of a method for detecting abnormal power usage provided in the first embodiment of the present invention. This embodiment is applicable to the case of detecting power theft by direct access to electricity. The method can be executed by a power usage abnormality detection device. The power usage abnormality detection device can be implemented in the form of hardware and / or software. The power usage abnormality detection device can be configured in electronic devices such as terminals and / or servers. Figure 1 As shown, the method includes:

[0031] S110: Obtain power consumption parameters of a target distribution line.

[0032] The target power distribution line may be the inlet and outlet power lines of the user's power meter and / or the power distribution cabinet. The power consumption parameters of the target power distribution line include but are not limited to: monitoring point location information, monitoring point Internet Protocol address, monitoring point loop power, complex rate power, power factor, active power, reactive power, current, voltage, power demand, current phase angle and voltage phase angle.

[0033] Exemplarily, the power consumption parameters of the target distribution line can be collected in real time by a power consumption parameter collection device, and the power consumption parameter collection device is connected in series and / or parallel to the target distribution line; then the power consumption parameter collection device uploads the power consumption parameters of the target distribution line to electronic devices such as terminals and / or servers.

[0034] S120: Inputting the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result.

[0035] In an embodiment of the present invention, the power consumption anomaly detection model is a pre-trained deep convolutional neural network model, which can be used for power consumption anomaly detection of distribution lines.

[0036] Specifically, the power consumption parameters of the target distribution line are used as input data of the power consumption anomaly detection model. The power consumption anomaly detection model makes predictions based on the input power consumption parameters of the target distribution line to obtain power consumption detection results. The power consumption detection results may be power consumption anomaly, power consumption normal, or power theft risk level.

[0037] S130: If the power consumption detection result is abnormal power consumption, determine the location of the abnormal power consumption line based on the power consumption parameters of the target distribution line.

[0038] The abnormal power line location refers to the location of the target distribution line with abnormal power consumption. For example, the abnormal power line location may be longitude and latitude coordinates or location coordinates in other coordinate systems, which are not specifically limited here.

[0039] Specifically, the location of the abnormal power consumption line may be determined based on location-related parameters in the power consumption parameters of the target power distribution line.

[0040] In some optional embodiments, the power consumption anomaly detection model may be a K-means clustering algorithm, a support vector machine, or a Bayesian network, etc., which is not specifically limited here.

[0041] The technical solution of the embodiment of the present invention obtains the power consumption parameters of the target distribution line, and then inputs the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result. If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target distribution line. The above technical solution realizes real-time detection and precise positioning of abnormal power consumption lines through prediction by the power consumption anomaly detection model and determination of the location of abnormal power consumption lines.

[0042] Embodiment 2

[0043] Figure 2 A flowchart of a method for detecting power anomaly provided in Embodiment 2 of the present invention, the method of this embodiment can be combined with the various optional schemes in the power anomaly detection method provided in the above embodiments. The power anomaly detection method provided in this embodiment has been further optimized. Optionally, the training step of the power anomaly detection model includes: obtaining multiple power consumption parameters of a sample distribution line; converting the multiple power consumption parameters of the sample distribution line into a power consumption parameter matrix, wherein the power consumption parameter matrix is ​​an m×n matrix, m represents the number of times the power consumption parameters are collected, and n represents the number of power consumption parameter items; normalizing the power consumption parameter matrix to obtain a normalized power consumption parameter matrix; inputting the normalized power consumption parameter matrix into a deep convolutional neural network model to be trained to obtain a power consumption prediction result; determining the model loss based on the power consumption prediction result and the actual power consumption result, and updating the model parameters of the deep convolutional neural network model based on the model loss until the model training stop condition is met to obtain a power anomaly detection model.

[0044] like Figure 2 As shown, the method includes:

[0045] S210. Acquire multiple power consumption parameters of a sample distribution line; convert the multiple power consumption parameters of the sample distribution line into a power consumption parameter matrix, wherein the power consumption parameter matrix is ​​an m×n matrix, m represents the number of times the power consumption parameters are collected, and n represents the number of power consumption parameter items.

[0046] The sample power distribution lines refer to the power lines in and out of the user's power meter and / or power distribution cabinet as samples. The multiple power consumption parameters include but are not limited to the monitoring point location information collected at multiple historical time points, the monitoring point Internet Protocol address, the power of the monitoring point loop, the complex rate power, power factor, active power, reactive power, current, voltage, power demand, current phase angle and voltage phase angle.

[0047] In the embodiment of the present invention, the number of times the power consumption parameters are collected is the number of model training samples, and the number of power consumption parameter items is the number of power consumption parameter items. ijRepresents the jth power consumption parameter item of the ith training sample in the power consumption parameter matrix.

[0048] S220: normalize the power usage parameter matrix to obtain a normalized power usage parameter matrix.

[0049] S230. Input the normalized electricity consumption parameter matrix into the deep convolutional neural network model to be trained to obtain the electricity consumption prediction result; determine the model loss based on the electricity consumption prediction result and the actual electricity consumption result, and update the model parameters of the deep convolutional neural network model based on the model loss until the model training stop condition is met to obtain the electricity consumption anomaly detection model.

[0050] The power consumption prediction result is the output result of the deep convolutional neural network model, and the actual power consumption result is the training label.

[0051] Exemplarily, the deep convolutional neural network model includes a convolution layer, a pooling layer, and a fully connected layer. The convolution layer is composed of convolution kernels with learnable parameters to effectively extract the normalized power consumption parameter matrix of the input. During the forward propagation process, the convolution kernel uses the normalized power consumption parameter matrix for convolution calculation. Furthermore, the convolution kernel moves on the result of data preprocessing of the normalized power consumption parameter matrix according to a predefined step size to retain the local features of the normalized power consumption parameter matrix and extract more abstract features. The pooling layer is designed to minimize the dimension of the features while retaining important information. At the end of the deep convolutional neural network model, the neurons are rearranged to form the last fully connected layer. Further, the output layer is processed by the softmax function to obtain the power consumption prediction result, which can be the probability of abnormal or normal power consumption.

[0052] S240: Obtain power consumption parameters of the target distribution line.

[0053] S250: Inputting the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result.

[0054] S260: If the power consumption detection result shows that the power consumption is abnormal, determine the location of the abnormal power consumption line based on the power consumption parameters of the target distribution line.

[0055] The technical solution of the embodiment of the present invention obtains multiple power consumption parameters of the sample distribution line, and then converts the multiple power consumption parameters of the sample distribution line into a power consumption parameter matrix, wherein the power consumption parameter matrix is ​​an m×n matrix, m represents the number of times the power consumption parameters are collected, and n represents the number of power consumption parameter items; then the power consumption parameter matrix is ​​normalized to obtain a normalized power consumption parameter matrix; then the normalized power consumption parameter matrix is ​​input into the deep convolutional neural network model to be trained to obtain a power consumption prediction result; then the model loss is determined based on the power consumption prediction result and the actual power consumption result, and the model parameters of the deep convolutional neural network model are updated based on the model loss until the model training stop condition is met to obtain a power consumption anomaly detection model. The above technical solution realizes the training of the power anomaly detection model and provides a model basis for the subsequent real-time detection of abnormal power lines.

[0056] Embodiment 3

[0057] Figure 3 A flow chart of a method for detecting abnormal power consumption is provided for Embodiment 3 of the present invention. The method of this embodiment can be combined with the various optional schemes in the method for detecting abnormal power consumption provided in the above embodiments. The method for detecting abnormal power consumption provided in this embodiment is further optimized. Optionally, the power consumption parameters of the target distribution line include monitoring point location information and / or monitoring point Internet Protocol address; accordingly, determining the abnormal power consumption line location based on the power consumption parameters of the target distribution line includes: determining the abnormal power consumption line location based on the monitoring point location information and / or the monitoring point Internet Protocol address.

[0058] like Figure 3 As shown, the method includes:

[0059] S310. Obtain power usage parameters of a target distribution line, where the power usage parameters of the target distribution line include monitoring point location information and / or monitoring point Internet Protocol address.

[0060] S320: Input the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result.

[0061] S330: If the power consumption detection result is abnormal power consumption, determine the location of the abnormal power consumption line based on the monitoring point location information and / or the Internet Protocol address of the monitoring point.

[0062] The monitoring point location information refers to the location of the power consumption parameter collection device, which can be longitude and latitude coordinates or coordinates in other coordinate systems. The monitoring point Internet Protocol (IP) address refers to the IP address used by the power consumption parameter collection device.

[0063] Optionally, the monitoring point location information includes the user meter node location and the distribution cabinet node location; accordingly, determining the abnormal power line location based on the monitoring point location information includes: determining the abnormal power line location based on the user meter node location and the distribution cabinet node location.

[0064] Among them, the user meter node location refers to the location of the power consumption parameter collection device located at the user meter node, and the distribution cabinet node location refers to the location of the power consumption parameter collection device located at the distribution cabinet node.

[0065] Exemplarily, abnormal electricity usage may be electricity theft. In this embodiment, the power lines where electricity theft occurs can be locked through the user's meter node location and the distribution cabinet node location, that is, the abnormal power line location is obtained, so that the user can conduct targeted investigation of electricity theft.

[0066] Optionally, the location of the abnormal power consumption line is determined based on the Internet Protocol address of the monitoring point, including: determining the location of the user meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line based on the Internet Protocol address of the monitoring point; determining the location of the abnormal power consumption line based on the location of the user meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line.

[0067] For example, the IP address of the monitoring point of the electricity parameter collection device can be used to accurately locate the positions of the user meter locations and distribution cabinets on both sides of the target distribution line, thereby determining the location of the abnormal electricity consumption line based on the positions of the user meter locations and distribution cabinets on both sides of the target distribution line, so that users can perform targeted abnormal electricity consumption behaviors.

[0068] The technical solution of the embodiment of the present invention achieves accurate positioning of abnormal power lines by determining the location of abnormal power lines based on monitoring point location information and / or monitoring point Internet Protocol addresses, so as to facilitate user investigation.

[0069] Embodiment 4

[0070] Figure 4 This is a schematic diagram of the structure of a power consumption anomaly detection device provided in Embodiment 4 of the present invention. Figure 4 As shown, the device comprises:

[0071] A line power consumption parameter acquisition module 410 is used to acquire power consumption parameters of a target distribution line;

[0072] The power consumption detection result prediction module 420 is used to input the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result;

[0073] The abnormal power line location determination module 430 is used to determine the abnormal power line location based on the power usage parameters of the target distribution line when the power usage detection result is abnormal power usage.

[0074] The technical solution of the embodiment of the present invention obtains the power consumption parameters of the target distribution line, and then inputs the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result. If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target distribution line. The above technical solution realizes real-time detection and precise positioning of abnormal power consumption lines through prediction by the power consumption anomaly detection model and determination of the location of abnormal power consumption lines.

[0075] In some optional implementations, the target power distribution lines are power lines entering and exiting a user's power meter and / or a power distribution cabinet.

[0076] In some optional implementations, the power consumption parameters of the target power distribution line include:

[0077] At least one of the monitoring point location information, the monitoring point Internet Protocol address, the power of the monitoring point loop, the complex rate power, the power factor, the active power, the reactive power, the current, the voltage, the electric energy demand, the current phase angle and the voltage phase angle.

[0078] In some optional implementations, the electrical anomaly detection device comprises:

[0079] The power consumption anomaly detection model training module is used to obtain multiple power consumption parameters of a sample distribution line; convert the multiple power consumption parameters of the sample distribution line into a power consumption parameter matrix, wherein the power consumption parameter matrix is ​​an m×n matrix, m represents the number of times the power consumption parameters are collected, and n represents the number of power consumption parameter items; normalize the power consumption parameter matrix to obtain a normalized power consumption parameter matrix; input the normalized power consumption parameter matrix into a deep convolutional neural network model to be trained to obtain a power consumption prediction result; determine a model loss based on the power consumption prediction result and the actual power consumption result, and update the model parameters of the deep convolutional neural network model based on the model loss until the model training stop condition is met to obtain a power consumption anomaly detection model.

[0080] In some optional embodiments, the power usage parameters of the target distribution line include monitoring point location information and / or monitoring point Internet Protocol addresses;

[0081] Correspondingly, the abnormal power line location determination module 430 includes:

[0082] An abnormal line determination unit is used to determine the location of the abnormal power line based on the monitoring point location information and / or the monitoring point Internet Protocol address.

[0083] In some optional implementations, the monitoring point location information includes the user meter node location and the distribution cabinet node location;

[0084] Accordingly, the abnormal line determination unit can be specifically used for:

[0085] The location of the abnormal power line is determined based on the user meter node location and the distribution cabinet node location.

[0086] In some optional implementations, the abnormal line determination unit may be specifically configured to:

[0087] Determine the location of the user's electric meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line based on the Internet Protocol address of the monitoring point;

[0088] The location of the abnormal power consumption line is determined based on the location of the user's electric meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line.

[0089] The power consumption anomaly detection device provided in the embodiment of the present invention can execute the power consumption anomaly detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] Embodiment 5

[0091] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0092] like Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The I / O interface 15 is also connected to the bus 14.

[0093] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0094] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a power consumption anomaly detection method, which includes:

[0095] Obtaining power consumption parameters of the target distribution line;

[0096] Inputting the power consumption parameters of the target power distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result;

[0097] If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target power distribution line.

[0098] In some embodiments, the power usage anomaly detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power usage anomaly detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the power usage anomaly detection method in any other appropriate manner (e.g., by means of firmware).

[0099] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0101] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0103] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0104] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting abnormal power consumption, characterized in that: include: Obtaining power consumption parameters of the target distribution line; Inputting the power consumption parameters of the target power distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result; If the power consumption detection result is abnormal power consumption, the location of the abnormal power consumption line is determined based on the power consumption parameters of the target power distribution line.

2. The method according to claim 1, characterized in that The target power distribution lines are power lines entering and exiting the user's power meter and / or power distribution cabinet.

3. The method according to claim 1, characterized in that The power consumption parameters of the target distribution line include: At least one of the monitoring point location information, the monitoring point Internet Protocol address, the power of the monitoring point loop, the complex rate power, the power factor, the active power, the reactive power, the current, the voltage, the electric energy demand, the current phase angle and the voltage phase angle.

4. The method according to claim 1, characterized in that: The training steps of the power consumption anomaly detection model include: Obtain multiple power consumption parameters of sample distribution lines; Converting multiple power consumption parameters of the sample power distribution line into a power consumption parameter matrix, wherein the power consumption parameter matrix is ​​an m×n matrix, m represents the number of times the power consumption parameters are collected, and n represents the number of power consumption parameter items; Normalizing the power consumption parameter matrix to obtain a normalized power consumption parameter matrix; Inputting the normalized power consumption parameter matrix into a deep convolutional neural network model to be trained to obtain a power consumption prediction result; The model loss is determined based on the power consumption prediction result and the actual power consumption result, and the model parameters of the deep convolutional neural network model are updated based on the model loss until the model training stop condition is met to obtain a power consumption anomaly detection model.

5. The method according to claim 1, characterized in that The power consumption parameters of the target distribution line include monitoring point location information and / or monitoring point Internet Protocol address; Correspondingly, determining the location of the abnormal power consumption line based on the power consumption parameter of the target power distribution line includes: The location of the abnormal power line is determined based on the monitoring point location information and / or the monitoring point Internet Protocol address.

6. The method according to claim 5, characterized in that The monitoring point location information includes the user meter node location and the distribution cabinet node location; Accordingly, determining the location of the abnormal power line based on the monitoring point location information includes: The location of the abnormal power line is determined based on the user meter node location and the distribution cabinet node location.

7. The method according to claim 5, characterized in that Determining the location of the abnormal power line based on the Internet Protocol address of the monitoring point includes: Determine the location of the user's electric meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line based on the Internet Protocol address of the monitoring point; The location of the abnormal power consumption line is determined based on the location of the user's electric meter on one side of the target distribution line and the location of the distribution cabinet on the other side of the target distribution line.

8. A device for detecting abnormal power consumption, characterized in that: include: A line power consumption parameter acquisition module is used to obtain the power consumption parameters of the target distribution line; A power consumption detection result prediction module, used to input the power consumption parameters of the target distribution line into a pre-trained power consumption anomaly detection model to obtain a power consumption detection result; The abnormal power line location determination module is used to determine the abnormal power line location based on the power usage parameters of the target distribution line if the power usage detection result is abnormal power usage.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power consumption anomaly detection method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power consumption anomaly detection method according to any one of claims 1 to 7 when executed.

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