Intelligent operation and maintenance centralized control method and system for electrical energy

Abstract

The invention relates to an electric power operation and maintenance technology, and discloses an electric power energy intelligent operation and maintenance centralized control method, which comprises the following steps: acquiring current power supply distribution data of a current electric power energy network, generating a current power supply distribution diagram according to the power supply distribution data, performing feature extraction on the current power supply distribution diagram to obtain current power supply distribution features, generating a power supply demand distribution diagram according to pre-acquired standard power supply demand data, performing feature extraction on the power supply demand distribution diagram to obtain power supply demand distribution features, calculating the difference between the power supply demand distribution features and the current power supply distribution features to obtain power supply difference features, and sending the power supply difference features to a server; and outputting power supply adjustment data according to the power supply difference characteristics by using a pre-trained power operation and maintenance model, calculating the power supply power adjustment amount of each power distribution node according to the power supply adjustment data, and adjusting the power supply power of each power distribution node according to the power supply power adjustment amount. The invention further provides an intelligent operation and maintenance centralized control system for the electrical energy. The efficiency of centralized control of power operation and maintenance can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power operation and maintenance technology, and in particular to an intelligent operation and maintenance centralized control method and system for electric power energy. Background Art

[0002] Driven by energy transformation and digitalization, the power industry is undergoing unprecedented changes. With the increasing complexity of the power grid structure and the continuous improvement of operation and maintenance requirements, the traditional operation and maintenance model can no longer meet the current needs of power grid operation and management.

[0003] At present, faced with the huge scale and complex structure of power grids, the efficiency and accuracy of existing power operation and maintenance centralized control methods are difficult to meet the needs. Summary of the invention

[0004] The present invention provides an intelligent operation and maintenance centralized control method and system for electric energy, the main purpose of which is to solve the problem of low efficiency and low precision of existing electric power operation and maintenance centralized control methods.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for intelligent operation and maintenance of electric energy, comprising:

[0006] Acquire current power supply distribution data of the current electric energy network, and generate a current power supply distribution map according to the power supply distribution data;

[0007] Extracting features from the current power supply distribution map to obtain current power supply distribution features;

[0008] Generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features;

[0009] Calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic;

[0010] Outputting power supply adjustment data according to the power supply difference characteristics using a pre-trained power operation and maintenance model;

[0011] Calculating the power supply adjustment amount of each distribution node in the electric power energy network according to the power supply adjustment data;

[0012] The power supply power of each power distribution node is adjusted according to the power supply power adjustment amount.

[0013] Optionally, obtaining location information of each distribution node in the electric power energy network;

[0014] Constructing a distribution node location map according to the location information of each distribution node;

[0015] Connecting the distribution nodes on the distribution node location diagram according to the pre-acquired electric power network data to obtain a distribution node connection diagram;

[0016] Generate a power supply power representation diagram of each power distribution node according to the current power supply distribution data;

[0017] The power supply power representation diagram of each distribution node is added to the corresponding distribution node in the distribution node connection diagram to obtain the current power supply distribution diagram.

[0018] Optionally, the current power supply distribution map is divided into a plurality of image blocks of preset sizes to obtain an image block set;

[0019] Performing grayscale processing on all image blocks in the image block set to obtain a grayscale image block set;

[0020] Calculating the grayscale mean of each grayscale image block in the grayscale image block set, and using the grayscale mean to replace the grayscale values ​​of all pixels of the corresponding grayscale image block to obtain a grayscale mean image block set;

[0021] The grayscale feature matrix of the image is obtained by combining the grayscale mean image block set;

[0022] The image grayscale feature matrix is ​​normalized to obtain current power supply distribution features.

[0023] Optionally, the normalizing the image grayscale feature matrix to obtain current power supply distribution features includes:

[0024] The normalization process is expressed by the following formula:

[0025]

[0026] Among them, Z ij represents the element in the i-th row and j-th column of the current power distribution feature, x ij represents the element in the i-th row and j-th column of the image grayscale feature matrix, x max Represents the maximum element in the image grayscale feature matrix, x min Represents the smallest element in the image grayscale feature matrix.

[0027] Optionally, before generating the power supply demand distribution map according to the pre-acquired standard power supply demand data, the method further includes:

[0028] The standard power supply demand data of each distribution node is calculated using the following formula:

[0029]

[0030] Among them, Ei represents the standard power supply demand data of the ith distribution node, T represents the current temperature, and T O It represents the preset standard temperature, F is a binary function, which takes 1 when it is a holiday and 0 when it is not a holiday, and H is a preset proportional coefficient used to control the increase in power demand during holidays.

[0031] Optionally, the calculating a difference between the power supply demand distribution feature and the current power supply distribution feature to obtain a power supply difference feature includes:

[0032] Subtracting the current power supply distribution characteristics from the power supply demand distribution characteristics to obtain a difference characteristic matrix;

[0033] The difference feature matrix is ​​normalized to obtain power supply difference features.

[0034] Optionally, the power supply difference feature is convolved using a convolution layer of the power operation and maintenance model to obtain a convolution feature;

[0035] Using the pooling layer of the electric power operation and maintenance model to perform maximum pooling processing on the convolution feature set to obtain pooling features;

[0036] Activating the pooled features using the activation layer of the power operation and maintenance model to obtain activated features;

[0037] The fully connected layer of the power operation and maintenance model is used to output the power supply adjustment data according to the activation features and preset weight parameters and bias items.

[0038] Optionally, the calculating the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data includes:

[0039] Confirming the connection relationship between the parent node and the child node in the electric power network according to the electric power network data;

[0040] According to the connection relationship, the number of parent nodes and child nodes of each power distribution node is determined to obtain parent-child node relationship data;

[0041] The power supply adjustment amount of each power distribution node is calculated according to the parent-child node relationship data and the power supply adjustment data.

[0042] Optionally, the calculating the power supply adjustment amount of each power distribution node according to the parent-child node relationship data and the power supply adjustment data includes:

[0043] The power supply adjustment amount of each distribution node is calculated using the following formula:

[0044]

[0045] Among them, W i is the power supply adjustment of the i-th distribution node, ΔP i represents the power supply adjustment data of the ith distribution node, n represents the number of child nodes of the ith distribution node, ΔP j represents the power supply adjustment data of the jth child node of the i-th distribution node, α p is the preset subnode influence coefficient, ΔD i represents the power supply adjustment data of the parent node of the i-th power distribution node, α d It is the preset parent node influence coefficient.

[0046] In order to solve the above problems, the present invention also provides an intelligent operation and maintenance centralized control system for electric energy, the device comprising:

[0047] An image generation module is used to obtain current power supply distribution data of the current electric power energy network and generate a current power supply distribution map according to the power supply distribution data;

[0048] A feature extraction module is used to extract features from the current power supply distribution map to obtain current power supply distribution features, generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features;

[0049] A difference calculation module, used for calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic;

[0050] A data output module, configured to output power supply adjustment data according to the power supply difference characteristics by using a pre-trained power operation and maintenance model;

[0051] A calculation and adjustment module is used to calculate the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data, and adjust the power supply power of each distribution node according to the power supply adjustment amount.

[0052] The embodiment of the present invention obtains the current power supply distribution data of the current electric energy network, generates the current power supply distribution map according to the power supply distribution data, improves the efficiency of subsequent feature extraction, extracts features from the current power supply distribution map to obtain the current power supply distribution features, generates a power supply demand distribution map according to the pre-acquired standard power supply demand data, extracts features from the power supply demand distribution map to obtain the power supply demand distribution features, calculates the difference between the power supply demand distribution features and the current power supply distribution features to obtain the power supply difference features, improves the efficiency of outputting power supply adjustment data, uses a pre-trained power operation and maintenance model to output the power supply adjustment data according to the power supply difference features, calculates the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data, improves the accuracy of adjusting the power supply, and adjusts the power supply of each distribution node according to the power supply adjustment amount. Therefore, the intelligent operation and maintenance centralized control method and system of electric energy proposed by the present invention can solve the problems of low efficiency and low accuracy of existing power operation and maintenance centralized control methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of a method for intelligent operation and maintenance of electric energy provided by an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a process for generating a current power supply distribution diagram provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a process for extracting features from a front power supply distribution diagram provided by an embodiment of the present invention;

[0056] Figure 4 A functional module diagram of an intelligent operation and maintenance centralized control system for electric energy provided by one embodiment of the present invention;

[0057] Figure 5 A schematic diagram of the structure of an electronic device for implementing the intelligent operation and maintenance centralized control method of an electric energy source provided in one embodiment of the present invention.

[0058] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0060] The embodiment of the present application provides a method for intelligent operation and maintenance of electric energy. The execution subject of the method for intelligent operation and maintenance of electric energy includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for intelligent operation and maintenance of electric energy can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0061] Reference Figure 1 FIG. 1 is a flow chart of a method for intelligent operation and maintenance of electric energy provided by an embodiment of the present invention. In this embodiment, the method for intelligent operation and maintenance of electric energy includes:

[0062] S1. Obtain current power supply distribution data of the current electric power energy network, and generate a current power supply distribution map according to the power supply distribution data.

[0063] In an embodiment of the present invention, the electric power energy network includes a power generation layer, a transmission layer, a transformation layer and a distribution layer.

[0064] In detail, the power generation layer refers to a power plant, which may be a thermal power plant, a hydroelectric power plant, a wind power plant, a solar power plant, or a nuclear power plant.

[0065] In detail, the transmission layer is used to transmit the electric energy generated by the power plant to the load center, and generally has a grid structure.

[0066] In detail, the substation layer is used to transform voltage, and generally includes hub substations, regional substations, and terminal substations.

[0067] In detail, the distribution node layer is used to distribute electric energy to various user terminals, includes a large number of grid structure distributed distribution nodes, and a distribution main node is connected to multiple distribution sub-nodes.

[0068] In the embodiment of the present invention, the current power supply distribution data refers to the real-time power supply data of the current electric energy network, including the power supply power data of each distribution node in the electric energy network.

[0069] In the embodiment of the present invention, refer to Figure 2As shown, a schematic diagram of a process for generating a current power supply distribution map is provided in an embodiment of the present invention, wherein the current power supply distribution data of the current electric energy network is obtained, and the current power supply distribution map is generated according to the power supply distribution data, including:

[0070] S21, obtaining the location information of each distribution node in the electric power energy network;

[0071] S22, constructing a distribution node location map according to the location information of each distribution node;

[0072] S23, connecting the distribution nodes on the distribution node location diagram according to the pre-acquired electric power energy network data to obtain a distribution node connection diagram;

[0073] S24, generating a power supply power representation diagram of each power distribution node according to the current power supply distribution data;

[0074] S25. Add the power supply power representation diagram of each distribution node to the corresponding distribution node in the distribution node connection diagram to obtain a current power supply distribution diagram.

[0075] In detail, the location information of each distribution node may refer to longitude and latitude information.

[0076] In detail, constructing a distribution node location map according to the location information of each distribution node refers to drawing each distribution node on a blank map with a preset latitude and longitude ratio.

[0077] In detail, the electric power energy network data is data containing the connection relationship between all main distribution nodes and sub-distribution nodes.

[0078] In detail, the power supply power representation diagram may be a circular diagram, the size of which is related to the power supply power of the corresponding power distribution node.

[0079] In the embodiment of the present invention, by acquiring the current power supply distribution data of the current electric energy network and generating a current power supply distribution map according to the power supply distribution data, the efficiency of subsequent feature extraction of the current power supply distribution map is improved.

[0080] S2. Extract features from the current power supply distribution map to obtain current power supply distribution features.

[0081] In the embodiment of the present invention, the feature extraction of the current power supply distribution map is performed by dividing the current power supply distribution map into a plurality of image blocks, extracting local features of each image block and combining them into new features to obtain the current power supply distribution features.

[0082] In the embodiment of the present invention, refer to Figure 3As shown, a schematic diagram of a process of extracting features from a previous power supply distribution map is provided in an embodiment of the present invention, wherein the feature extraction from the current power supply distribution map to obtain the current power supply distribution features includes:

[0083] S31, dividing the current power supply distribution map into a plurality of image blocks of preset sizes to obtain an image block set;

[0084] S32, performing grayscale processing on all image blocks in the image block set to obtain a grayscale image block set;

[0085] S33, calculating the grayscale mean of each grayscale image block in the grayscale image block set, and using the grayscale mean to replace the grayscale values ​​of all pixels of the corresponding grayscale image block to obtain a grayscale mean image block set;

[0086] S34, obtaining an image grayscale feature matrix by combining the grayscale mean image block set;

[0087] S35, normalizing the image grayscale feature matrix to obtain current power supply distribution features.

[0088] In detail, dividing the current power supply distribution map into multiple image blocks of preset sizes refers to dividing the current power supply distribution map into multiple small-sized rectangular image blocks, each image block has the same size, and its size can be set according to the size of the current power supply distribution map.

[0089] In detail, the grayscale processing is the process of converting a color image into a grayscale image. A color image is usually composed of three channels: red (R), green (G), and blue (B). The value range of each channel is generally 0 to 255. Grayscale processing is to merge the information of these three channels into one channel, so that the image becomes a grayscale image with only brightness information, which can improve the efficiency and accuracy of image feature extraction.

[0090] In detail, the method of obtaining an image grayscale feature matrix by combining the grayscale mean image block set refers to recombining the grayscale mean image blocks in the grayscale mean image block set into a new image, and then using the grayscale value of each grayscale mean image block as an element of the matrix to obtain an image grayscale feature matrix.

[0091] In the embodiment of the present invention, the normalization process for the image grayscale feature matrix is ​​to map all elements to between 0 and 1 based on the maximum value and the minimum value of the elements in the image grayscale feature matrix.

[0092] In detail, the normalizing process of the image grayscale feature matrix to obtain the current power supply distribution feature includes:

[0093] The normalization process is expressed by the following formula:

[0094]

[0095] Among them, Z ij represents the element in the i-th row and j-th column of the current power distribution feature, x ij represents the element in the i-th row and j-th column of the image grayscale feature matrix, x max Represents the maximum element in the image grayscale feature matrix, x min Represents the smallest element in the image grayscale feature matrix.

[0096] In the embodiment of the present invention, by extracting features from the current power supply distribution graph, current power supply distribution features are obtained, thereby improving the efficiency of subsequently acquiring power supply difference features.

[0097] S3. Generate a power supply demand distribution map according to the pre-acquired standard power supply demand data, perform feature extraction on the power supply demand distribution map, and obtain power supply demand distribution features.

[0098] In an embodiment of the present invention, the pre-acquired standard power supply demand data is a standard power supply demand of a distribution node calculated based on historical power consumption data of each distribution node and other relevant factors (such as temperature, day and night, holidays, etc.).

[0099] In the embodiment of the present invention, before generating the power supply demand distribution map according to the pre-acquired standard power supply demand data, the method further includes:

[0100] The standard power supply demand data of each distribution node is calculated using the following formula:

[0101]

[0102] Among them, E i represents the standard power supply demand data of the ith distribution node, T represents the current temperature, and T O It represents the preset standard temperature, F is a binary function, which takes 1 when it is a holiday and 0 when it is not a holiday, and H is a preset proportional coefficient used to control the increase in power demand during holidays.

[0103] In the embodiment of the present invention, the step of generating a power supply demand distribution map according to the pre-acquired standard power supply demand data is the same as the step of generating a current power supply distribution map according to the power supply distribution data, which will not be described in detail here.

[0104] In the embodiment of the present invention, the step of extracting features from the power supply demand distribution map to obtain power supply demand distribution features is the same as the step of extracting features from the current power supply distribution map, and will not be repeated here.

[0105] In the embodiment of the present invention, a power supply demand distribution map is generated according to pre-acquired standard power supply demand data, and features are extracted from the power supply demand distribution map to obtain power supply demand distribution features, thereby improving the efficiency of subsequent calculation of power supply difference features.

[0106] S4. Calculate the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic.

[0107] In the embodiment of the present invention, the calculating the difference between the power supply demand distribution feature and the current power supply distribution feature to obtain the power supply difference feature is performed by subtracting the previous power supply distribution feature from the power supply demand distribution feature to obtain the power supply difference feature.

[0108] In the embodiment of the present invention, the calculating the difference between the power supply demand distribution feature and the current power supply distribution feature to obtain the power supply difference feature includes:

[0109] Subtracting the current power supply distribution characteristics from the power supply demand distribution characteristics to obtain a difference characteristic matrix;

[0110] The difference feature matrix is ​​normalized to obtain power supply difference features.

[0111] In the embodiment of the present invention, a power supply difference feature is obtained by calculating the difference between the power supply demand distribution feature and the current power supply distribution feature, thereby improving the efficiency of subsequently outputting power supply adjustment data according to the power supply difference feature.

[0112] S5. Outputting power supply adjustment data according to the power supply difference characteristics using a pre-trained power operation and maintenance model.

[0113] In an embodiment of the present invention, the power operation and maintenance model is a neural network model, which can calculate the difference between each distribution node and the standard power according to the power supply difference characteristics.

[0114] In an embodiment of the present invention, the pre-trained power operation and maintenance model is obtained by acquiring a large amount of power supply data and standard power supply data, converting the acquired data into images for feature extraction, and calculating the differences, so as to obtain a large amount of power supply difference data as training data, and then labeling the standard power supply adjustment data of each distribution node for each training data, using a neural network model to predict the power supply adjustment parameters of each distribution node according to the training data, and then calculating the loss value of the prediction result and the standard annotation, and using the loss value to continuously iterate and optimize the neural network model, and finally obtaining the power operation and maintenance model.

[0115] In an embodiment of the present invention, the method of outputting power supply adjustment data according to the power supply difference characteristics by using a pre-trained power operation and maintenance model includes:

[0116] Using the convolution layer of the power operation and maintenance model to perform convolution processing on the power supply difference feature to obtain a convolution feature;

[0117] Using the pooling layer of the electric power operation and maintenance model to perform maximum pooling processing on the convolution feature set to obtain pooling features;

[0118] Activating the pooled features using the activation layer of the power operation and maintenance model to obtain activated features;

[0119] The fully connected layer of the power operation and maintenance model is used to output the power supply adjustment data according to the activation features and preset weight parameters and bias items.

[0120] In detail, the convolution processing is to set a convolution kernel, use the convolution kernel to slide on the power supply difference feature according to a preset step size, calculate the dot product between the data covered by the convolution after each slide and the convolution kernel, and obtain the convolution feature after summarizing all calculation results. The convolution kernel is a small-sized matrix.

[0121] In detail, the maximum pooling process is a local area based downsampling method, which selects the maximum value as the output in the local area of ​​the input data. Specifically, for a given input matrix or tensor, the maximum pooling process divides it into several non-overlapping local areas, and then selects the maximum value in each area as the output of the area.

[0122] In detail, the activation process is to introduce nonlinear factors into the pooling features using a preset activation function.

[0123] In detail, the fully connected layer of the power operation and maintenance model outputs the power supply adjustment data according to the activation feature and the preset weight parameters and bias items, by performing a dot product operation on the activation feature and the preset weight parameters, and adding a bias item.

[0124] In detail, the weight parameters are the ties between neurons, which determine the strength of information transmission between neurons. In a simple fully connected neural network layer, if there are input neurons and output neurons, then the weight parameters are usually a matrix of . These weights represent the strength of the connection from the input neurons to the output neurons.

[0125] In detail, the bias term is an adjustable parameter in the neural network, which provides an additional offset for the neuron. For each neuron, there is a corresponding bias term. The existence of the bias term allows the neuron to have a non-zero output even when the input is zero, increasing the flexibility of the neural network.

[0126] In the embodiment of the present invention, the power supply adjustment data is output according to the power supply difference characteristics by using a pre-trained power operation and maintenance model, thereby improving the efficiency of subsequent calculation of the power supply power adjustment amount of each distribution node.

[0127] S6. Calculate the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data.

[0128] In the embodiment of the present invention, since each distribution node has a parent node and a distribution node is connected to multiple child nodes, the power adjustment of each distribution node needs to comprehensively consider the power supply adjustment data of itself and its parent node and child nodes.

[0129] In an embodiment of the present invention, the step of calculating the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data includes:

[0130] Confirming the connection relationship between the parent node and the child node in the electric power network according to the electric power network data;

[0131] According to the connection relationship, the number of parent nodes and child nodes of each power distribution node is determined to obtain parent-child node relationship data;

[0132] The power supply adjustment amount of each power distribution node is calculated according to the parent-child node relationship data and the power supply adjustment data.

[0133] In detail, the calculating the power supply adjustment amount of each power distribution node according to the parent-child node relationship data and the power supply adjustment data includes:

[0134] The power supply adjustment amount of each distribution node is calculated using the following formula:

[0135]

[0136] Among them, W i is the power supply adjustment of the i-th distribution node, ΔP i represents the power supply adjustment data of the ith distribution node, n represents the number of child nodes of the ith distribution node, ΔP j represents the power supply adjustment data of the jth child node of the i-th distribution node, α p is the preset subnode influence coefficient, ΔD i represents the power supply adjustment data of the parent node of the i-th power distribution node, α d It is the preset parent node influence coefficient.

[0137] In detail, the preset child node influence coefficient is used to control the influence of the power supply adjustment data of the child node of the distribution node on the power supply power adjustment amount, and the preset parent node influence coefficient is used to control the influence of the power supply adjustment data of the parent node of the distribution node on the power supply power adjustment amount.

[0138] In the embodiment of the present invention, by calculating the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data, the efficiency of subsequent adjustment of the power supply power of each distribution node is improved.

[0139] S7. Adjust the power supply of each power distribution node according to the power supply adjustment amount.

[0140] In an embodiment of the present invention, by converting the current power supply data and the standard power supply demand data into an image, extracting features from the image and calculating the difference between the features, the power supply difference characteristics of the electric power energy network are obtained, thereby improving the efficiency of the analysis. By utilizing a pre-trained power operation and maintenance model to output the power supply adjustment parameters of each distribution node according to the power supply difference characteristics, the power supply power adjustment amount of each distribution node is calculated by comprehensively considering the influence of the child nodes and parent nodes of each distribution node, and then the power supply power of each distribution node is adjusted, thereby improving the efficiency of the current energy operation and maintenance centralized control.

[0141] like Figure 4 The figure shows a functional module diagram of an intelligent operation and maintenance centralized control system for electric energy provided by one embodiment of the present invention.

[0142] The intelligent operation and maintenance centralized control system 100 of electric energy described in the present invention can be installed in an electronic device. According to the functions to be implemented, the intelligent operation and maintenance centralized control system 100 of electric energy can include an image generation module 101, a feature extraction module 102, a difference calculation module 103, a data output module 104 and a calculation adjustment module 105. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0143] In this embodiment, the functions of each module / unit are as follows:

[0144] The image generation module 101 is used to obtain current power supply distribution data of the current electric power energy network and generate a current power supply distribution map according to the power supply distribution data;

[0145] The feature extraction module 102 is used to extract features from the current power supply distribution map to obtain current power supply distribution features, generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features;

[0146] The difference calculation module 103 is used to calculate the difference between the power supply demand distribution feature and the current power supply distribution feature to obtain a power supply difference feature;

[0147] The data output module 104 is used to output power supply adjustment data according to the power supply difference characteristics by using a pre-trained power operation and maintenance model;

[0148] The calculation and adjustment module 105 is used to calculate the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data, and adjust the power supply power of each distribution node according to the power supply adjustment amount.

[0149] In detail, each module described in the intelligent operation and maintenance centralized control system 100 of electric energy in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The same technical means are used as the method for intelligent operation and maintenance of electric energy and can produce the same technical effects, so they will not be repeated here.

[0150] like Figure 5 , which is a schematic diagram of the structure of an electronic device of an intelligent operation and maintenance centralized control method of electric energy provided by an embodiment of the present invention.

[0151] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an intelligent operation and maintenance centralized control program for electric energy.

[0152] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a smart operation and maintenance centralized control program for electric energy, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0153] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of an intelligent operation and maintenance centralized control program of an electric energy source, but also can be used to temporarily store data that has been output or is to be output.

[0154] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0155] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in this embodiment, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0156] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0157] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0158] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0159] The memory 11 in the electronic device 1 stores an intelligent operation and maintenance centralized control program of electric energy, which is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0160] Acquire current power supply distribution data of the current electric energy network, and generate a current power supply distribution map according to the power supply distribution data;

[0161] Extracting features from the current power supply distribution map to obtain current power supply distribution features;

[0162] Generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features;

[0163] Calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic;

[0164] Outputting power supply adjustment data according to the power supply difference characteristics using a pre-trained power operation and maintenance model;

[0165] Calculating the power supply adjustment amount of each distribution node in the electric power energy network according to the power supply adjustment data;

[0166] The power supply power of each power distribution node is adjusted according to the power supply power adjustment amount.

[0167] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0168] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0169] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0170] Acquire current power supply distribution data of the current electric energy network, and generate a current power supply distribution map according to the power supply distribution data;

[0171] Extracting features from the current power supply distribution map to obtain current power supply distribution features;

[0172] Generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features;

[0173] Calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic;

[0174] Outputting power supply adjustment data according to the power supply difference characteristics using a pre-trained power operation and maintenance model;

[0175] Calculating the power supply adjustment amount of each distribution node in the electric power energy network according to the power supply adjustment data;

[0176] The power supply power of each power distribution node is adjusted according to the power supply power adjustment amount.

[0177] In the embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0178] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0179] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0180] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0181] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0182] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0183] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for intelligent operation and maintenance of electric energy, characterized in that: The method comprises: Acquire current power supply distribution data of the current electric energy network, and generate a current power supply distribution map according to the power supply distribution data; Extracting features from the current power supply distribution map to obtain current power supply distribution features; Generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features; Calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic; Outputting power supply adjustment data according to the power supply difference characteristics using a pre-trained power operation and maintenance model; Calculating the power supply adjustment amount of each distribution node in the electric power energy network according to the power supply adjustment data; The power supply power of each power distribution node is adjusted according to the power supply power adjustment amount.

2. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: The obtaining of current power supply distribution data of the current electric energy network and generating a current power supply distribution map according to the power supply distribution data includes: Obtaining location information of each distribution node in the electric power energy network; Constructing a distribution node location map according to the location information of each distribution node; Connecting the distribution nodes on the distribution node location diagram according to the pre-acquired electric power energy network data to obtain a distribution node connection diagram; Generate a power supply power representation diagram of each power distribution node according to the current power supply distribution data; The power supply power representation diagram of each distribution node is added to the corresponding distribution node in the distribution node connection diagram to obtain the current power supply distribution diagram.

3. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: The extracting features of the current power supply distribution map to obtain current power supply distribution features includes: Dividing the current power supply distribution map into a plurality of image blocks of preset sizes to obtain an image block set; Performing grayscale processing on all image blocks in the image block set to obtain a grayscale image block set; Calculating the grayscale mean of each grayscale image block in the grayscale image block set, and using the grayscale mean to replace the grayscale values ​​of all pixels of the corresponding grayscale image block to obtain a grayscale mean image block set; The grayscale feature matrix of the image is obtained by combining the grayscale mean image block set; The image grayscale feature matrix is ​​normalized to obtain current power supply distribution features.

4. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: The normalizing the image grayscale feature matrix to obtain current power supply distribution features includes: The normalization process is expressed by the following formula: Among them, Z ij represents the element in the i-th row and j-th column of the current power distribution feature, x ij represents the element in the i-th row and j-th column of the image grayscale feature matrix, x max Represents the maximum element in the image grayscale feature matrix, x min Represents the smallest element in the image grayscale feature matrix.

5. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: Before generating the power supply demand distribution map according to the pre-acquired standard power supply demand data, the method further includes: The standard power supply demand data of each distribution node is calculated using the following formula: Among them, E i represents the standard power supply demand data of the ith distribution node, T represents the current temperature, and T O It represents the preset standard temperature, F is a binary function, which takes 1 when it is a holiday and 0 when it is not a holiday, and H is a preset proportional coefficient used to control the increase in power demand during holidays.

6. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: The calculating the difference between the power supply demand distribution feature and the current power supply distribution feature to obtain a power supply difference feature includes: Subtracting the current power supply distribution characteristics from the power supply demand distribution characteristics to obtain a difference characteristic matrix; The difference feature matrix is ​​normalized to obtain power supply difference features.

7. The intelligent operation and maintenance centralized control method of electric energy according to claim 1, characterized in that: The using a pre-trained power operation and maintenance model to output power supply adjustment data according to the power supply difference characteristics includes: Using the convolution layer of the power operation and maintenance model to perform convolution processing on the power supply difference feature to obtain a convolution feature; Using the pooling layer of the electric power operation and maintenance model to perform maximum pooling processing on the convolution feature set to obtain pooling features; Activating the pooled features using the activation layer of the power operation and maintenance model to obtain activated features; The fully connected layer of the power operation and maintenance model is used to output the power supply adjustment data according to the activation features and preset weight parameters and bias items.

8. The intelligent operation and maintenance centralized control method of electric energy according to claim 2, characterized in that: The calculating the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data includes: Confirming the connection relationship between the parent node and the child node in the electric power network according to the electric power network data; According to the connection relationship, the number of parent nodes and child nodes of each power distribution node is determined to obtain parent-child node relationship data; The power supply adjustment amount of each power distribution node is calculated according to the parent-child node relationship data and the power supply adjustment data.

9. The intelligent operation and maintenance centralized control method of electric energy according to claim 8, characterized in that: The calculating the power supply adjustment amount of each power distribution node according to the parent-child node relationship data and the power supply adjustment data includes: The power supply adjustment amount of each distribution node is calculated using the following formula: Among them, W i is the power supply adjustment of the i-th distribution node, ΔP i represents the power supply adjustment data of the ith distribution node, n represents the number of child nodes of the ith distribution node, ΔP j represents the power supply adjustment data of the jth child node of the i-th distribution node, α p is the preset subnode influence coefficient, ΔD i represents the power supply adjustment data of the parent node of the i-th power distribution node, α d It is the preset parent node influence coefficient.

10. An intelligent operation and maintenance centralized control system for electric energy, characterized in that: The device comprises: An image generation module is used to obtain current power supply distribution data of the current electric power energy network and generate a current power supply distribution map according to the power supply distribution data; A feature extraction module is used to extract features from the current power supply distribution map to obtain current power supply distribution features, generate a power supply demand distribution map based on pre-acquired standard power supply demand data, and extract features from the power supply demand distribution map to obtain power supply demand distribution features; A difference calculation module, used for calculating the difference between the power supply demand distribution characteristic and the current power supply distribution characteristic to obtain a power supply difference characteristic; A data output module, configured to output power supply adjustment data according to the power supply difference characteristics by using a pre-trained power operation and maintenance model; A calculation and adjustment module is used to calculate the power supply adjustment amount of each distribution node in the electric energy network according to the power supply adjustment data, and adjust the power supply power of each distribution node according to the power supply adjustment amount.

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