Power line risk monitoring method, device, equipment and program

Abstract

The invention discloses an electric power circuit risk monitoring method comprising the steps that a first data set of a plurality of target electricity meters of a plurality of electric power circuits in a target power grid in a preset period is acquired, and the first data set is used for recording electric power data displayed by a single target electricity meter; for a single first data set, determining an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determining a target data category corresponding to the first data set according to the actual variance value and the actual discrete value; and dividing the plurality of first data sets into a plurality of second data sets according to the target data categories corresponding to the first data sets, and determining a risk level corresponding to each power line in the target power grid according to the plurality of second data sets. By classifying and dividing the data in advance, the accuracy and speed of data processing can be improved, and then the risk monitoring efficiency and monitoring accuracy of the power line are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of risk monitoring, and in particular to a method, device, electronic equipment, storage medium and program product for risk monitoring of power lines. Background Art

[0002] Electricity meters are commonly used instruments in electric energy systems. They can monitor various power data in the power grid and then control and adjust the electric energy system based on the monitored data.

[0003] In the prior art, the meter reading data is generally directly analyzed, and then the power energy system is controlled and adjusted according to the monitored data.

[0004] However, the reading data includes a lot of data, which leads to a slow data analysis speed when analyzing and processing the reading data, low efficiency in risk prediction of power lines in the power grid, and difficulty in meeting work monitoring needs. Summary of the Invention

[0005] The present invention provides a method, device, electronic equipment, storage medium and program product for monitoring the risk of power lines in a power grid, so as to improve the efficiency of risk monitoring of power lines in a power grid.

[0006] According to one aspect of the present invention, a method for monitoring risk of a power line is provided, comprising:

[0007] Acquire a first data set of a plurality of target electricity meters of a plurality of power lines in a target power grid within a preset period, wherein the first data set is used to record power data displayed by a single target electricity meter;

[0008] For a single first data set, determining an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determining a target data category corresponding to the first data set according to the actual variance value and the actual discrete value;

[0009] The plurality of first data sets are divided into a plurality of second data sets according to target data categories corresponding to the first data sets, and the risk level corresponding to each of the power lines in the target power grid is determined according to the plurality of second data sets.

[0010] According to another aspect of the present invention, there is provided a risk monitoring device for a power line, comprising:

[0011] a data acquisition module, configured to acquire a first data set of a plurality of target electricity meters of a plurality of power lines in a target power grid within a preset period, wherein the first data set is used to record power data displayed by a single target electricity meter;

[0012] a data category determination module, configured to determine, for a single first data set, an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determine a target data category corresponding to the first data set based on the actual variance value and the actual discrete value;

[0013] The risk level determination module is used to divide the plurality of first data sets into a plurality of second data sets according to the target data categories corresponding to the first data sets, and determine the risk level corresponding to each of the power lines in the target power grid according to the plurality of second data sets.

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

[0015] at least one processor; and

[0016] a memory communicatively connected to at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the power line risk monitoring method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, which are used to enable a processor to implement the power line risk monitoring method according to any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the computer program implements the power line risk monitoring method according to any embodiment of the present invention.

[0020] The technical solution of an embodiment of the present invention obtains first data sets from multiple target electricity meters corresponding to multiple power lines in a target power grid within a preset period, and for each first data set, determines the actual variance value and actual discrete value corresponding to the power data in the first data set, determines the target data category corresponding to the first data set based on the actual variance value and actual discrete value, and divides the multiple first data sets into multiple second data sets based on the target data category corresponding to the first data set. By pre-classifying and dividing the multiple data sets, the data processing speed can be improved. Determining the risk level corresponding to each power line in the target power grid based on the multiple second data sets can improve the risk monitoring efficiency and monitoring accuracy of the power lines.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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.

[0023] Figure 1 This is a flow chart of a method for monitoring risk of power lines provided according to the first embodiment of the present invention;

[0024] Figure 2 This is a flow chart of another power line risk monitoring method provided in accordance with the second embodiment of the present invention;

[0025] Figure 3 This is a schematic structural diagram of a risk monitoring device for power lines provided according to a third embodiment of the present invention;

[0026] Figure 4 It is a structural diagram of an electronic device for implementing the power line risk monitoring 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 solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description 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 numbers used in this way can be interchanged 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 variations thereof 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Example 1

[0030] Figure 1 A flowchart of a method for monitoring the risk of a power line is provided for the first embodiment of the present invention. This embodiment is applicable to detecting the risk level of a power line based on power data. The method can be executed by a risk monitoring device for a power line. The risk monitoring device for a power line can be implemented in the form of hardware and / or software. The risk monitoring device for a power line can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110. Acquire a first data set of multiple target electricity meters of multiple power lines in a target power grid within a preset period, wherein the first data set is used to record power data displayed by a single target electricity meter.

[0032] Among them, the preset period can be pre-set and used to indicate the period for collecting power data of the power line. By analyzing the power data collected within the period, the power line risk situation reflected by the power data of the power line within the preset period can be determined. For example, the preset period can be one minute, ten minutes, one hour or other periodic time. The present invention does not specifically limit the periodic time. The target power grid can be a power grid that needs to monitor the safety level of multiple power lines within the power grid. The power line can be a combination of conductors that transmit electric energy and related facilities. For example, high-voltage transmission lines, low-voltage distribution lines or other forms of power transmission lines of the same type. The target meter can be a device installed in the power line for measuring and recording power data. The power data can include data such as voltage data, current data, power factor data, active power data and reactive power data.

[0033] The first data set may be a data set obtained by recording data displayed by the target electricity meter within a preset period. The first data set may include power data and recording time corresponding to the power data.

[0034] Specifically, multiple pieces of power data displayed by multiple target electricity meters within a target power grid within a preset period can be recorded to obtain at least two first data sets corresponding to each of the multiple power lines within the target power grid. By recording the power data displayed by the electricity meters within the preset period and the time when the power data was generated, a more accurate data basis can be provided for determining the risk level of the power lines within the target power grid, thereby improving the accuracy of risk level prediction for the power lines within the target power grid.

[0035] S120 . For a single first data set, determine an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determine a target data category corresponding to the first data set according to the actual variance value and the actual discrete value.

[0036] The actual variance value may be a variance value corresponding to each first data set, calculated based on the power data in each first data set. The actual variance value may be used to characterize the central tendency of the power data in the first data set. The actual discrete value may be a discrete value corresponding to each first data set, calculated based on the power data in each first data set. The actual discrete value may be used to characterize the degree of discreteness of the power data in the first data set.

[0037] The target data category may be determined based on the actual variance value and the actual discrete value corresponding to the power data in each first data set, and is used to characterize the data category corresponding to the power data recorded in the first data set.

[0038] Optionally, before determining the actual variance value and the actual discrete value corresponding to the power data in the first data set, it may also include: for each first data set, determining the second recording time of the power data missing in the first data set based on the first recording time corresponding to the power data recorded in the first data set, and determining a plurality of first power data corresponding to the first recording time adjacent to the second recording time from the power data in the first data set; determining the second power data corresponding to the second recording time based on the plurality of first power data, and inserting the second power data into the first data set according to the second recording time.

[0039] The first recording time may be the time point of the power data already recorded in the first data set, and the second recording time may be the time point of the missing power data in the data set. For example, the first recording time in each first data set may be traversed to find the second recording time of all missing power data in each data set. The first power data may be the recorded power data adjacent to the missing data, and the second power data may be the power data at the missing time point calculated by interpolation or other methods, i.e., the power data at the second recording time.

[0040] Specifically, before determining the actual variance value and actual discrete value corresponding to the power data in the first data set, multiple first data sets corresponding to multiple power lines can be preprocessed to identify and fill in missing values in each first data set. For example, for each first data set, the second recording time of the missing power data in each data set can be determined based on the first recording time corresponding to the power data recorded in the data set. Furthermore, multiple first power data corresponding to the first recording time adjacent to the second recording time of the missing data are determined from the power data in the data set. The missing time point, i.e., the power data at the second recording time, is then determined by calculating the mean of the multiple first power data corresponding to the first recording time adjacent to the second recording time of the missing data and using interpolation or other methods. The calculated second recording time and the corresponding second power data are then inserted into the original first data set to ensure the integrity and accuracy of the data set. Preprocessing the first data set provides a data foundation for the subsequent calculation of the actual variance value and actual discrete value, thereby more accurately determining the target data category corresponding to each first data set.

[0041] Optionally, determining the actual variance value and actual discrete value corresponding to the power data in the first data set may include: determining the average value of multiple power data in the first data set, and determining the actual variance value of the multiple power data based on the average value; respectively determining the difference between each two power data in the first data set, and determining the actual discrete value corresponding to each first data set based on the absolute value of the difference between each two power data.

[0042] The average value of the power data may be the average value corresponding to the first data set calculated based on the value of the power data in the first data set. The average value of the power data in each first data set may be used to calculate the actual variance value corresponding to each first data set. The difference between each pair of power data may be the difference between each power data in the first data set and other power data in the first data set, and the absolute value of the difference between each pair of power data may be the absolute value of the difference between each power data in the first data set and other power data in the first data set.

[0043] Optionally, the actual variance values of the plurality of power data may be determined based on the average value of the first data set using the following formula:

[0044]

[0045] Where D is the actual variance value corresponding to the first data set, n is the number of power data in the first data set, and x i is the value of the i-th power data in the first data set, and a is the average value of the data.

[0046] Optionally, determining the target data category corresponding to the first data set based on the actual variance value and the actual discrete value may include: obtaining the standard variance value and the standard discrete value corresponding to multiple preset data categories; determining the target data category corresponding to each first data set from multiple preset data categories based on the actual variance values, the actual discrete values corresponding to the multiple first data sets and the standard variance value and the standard discrete value corresponding to the preset data categories, wherein the target data category is used to characterize the data category of the power data.

[0047] Among them, the preset data categories can be data categories that can be displayed by the target electric meter according to the categories of power data and the characteristics corresponding to each category. For example, data of categories such as voltage data, current data, power factor data, and power data. The standard deviation value can be the variance value or variance interval value of each category determined by analyzing the historical data or representative sample data corresponding to each preset data category. The standard deviation value is used to characterize the degree of dispersion or volatility of the data of this category. The standard discrete value can also be a discrete value or discrete interval value calculated based on historical data or sample data to measure the difference between data points.

[0048] Specifically, the power data in each first data set can be preprocessed first to fill in the missing values in each first data set, thereby ensuring the integrity and accuracy of the data in each data set. By calculating the average value of each first data set, and based on the power data value corresponding to the average power data corresponding to each first data set, the actual variance value is calculated using the variance formula, thereby accurately characterizing the central trend of the data. By calculating the absolute value of the difference between each two power data in the data set, the actual discrete value is determined to quantify the degree of discreteness of the data. Furthermore, the standard variance value and standard discrete value corresponding to multiple preset data categories are obtained, and the actual variance value and actual discrete value of the first data set are compared and matched with the standard value of the preset category to determine the target data category to which the data set belongs. By preprocessing the data, the integrity of the data can be improved, and the data set can be classified by the data in the preprocessed data set, which can provide data with higher accuracy for determining the risk level of the power line, thereby improving the efficiency and accuracy of predicting the risk level of the power line.

[0049] S130 , dividing the plurality of first data sets into a plurality of second data sets according to target data categories corresponding to the first data sets, and determining a risk level corresponding to each power line in the target power grid according to the plurality of second data sets.

[0050] Among them, the second data set can be a collection of multiple first data sets for recording the same target data category. Each second data set includes multiple first data sets of the same target data category. The second data set includes multiple first data sets, and the power line identification information corresponding to each first data set. The second data set can also include the actual variance value and actual discrete value corresponding to each first data set. The risk level can be determined based on the multiple first data sets corresponding to each power line, and the risk level of each power line after the end of a preset period. The higher the risk level, the higher the probability of failure or other abnormalities in the corresponding power line. By accurately predicting the risk level of each power line, the safety of power grid operation can be improved.

[0051] Specifically, multiple first datasets of the same category can be grouped into second datasets based on the target data category of each first dataset. Each second dataset contains a specific category of power datasets and related information (such as the power line identifier, actual variance value, and actual discrete value corresponding to each first dataset). By analyzing the datasets and related information recorded in the second datasets, the risk level of each power line in the target power grid is determined, thereby identifying lines with a high probability of potential failure or abnormality. This enables accurate prediction of the risk level of each power line, improving the safety of power grid operation.

[0052] The technical solution of an embodiment of the present invention obtains first data sets from multiple target electricity meters corresponding to multiple power lines in a target power grid within a preset period, and for each first data set, determines the actual variance value and actual discrete value corresponding to the power data in the first data set, determines the target data category corresponding to the first data set based on the actual variance value and actual discrete value, and divides the multiple first data sets into multiple second data sets based on the target data category corresponding to the first data set. By pre-classifying and dividing the multiple data sets, the data processing speed can be improved. Determining the risk level corresponding to each power line in the target power grid based on the multiple second data sets can improve the risk monitoring efficiency and monitoring accuracy of the power lines.

[0053] Example 2

[0054] Figure 2This is a flowchart of another power line risk monitoring method provided in the second embodiment of the present invention. Based on the above embodiment, this embodiment further refines the risk level corresponding to each power line in the target power grid determined according to multiple second data sets, and adds a technical feature of responding to the risk of the power line. Optionally, the risk level corresponding to each power line in the target power grid is determined according to multiple second data sets, including: inputting multiple second data sets into a pre-trained model, and determining the risk level corresponding to each power line in the target power grid through a risk prediction model. After determining the risk level corresponding to each power line in the target power grid according to multiple second data sets, it also includes: when the risk level of the power line is greater than the preset safety level, generating risk warning information corresponding to the power line, and sending the risk warning information to the target terminal. Figure 2 As shown, the method may include:

[0055] S210: Acquire a first data set of multiple target electricity meters of multiple power lines in a target power grid within a preset period, wherein the first data set is used to record power data displayed by a single target electricity meter.

[0056] S220 . For a single first data set, determine an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determine a target data category corresponding to the first data set according to the actual variance value and the actual discrete value.

[0057] S230. Divide the multiple first data sets into multiple second data sets according to the target data categories corresponding to the first data sets, input the multiple second data sets into a pre-trained model, and determine the risk level corresponding to each power line in the target power grid through the risk prediction model.

[0058] The risk prediction model can be trained based on sample data sets corresponding to multiple types of power data and the expected risk levels corresponding to the sample data sets, and is used to predict the risk level of each power line in the target power grid based on the data recorded in multiple second data sets. The sample data set can be a collection of sample data obtained by collecting and classifying historical data from multiple power lines in the target power grid over different historical periods, used to train a pre-built deep learning model. The historical data can include power data from a preset period before a fault or anomaly of different safety levels occurred on the power lines in the target power grid, as well as power data from a preset period when no faults occurred on the power lines in the target power grid. The sample data set used to train the deep learning model is obtained by calculating the actual variance and actual discrete value of the historical data and classifying the historical data based on the calculation results. The expected risk level can represent the probability of an operational fault or anomaly occurring on a power line in the target power grid. A higher risk level indicates a greater probability of a fault or anomaly occurring on the power line.

[0059] Optionally, a risk prediction model can be trained in the following manner: construct a target sample set based on sample data sets corresponding to multiple types of power data and the expected risk levels corresponding to the sample data sets; train a pre-constructed deep learning model based on the target sample set, and determine the trained deep learning model as a risk prediction model when the preset training end conditions are met.

[0060] The target sample set can be a data set used to train a deep learning model by collecting and preprocessing various types of power data from each power line in the target power grid over different preset periods. For example, the various types of power data can include historical records of power parameters such as current, voltage, power factor, and frequency for the power lines over different preset periods. The preprocessing can be performed by collecting and classifying historical records of multiple power lines in the target power grid over different historical periods. The historical records can include data from power lines in various states, such as normal operation, minor faults, and major faults.

[0061] The deep learning model can be a machine learning model based on a deep neural network, capable of automatically learning features and making predictions from large amounts of data. The deep learning model can be used to predict the risk level of power lines in a target power grid based on an input power data set. Deep learning models can include convolutional neural networks, recurrent neural networks, and long short-term memory networks, among others. The embodiments of the present invention do not specifically limit the type of deep learning model.

[0062] The preset end-of-training conditions may be rules or standards for determining whether to stop training when training a deep learning model. For example, the preset end-of-training conditions may include stopping training the deep learning model after a preset number of training rounds has been reached; stopping training the deep learning model when the loss function value (such as mean square error, etc.) drops below a preset threshold; stopping training the deep learning model when the performance (such as accuracy, recall, etc.) on the validation set does not improve within a certain number of rounds to avoid overfitting; and stopping training the deep learning model after a preset training time has been reached.

[0063] Specifically, multiple first data sets can be subdivided into multiple second data sets based on the target data category corresponding to each first data set. These second data sets are then fed into a deep learning risk prediction model pre-trained using multiple sample data sets (covering power data from different historical periods and corresponding expected risk levels) to determine the risk level of each power line in the target power grid. This can improve both the speed and accuracy of risk level prediction for power lines.

[0064] S240. When the risk level of the power line is greater than the preset safety level, generate risk warning information corresponding to the power line, and send the risk warning information to the target terminal.

[0065] The preset safety level can be a pre-set threshold used to determine whether the risk level of a power line exceeds an acceptable safety range. The preset safety level can be determined based on a combination of factors, including historical operating data of the power line, industry standards, safety regulations, and the operator's risk tolerance. For example, if the risk level of a power line exceeds the preset safety level, the power line is confirmed to have a high safety risk, requiring appropriate preventive measures or emergency treatment.

[0066] Among them, risk warning information can be a type of information used to notify relevant personnel or terminals that there are safety risks and safety risk levels in power lines. Risk warning information can include line identification information and risk level information of the power line. Line identification information can be information used to uniquely identify the power line. Line identification information can include information such as the line number, line name, or line geographical location of the power line. Through the line identification information, relevant personnel at the terminal can quickly locate the power line at risk after receiving the information. Risk level information can be the current risk level of the power line, which can be identified by a numerical value or other level identification information. The higher the risk level, the greater the probability of failure or abnormality in the power line, and the more urgent the preventive measures that need to be taken.

[0067] Among them, the target terminal refers to the device or system that receives risk warning information. For example, the monitoring center of the power company, the mobile device of the operation and maintenance personnel (such as mobile phones, tablets, etc.) or the automation control system, etc. The selection of the target terminal can depend on the urgency of the risk warning information, the type of measures to be taken, and the role and responsibilities of the recipient. Warning information of different risk levels can be sent to different terminals. For example, for urgent risk warning information, it may need to be sent directly to the mobile device of the operation and maintenance personnel so that quick action can be taken; for non-urgent warning information, it can be sent to the relevant departments for subsequent processing through the internal system of the power company; for those with lower risk levels, only the abnormal information can be recorded and not sent to the target terminal, waiting for subsequent maintenance for unified maintenance and adjustment.

[0068] Specifically, when the risk level of a power line exceeds a preset safety level, risk warning information containing line identification information and risk level information is generated, and this information is sent to a target terminal corresponding to the safety level, so that relevant maintenance personnel can understand and take corresponding measures in a timely manner to reduce the safety risks of the power line and the target power grid, thereby improving the safety of the power lines in the power grid and avoiding losses to the power grid caused by faults.

[0069] The technical solution of the embodiment of the present invention obtains the first data sets of multiple target electricity meters corresponding to multiple power lines in the target power grid within a preset period, and for a single first data set, determines the actual variance value and actual discrete value corresponding to the power data in the first data set, determines the target data category corresponding to the first data set according to the actual variance value and the actual discrete value, and divides the multiple first data sets into multiple second data sets according to the target data category corresponding to the first data set. By classifying and dividing the multiple data sets in advance, the data processing speed can be improved. The risk level corresponding to each power line in the target power grid is determined based on the multiple second data sets, which can improve the risk monitoring efficiency and monitoring accuracy of the power lines. When the risk level of the power line exceeds the preset safety level, by generating risk warning information containing line identification information and risk level information, and sending this information to the designated target terminal, the relevant maintenance personnel can promptly understand and take corresponding measures to reduce the safety risks of the power lines and the target power grid.

[0070] Example 3

[0071] Figure 3 This is a schematic diagram of the structure of a power line risk monitoring device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes a data acquisition module 310 , a data category determination module 320 and a risk level determination module 330 .

[0072] The data acquisition module 310 can be used to obtain a first data set of multiple target electricity meters of multiple power lines in a target power grid within a preset period, wherein the first data set is used to record the power data displayed by a single target electricity meter; the data category determination module 320 can be used to determine, for a single first data set, an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determine the target data category corresponding to the first data set based on the actual variance value and the actual discrete value; the risk level determination module 330 can be used to divide multiple first data sets into multiple second data sets based on the target data category corresponding to the first data set, and determine the risk level corresponding to each power line in the target power grid based on the multiple second data sets.

[0073] Furthermore, the risk monitoring device for power lines also includes a preprocessing module for determining, for each first data set, a second recording time of power data missing from the first data set based on the first recording time corresponding to the power data recorded in the first data set before determining the actual variance value and the actual discrete value corresponding to the power data in the first data set; determining, from the power data in the first data set, a plurality of first power data corresponding to first recording times adjacent to the second recording time; determining, based on the plurality of first power data, second power data corresponding to the second recording time; and inserting the second power data into the first data set based on the second recording time. The first data set includes power data and the recording time corresponding to the power data.

[0074] Furthermore, the data category determination module 320 is specifically used to: determine the average value of multiple power data in the first data set, and determine the actual variance value of the multiple power data based on the average value; respectively determine the difference between each two power data in the first data set, and determine the actual discrete value corresponding to each first data set based on the absolute value of the difference between each two power data.

[0075] Furthermore, the data category determination module 320 is specifically used to: obtain standard variance values and standard discrete values corresponding to multiple preset data categories; determine the target data category corresponding to each first data set from multiple preset data categories based on the actual variance values, actual discrete values corresponding to multiple first data sets and the standard variance values and standard discrete values corresponding to the preset data categories, wherein the target data category is used to characterize the target data category of the power data in the first data set.

[0076] Furthermore, the risk level determination module 330 is specifically used to: input multiple second data sets into a pre-trained model, wherein the risk prediction model is trained based on sample data sets corresponding to multiple types of power data and the expected risk levels corresponding to the sample data sets; and determine the risk level corresponding to each power line in the target power grid through the risk prediction model.

[0077] Furthermore, the risk monitoring device for power lines also includes a model training module, which is used to construct a target sample set based on sample data sets corresponding to multiple types of power data and the expected risk levels corresponding to the sample data sets, train a pre-constructed deep learning model based on the target sample set, and determine the trained deep learning model as a risk prediction model when the preset training end conditions are met.

[0078] Furthermore, the risk monitoring device for power lines also includes an alarm module, which is used to generate risk warning information corresponding to the power line when the risk level of the power line is greater than the preset safety level, and send the risk warning information to the target terminal; wherein the risk warning information includes line identification information and risk level information of the power line.

[0079] The power line risk monitoring device provided in the embodiment of the present invention can execute the power line risk monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0080] Example 4

[0081] Figure 4 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 processing, 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 claimed herein.

[0082] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. 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 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0083] Multiple 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 magnetic 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 via a computer network such as the Internet and / or various telecommunication networks.

[0084] The processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power line risk monitoring method.

[0085] In some embodiments, the power line risk monitoring method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power line risk monitoring method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the power line risk monitoring method in any other appropriate manner (e.g., via firmware).

[0086] Various embodiments of the systems and techniques described 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-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0087] 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0089] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0090] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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.

[0091] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0092] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0093] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the risk of power lines, characterized in that: include: Acquire a first data set of a plurality of target electricity meters of a plurality of power lines in a target power grid within a preset period, wherein the first data set is used to record power data displayed by a single target electricity meter; For a single first data set, determining an actual variance value and an actual discrete value corresponding to the power data in the first data set, and determining a target data category corresponding to the first data set according to the actual variance value and the actual discrete value; The plurality of first data sets are divided into a plurality of second data sets according to target data categories corresponding to the first data sets, and the risk level corresponding to each power line in the target power grid is determined according to the plurality of second data sets.

2. The method according to claim 1, characterized in that The first data set includes power data and recording time corresponding to the power data; Before determining the actual variance value and the actual discrete value corresponding to the power data in the first data set, the method further includes: For each first data set, determining a second recording time of power data missing from the first data set based on a first recording time corresponding to the power data recorded in the first data set, and determining a plurality of first power data corresponding to first recording times adjacent to the second recording time from the power data in the first data set; Second power data corresponding to a second recording time is determined based on the plurality of first power data, and the second power data is inserted into the first data set based on the second recording time.

3. The method according to claim 1, characterized in that Determining an actual variance value and an actual discrete value corresponding to the power data in the first data set includes: Determine an average value of the plurality of power data in the first data set, and determine actual variance values of the plurality of power data according to the average value; The difference between each two power data in the first data set is determined respectively, and the actual discrete value corresponding to each first data set is determined according to the absolute value of the difference between each two power data.

4. The method according to claim 1, characterized in that Determining a target data category corresponding to the first data set according to the actual variance value and the actual discrete value includes: Get the standard deviation and standard discrete values corresponding to multiple preset data categories; A target data category corresponding to each first data set is determined from multiple preset data categories based on actual variance values, actual discrete values corresponding to multiple first data sets and standard variance values and standard discrete values corresponding to preset data categories, wherein the target data category is used to characterize the data category of the power data in the first data set.

5. The method according to claim 1, characterized in that Determining a risk level corresponding to each power line in the target power grid according to the plurality of second data sets includes: Inputting multiple second data sets into a pre-trained model, wherein the risk prediction model is trained based on sample data sets corresponding to multiple types of power data and expected risk levels corresponding to the sample data sets; The risk level corresponding to each power line in the target power grid is determined through the risk prediction model.

6. The method according to claim 5, characterized in that The risk prediction model is trained in the following way: Construct a target sample set based on sample data sets corresponding to various types of power data and expected risk levels corresponding to the sample data sets; The pre-built deep learning model is trained according to the target sample set, and when the preset end training conditions are met, the trained deep learning model is determined as the risk prediction model.

7. The method according to claim 1, characterized in that After determining the risk level corresponding to each power line in the target power grid according to the plurality of second data sets, the method further includes: When the risk level of the power line is greater than the preset safety level, risk warning information corresponding to the power line is generated and sent to the target terminal; The risk warning information includes line identification information and risk level information of the power line.

8. An electronic device, characterized in that: Electronic equipment includes: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by one or more processors, the one or more processors implement the risk monitoring method for power lines according to any one of claims 1 to 7.

9. 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 line risk monitoring method according to any one of claims 1 to 7 when executed.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the risk monitoring method for a power line according to any one of claims 1 to 7 is implemented.

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