Power transmission line jump height prediction method, device, equipment, medium and program product

Abstract

The invention relates to a power transmission line jump height prediction method and device, equipment, a medium and a program product. The method comprises the following steps: firstly, obtaining monitoring data of a power transmission line carrying ice coating, and then inputting the monitoring data into a preset prediction model for prediction to obtain the jump height of the power transmission line; wherein the preset prediction model is obtained based on training of the sample monitoring data and the initial prediction model; the initial prediction model comprises a convolutional neural network and a long short-term memory network; the monitoring data comprises at least one of meteorological data, icing data and vibration data. According to the method, the monitoring data of the power transmission line are input into the preset prediction model in a model prediction mode, the jump height of the power transmission line is predicted, and compared with an existing method for determining the jump height of the power transmission line based on an empirical formula and an experiment, the accuracy of the jump height of the power transmission line is improved to a certain degree.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, equipment, medium and program product for predicting jump height of a transmission line. Background Art

[0002] Wire-Icing-Cover refers to the phenomenon that moisture in the clouds, fog or air collides with the surface of the ground wire of the transmission line and freezes at 0℃ or lower. When the external temperature or meteorological conditions change and cause the ice to fall off, the wire will vibrate elastically and produce significant jumping phenomenon. This jumping may cause electrical contact between wires or between wires and towers, resulting in serious accidents.

[0003] In addition, the greater the amplitude of elastic vibration, the higher the possibility of an accident. Therefore, estimating the jump height of the transmission line when the ice falls off and taking different countermeasures based on the estimated different jump heights to avoid serious accidents have become technical problems that need to be solved urgently. At present, the jump height of the transmission line when the ice falls off is mainly predicted through empirical formulas, experimental methods and numerical simulation methods.

[0004] However, the above-mentioned method for predicting the jump height of the transmission line has the problem of low accuracy. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, equipment, medium and program product for predicting the jump height of a transmission line that can accurately predict the jump height of a transmission line in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for predicting the jump height of a transmission line, comprising:

[0007] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0008] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0009] In one embodiment, the above-mentioned inputting the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line includes:

[0010] Preprocessing the monitoring data to obtain preprocessed monitoring data;

[0011] The preprocessed monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0012] In one embodiment, inputting the preprocessed monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line includes:

[0013] Performing feature extraction on the preprocessed monitoring data to obtain a feature vector of the monitoring data;

[0014] Inputting the feature vector of the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0015] In one embodiment, the above preprocessing includes data cleaning, data completion, and data normalization; the preprocessing of the monitoring data to obtain the preprocessed monitoring data includes:

[0016] Performing data cleaning on the monitoring data to obtain the monitored data after cleaning;

[0017] Performing data completion on the monitored data after cleaning to obtain the monitored data after completion;

[0018] Performing data normalization on the monitored data after completion to obtain the monitored data after normalization.

[0019] In one embodiment, the above method further includes:

[0020] Obtaining sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line;

[0021] Training an initial prediction model according to the sample monitoring data to obtain a preset prediction model.

[0022] In one embodiment, the above obtaining sample monitoring data includes:

[0023] Obtaining sample meteorological data, sample icing data, and sample vibration data of the transmission line;

[0024] Inputting the sample meteorological data, sample icing data, and sample vibration data into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined according to the relationship between meteorological data, icing data, transmission line vibration data, and transmission line jump height.

[0025] In a second aspect, the present application further provides a prediction device for the jump height of a transmission line, including:

[0026] An acquisition module, used to acquire monitoring data of an ice-covered power transmission line; the monitoring data includes at least one of meteorological data, ice coverage data, and vibration data;

[0027] The prediction module is used to input the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0028] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0030] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0032] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0033] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0035] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0036] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0037] The above-mentioned prediction method, device, equipment, medium and program product for the jumping height of a transmission line. The method includes: first obtaining monitoring data of a transmission line with ice coating, and then inputting the monitoring data into a preset prediction model for prediction to obtain the jumping height of the transmission line; wherein, the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network; the monitoring data includes at least one of meteorological data, ice coating data, and vibration data. The above method predicts the jumping height of the transmission line by inputting the monitoring data of the transmission line into the preset prediction model in a model prediction manner. Compared with the existing methods for determining the jumping height of a transmission line based on empirical formulas and experiments, the above method improves the accuracy of the jumping height of the transmission line to a certain extent. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0039] Figure 1 It is an application environment diagram of the prediction method for the jumping height of a transmission line in an embodiment;

[0040] Figure 2 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in an embodiment;

[0041] Figure 3 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0042] Figure 4 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0043] Figure 5 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0044] Figure 6 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0045] Figure 7 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0046] Figure 8 It is a schematic flowchart of the prediction method for the jumping height of a transmission line in another embodiment;

[0047] Fig. 9 It is a structural block diagram of a device for predicting jump height of a power transmission line in one embodiment;

[0048] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The importance of safe and stable operation of power transmission lines to the development of the national economy is self-evident. As a core component of national infrastructure, the power system directly affects industrial production, residents' lives, national defense security, etc. With the continuous growth of energy demand and the continuous expansion of the scale of power transmission lines, especially in the context of the development of long-distance power transmission and cross-regional power grid interconnection, the operating environment of power transmission lines has become increasingly complex and changeable, and the climate challenges they face are becoming increasingly severe.

[0051] Icing is one of the main threats to power transmission lines in cold climates. In low-temperature environments, transmission lines often bear additional mechanical loads due to icing, which may lead to serious consequences such as increased conductor sag, damage to insulator strings, and even collapse of towers, and even directly affect the safe and stable operation of the power system. When changes in external temperature or meteorological conditions cause ice to fall off, the conductor will vibrate elastically due to the sudden release of force, resulting in significant jumping. This de-icing jump may cause violent vertical or horizontal movement of the conductor, thereby causing electrical contact between conductors or between conductors and towers, resulting in short circuits, arc discharges, and even serious accidents such as conductor breakage. Especially in high-latitude cold areas and high-altitude mountainous areas, or complex terrain or high-voltage transmission lines, excessive fluctuations in jump height will exacerbate these risks.

[0052] Therefore, estimating the jump height of the transmission line when the ice falls off, and taking different countermeasures based on the estimated different jump heights to avoid the occurrence of serious accidents has become a technical problem that needs to be solved urgently. By studying and calculating the de-icing jump height, it is not only possible to predict the dynamic response of the conductor under extreme working conditions, provide important design basis and early warning standards, but also provide scientific support for line maintenance strategies and emergency treatment plans. At present, the jump height of the transmission line when the ice falls off is mainly predicted through empirical formulas, experimental methods and numerical simulation methods. However, the above-mentioned transmission line jump height prediction method has the problem of low accuracy. The present application aims to solve this problem.

[0053] After introducing the background technology of the method for predicting the jump height of a transmission line provided by the embodiment of the present application, the following briefly describes the implementation environment involved in the method for predicting the jump height of a transmission line provided by the embodiment of the present application. The method for predicting the jump height of a transmission line provided by the embodiment of the present application can be applied to Figure 1 In the implementation environment shown. The implementation environment includes a server 104, which can be implemented by an independent server 104 or a server cluster composed of multiple servers 104, and a data storage system 102 can store data that the server 104 needs to process. The data storage system 102 can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the server 104 can obtain monitoring data of the transmission line carrying ice, and input the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0054] In other possible implementations, the image retrieval method provided in the embodiment of the present application can also be applied to terminals, which can be, but are not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc.

[0055] In one embodiment, Figure 2 As shown in FIG. 1 , a method for predicting the jump height of a transmission line is provided. The method is applied to Figure 1 The computer device in the example is used to illustrate, including the following steps:

[0056] S201. Acquire monitoring data of an ice-covered power transmission line.

[0057] The monitoring data includes at least one of meteorological data, icing data and vibration data. The meteorological data refers to the meteorological data around the transmission line, including the temperature, humidity and wind speed around the transmission line, the icing data includes the thickness of ice on the transmission line, and the vibration data refers to the amplitude and regularity of the vibration of the transmission line.

[0058] It should be noted that the monitoring data can be real-time monitoring data or non-real-time monitoring data.

[0059] In an embodiment of the present application, when it is necessary to predict the jump height of an ice-covered power transmission line, the meteorological data of the transmission line can be first obtained from the meteorological agency corresponding to the transmission line, and the ice data and vibration data on the transmission line can be obtained by installing sensors on the transmission line.

[0060] Optionally, monitoring data within a preset time period may be obtained through sensors and meteorological agencies, and the obtained monitoring data may be stored in a database. When the jump height of the transmission line needs to be predicted, the corresponding monitoring data may be directly obtained from the database.

[0061] S202: Input the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0062] Among them, the preset prediction model is trained based on sample monitoring data and the initial prediction model, and the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0063] In an embodiment of the present application, after the meteorological data, icing data and transmission line vibration data on the transmission line are obtained as mentioned above, the meteorological data, icing data and transmission line vibration data on the transmission line can be input into a pre-trained preset prediction model to predict the possible jump height of the transmission line to obtain the jump height of the transmission line.

[0064] The method for predicting the jump height of a transmission line provided in an embodiment of the present application first obtains monitoring data of a transmission line with ice, and then inputs the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line; wherein the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network; the monitoring data includes at least one of meteorological data, ice data, and vibration data. The above method inputs the monitoring data of the transmission line into a preset prediction model by means of model prediction to predict the jump height of the transmission line. Compared with the existing method of determining the jump height of the transmission line based on empirical formulas and experiments, the above method improves the accuracy of the jump height of the transmission line to a certain extent.

[0065] In one embodiment, Figure 2 Based on the embodiment shown, the process of obtaining the jump height of the transmission line can be described as follows: Figure 3 As shown, the above S202 "inputting the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line" includes:

[0066] S301 , preprocessing the monitoring data to obtain preprocessed monitoring data.

[0067] The preprocessing includes at least one of normalization processing, noise reduction processing and data enhancement processing.

[0068] In an embodiment of the present application, after the monitoring data is acquired as described above, the monitoring data may be preprocessed to obtain the monitoring data after the preprocessing. Optionally, the monitoring data may be subjected to normalization processing, noise reduction processing, and data enhancement processing in sequence to obtain the monitoring data after the preprocessing. Optionally, the monitoring data may be subjected to normalization processing to obtain the monitoring data after the normalization processing. Optionally, the monitoring data may be subjected to noise reduction processing to obtain the monitoring data after the noise reduction processing. Optionally, the monitoring data may be subjected to data enhancement processing to obtain the monitoring data after the data enhancement processing.

[0069] S302: Input the preprocessed monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0070] In the embodiment of the present application, after the preprocessed monitoring data is determined as described above, the preprocessed monitoring data can be input into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0071] Optionally, an implementation method for obtaining the jump height of the transmission line according to the preprocessed monitoring data is also provided, such as Figure 4 As shown, the above S302 "inputting the pre-processed monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line" includes:

[0072] S401: Extract features from the preprocessed monitoring data to obtain feature vectors of the monitoring data.

[0073] Among them, feature extraction refers to selecting meteorological features (such as temperature, humidity, wind speed, wind direction, etc.) that are strongly correlated with de-icing jump height through methods such as correlation analysis and principal component analysis (PCA). It should be noted that feature extraction can also reduce the number of features through dimensionality reduction technology, reduce model complexity, and prevent overfitting.

[0074] In the embodiment of the present application, after the preprocessed monitoring data is obtained as described above, feature extraction may be further performed on the preprocessed monitoring data to obtain a feature vector of the monitoring data.

[0075] Optionally, feature extraction aims to extract the most representative and predictive features from a large amount of raw data to simplify the model and improve prediction accuracy.

[0076] S402: Input the characteristic vector of the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0077] In the embodiment of the present application, after the characteristic vector of the detection data is determined as described above, the characteristic vector of the monitoring data can be input into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0078] The method for predicting the jump height of a transmission line provided in an embodiment of the present application improves the accuracy of the jump height of the transmission line by preprocessing and extracting features from monitoring data and predicting the jump height of the transmission line based on the monitoring data after preprocessing and feature extraction.

[0079] In one embodiment, Figure 2 Based on the embodiment shown, the above preprocessing includes data cleaning processing, data completion processing, and data normalization processing; the acquisition process of the preprocessed monitoring data can be described as follows: Figure 5 As shown, the above S301 "preprocessing the monitoring data to obtain preprocessed monitoring data" includes:

[0080] S501: Perform data cleaning on monitoring data to obtain cleaned monitoring data.

[0081] In the embodiment of the present application, after the monitoring data is acquired as described above, data cleaning processing may be performed on the acquired monitoring data to obtain cleaned monitoring data.

[0082] Optionally, commonly used cleaning methods include noise processing, outlier detection processing, data consistency verification processing and removal processing.

[0083] In the process of noise processing, the moving average method is needed to smooth the data. The moving average method can be expressed by the following formula (1):

[0084]

[0085] in, is the average value of the monitoring data. , and Refers to three adjacent monitoring data.

[0086] In the process of outlier detection, the Z-score method is generally used to identify outliers. The Z-score method can be expressed by the following formula (2):

[0087]

[0088] in, Any monitoring data. and The data consistency check ensures that the logic and format of each record are consistent. Removing duplicate data avoids model overfitting and improves calculation accuracy.

[0089] S502: Perform data completion processing on the cleaned monitoring data to obtain completed monitoring data.

[0090] In the embodiment of the present application, after the cleaned monitoring data is obtained as described above, data completion processing may be continued on the cleaned monitoring data to obtain completed monitoring data.

[0091] The main purpose of data completion is to fill in missing values ​​so that the model can make full use of all available data. Common data completion methods include interpolation and mean filling.

[0092] The interpolation method estimates missing values ​​by using the relationship between existing data points. For example, linear interpolation linearly infers missing values ​​based on the data points before and after the missing point, while polynomial interpolation uses polynomial functions for more complex estimation. Mean filling is another simple and effective method that replaces missing values ​​with the mean of the feature column. This method is suitable for situations where the proportion of missing data is small and the data distribution is relatively uniform. The Lagrange interpolation formula is shown in the following formula (3):

[0093]

[0094] in, and are any two data in the monitoring data, is the position that needs to be interpolated, is the total amount of monitoring data.

[0095] S503: Perform data normalization processing on the completed monitoring data to obtain normalized monitoring data.

[0096] In the embodiment of the present application, after the cleaned monitoring data is obtained as described above, data completion processing may be continued on the cleaned monitoring data to obtain completed monitoring data.

[0097] It should be noted that data from different data sources may have different dimensions and value ranges. In order to make the model training process more stable, it is usually necessary to normalize the data. The commonly used normalization method is standardization. The normalization formula is as follows:

[0098]

[0099] in, is the normalized data, and are the mean and standard deviation of the data respectively.

[0100] The monitoring data preprocessing method provided in the embodiment of the present application makes the monitoring data used to predict the jumping height more accurate by performing data cleaning, data completion and data normalization on the monitoring data, thereby making the final predicted jumping height more accurate.

[0101] In one embodiment, Figure 2 Based on the embodiment shown, Figure 6 As shown, the above method also includes:

[0102] S203. Obtain sample monitoring data.

[0103] Among them, the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line.

[0104] The sample meteorological data refers to the meteorological data around the transmission lines, including the temperature, humidity and wind speed around the transmission lines. The sample icing data includes the thickness of ice on the transmission lines. The sample vibration data refers to the amplitude and regularity of vibration of the transmission lines.

[0105] Among them, the jump height of the transmission line refers to the jump height generated by numerical simulation based on sample meteorological data, sample icing data, and sample vibration data. Optionally, numerical simulation software such as Fluent, ANSYS and other software can be used to establish accurate physical models of conductors and transmission towers to simulate icing conditions under various meteorological conditions, simulate the icing and deicing process of conductors under different environmental conditions, and calculate the vibration response and jump height of the conductor to generate a large amount of data. In this way, the diversity of training data is increased, thereby improving the generalization ability and accuracy of subsequent preset prediction models.

[0106] It should be noted that the sample monitoring data can be real-time monitoring data or non-real-time monitoring data.

[0107] In an embodiment of the present application, when it is necessary to train a preset prediction model, sample meteorological data of the transmission line can be first obtained from the meteorological agency corresponding to the transmission line, and sample icing data and sample vibration data on the transmission line can be obtained by installing sensors on the transmission line, and data simulation can be performed based on the sample meteorological data, sample icing data, and sample vibration data to obtain a sample jump height of the corresponding transmission line.

[0108] Optionally, sample monitoring data within a preset time period may be obtained through sensors and meteorological agencies, and the obtained sample monitoring data may be stored in a database. When the preset prediction model needs to be trained, the corresponding sample monitoring data may be directly obtained from the database.

[0109] S204: Train the initial prediction model according to the sample monitoring data to obtain a preset prediction model.

[0110] Among them, the initial prediction model can select a convolutional neural network model and a long short-term memory network. The convolutional neural network model is used to learn the complex nonlinear relationship between meteorological monitoring data and the de-icing jump height of the transmission line. The convolutional neural network (CNN) has a powerful feature extraction capability and can automatically extract spatial features from the input data. The model structure of CNN includes several main layers: input layer, convolution layer, activation layer, pooling layer, and output layer. The convolution layer is the core of CNN, which is responsible for extracting local features of the input data. It performs convolution operations with the local area of ​​the input data through the convolution kernel (filter) to generate a feature map. Multiple layers of convolution layers can be superimposed to capture high-order features in the data.

[0111] The long short-term memory network processes time series information such as meteorological data and wire vibration data to capture the impact of meteorological changes and ice cover dynamics on wire jumps. The long short-term memory network (LSTM) is used to capture long-range dependencies in time series data. The model structure of LSTM usually includes an input layer, an LSTM layer, a fully connected layer, and an output layer. The LSTM layer is the core of the network and consists of multiple LSTM units. Each LSTM unit contains a memory unit and three gates (input gate, forget gate, and output gate) to control the flow of information, retain important information, and ignore irrelevant information.

[0112] It should be noted that when training CNN and LSTM models, the backpropagation algorithm is usually combined with the gradient descent method to optimize the model parameters. For regression tasks (such as de-icing jump height prediction), the commonly used loss function is the mean square error (MSE); for classification tasks, the cross-entropy loss function is usually used. The loss function measures the gap between the model prediction and the true value and guides the learning direction of the model. In order to prevent the model from overfitting on the training data, regularization techniques such as L2 regularization and Dropout (randomly discarding some neurons during training) are usually used. Through these methods, the model can better generalize to unseen data. During the training process, by monitoring the change curves of the training loss and the validation loss, the learning progress of the model can be judged, and the learning rate can be adjusted or the training can be stopped early to avoid overfitting or insufficient training.

[0113] In addition, after completing the training of the preset prediction model, the preset prediction model needs to be strictly verified and tested. Cross-validation or leave-one-out is usually used to evaluate the generalization ability of the model. In the testing phase, an independent data set that was not involved in the training is used to evaluate the prediction accuracy and robustness of the model. Important evaluation indicators include mean square error (MSE), absolute error (MAE), etc.

[0114] In the actual application of the model, by collecting new data and feedback, the preset prediction model is continuously optimized and iterated. The use of transfer learning technology can quickly adapt to new environmental conditions or regions and improve the applicability of the model. In addition, the continuous optimization of the model also includes adjusting the model architecture, increasing the amount of data samples, introducing more complex feature engineering, etc., to further improve the prediction accuracy and stability of the model.

[0115] The training method of the preset prediction model provided in the embodiment of the present application provides a data model basis for the subsequent prediction of the jump height of the transmission line based on the preset prediction model.

[0116] In one embodiment, Figure 2 Based on the embodiment shown, the process of obtaining sample monitoring data can be described as follows: Figure 7 As shown, the above S203 "obtaining sample monitoring data" includes:

[0117] S601. Obtain sample meteorological data, sample icing data, and sample transmission line vibration data of the transmission line.

[0118] In an embodiment of the present application, when it is necessary to train a preset prediction model, sample meteorological data of the transmission line can be first obtained from the meteorological agency corresponding to the transmission line, and sample icing data and sample vibration data on the transmission line can be obtained by installing sensors on the transmission line.

[0119] S602: Input the sample meteorological data, the sample ice cover data and the sample vibration data into a preset power transmission line model for calculation to obtain the corresponding jump height of the power transmission line.

[0120] Among them, the preset transmission line model is determined according to the relationship between meteorological data, icing data, transmission line vibration data and transmission line jump height.

[0121] In an embodiment of the present application, after the sample meteorological data, sample ice cover data and sample vibration data are obtained as mentioned above, the sample meteorological data, sample ice cover data and sample vibration data can be input into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line.

[0122] Optionally, accurate physical models of conductors and transmission towers can be established through numerical simulation software to simulate icing under various meteorological conditions, simulate the icing and deicing process of conductors under different environmental conditions, and calculate the vibration response and jump height of the conductors.

[0123] The method for obtaining sample monitoring data provided by the embodiment of the present application increases the diversity of training data and improves the generalization ability and accuracy of the model. In the process of creating the training set, the numerical simulation results and the measured data are cross-validated to ensure the credibility of the simulation data.

[0124] In one embodiment, Figure 8 As shown, a method for predicting the jump height of a transmission line is also provided, including:

[0125] S10, obtaining monitoring data of the transmission line with ice;

[0126] S11, performing data cleaning processing on the monitoring data to obtain the cleaned monitoring data;

[0127] S12, performing data completion processing on the monitoring data after the cleaning processing to obtain the monitoring data after the completion processing;

[0128] S13, performing data normalization processing on the monitoring data after the completion processing to obtain normalized monitoring data;

[0129] S14, extracting features from the preprocessed monitoring data to obtain feature vectors of the monitoring data;

[0130] S15. Input the characteristic vector of the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0131] The above method inputs the monitoring data of the transmission line into a preset prediction model through model prediction to predict the jump height of the transmission line. Compared with the existing method of determining the jump height of the transmission line based on empirical formulas and experiments, the above method improves the accuracy of the jump height of the transmission line to a certain extent.

[0132] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0133] Based on the same inventive concept, the embodiment of the present application also provides a transmission line jump height prediction device for implementing the above-mentioned transmission line jump height prediction method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more transmission line jump height prediction device embodiments provided below can refer to the limitations of the transmission line jump height prediction method above, and will not be repeated here.

[0134] In an exemplary embodiment, Fig. 9 As shown, a device for predicting the jump height of a transmission line is provided, comprising: an acquisition module 10 and a prediction module 11, wherein:

[0135] The acquisition module 10 is used to acquire monitoring data of the transmission line with ice; the monitoring data includes at least one of meteorological data, ice data, and vibration data.

[0136] The prediction module 11 is used to input the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0137] In an exemplary embodiment, the above prediction model includes: a preprocessing unit and a prediction unit, wherein:

[0138] A preprocessing unit, specifically used to preprocess the monitoring data to obtain preprocessed monitoring data;

[0139] The prediction unit is specifically used to input the preprocessed monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0140] In an exemplary embodiment, the above-mentioned prediction unit is specifically used to extract features from the preprocessed monitoring data to obtain a feature vector of the monitoring data; the feature vector of the monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line.

[0141] In an exemplary embodiment, the above-mentioned preprocessing includes data cleaning processing, data completion processing, and data normalization processing; the above-mentioned preprocessing unit is specifically used to perform data cleaning processing on the monitoring data to obtain the monitoring data after cleaning processing; perform data completion processing on the monitoring data after cleaning processing to obtain the monitoring data after completion processing; perform data normalization processing on the monitoring data after completion processing to obtain the monitoring data after normalization processing.

[0142] In an exemplary embodiment, the above device further includes: an acquisition module and a training module, wherein:

[0143] An acquisition module is used to acquire sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line;

[0144] The training module is used to train the initial prediction model according to the sample monitoring data to obtain the preset prediction model.

[0145] In an exemplary embodiment, the acquisition module includes: an acquisition unit and a calculation unit, wherein:

[0146] An acquisition unit, specifically used to acquire sample meteorological data, sample icing data, and sample vibration data of the transmission line;

[0147] The calculation unit is specifically used to input the sample meteorological data, the sample icing data and the sample vibration data into the preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined according to the relationship between the meteorological data, the icing data, the transmission line vibration data and the transmission line jump height.

[0148] Each module in the above-mentioned transmission line jump height prediction device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0149] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store monitoring data of the transmission line. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the jump height of a transmission line is implemented.

[0150] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0151] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0152] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0153] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0154] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0155] Preprocessing the monitoring data to obtain preprocessed monitoring data;

[0156] The preprocessed monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0157] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0158] Perform feature extraction on the preprocessed monitoring data to obtain a feature vector of the monitoring data;

[0159] The characteristic vector of the monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0160] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0161] Performing data cleaning on the monitoring data to obtain the cleaned monitoring data;

[0162] Performing data completion processing on the cleaned monitoring data to obtain completed monitoring data;

[0163] The completed monitoring data is subjected to data normalization processing to obtain normalized monitoring data.

[0164] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0165] Obtaining sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line;

[0166] The initial prediction model is trained according to the sample monitoring data to obtain the preset prediction model.

[0167] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0168] Obtain sample meteorological data, sample icing data, and sample vibration data of the transmission line;

[0169] The sample meteorological data, sample icing data and sample vibration data are input into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined based on the relationship between the meteorological data, icing data, transmission line vibration data and the transmission line jump height.

[0170] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0171] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0172] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0173] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0174] Preprocessing the monitoring data to obtain preprocessed monitoring data;

[0175] The preprocessed monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0176] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0177] Perform feature extraction on the preprocessed monitoring data to obtain a feature vector of the monitoring data;

[0178] The characteristic vector of the monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0179] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0180] Performing data cleaning on the monitoring data to obtain the cleaned monitoring data;

[0181] Performing data completion processing on the cleaned monitoring data to obtain completed monitoring data;

[0182] The completed monitoring data is subjected to data normalization processing to obtain normalized monitoring data.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] Obtaining sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line;

[0185] The initial prediction model is trained according to the sample monitoring data to obtain the preset prediction model.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] Obtain sample meteorological data, sample icing data, and sample vibration data of the transmission line;

[0188] The sample meteorological data, sample icing data and sample vibration data are input into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined based on the relationship between the meteorological data, icing data, transmission line vibration data and the transmission line jump height.

[0189] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0190] Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data;

[0191] The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

[0192] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0193] Preprocessing the monitoring data to obtain preprocessed monitoring data;

[0194] The preprocessed monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0195] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0196] Perform feature extraction on the preprocessed monitoring data to obtain a feature vector of the monitoring data;

[0197] The characteristic vector of the monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] Performing data cleaning on the monitoring data to obtain the cleaned monitoring data;

[0200] Performing data completion processing on the cleaned monitoring data to obtain completed monitoring data;

[0201] The completed monitoring data is subjected to data normalization processing to obtain normalized monitoring data.

[0202] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0203] Obtaining sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line;

[0204] The initial prediction model is trained according to the sample monitoring data to obtain the preset prediction model.

[0205] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0206] Obtain sample meteorological data, sample icing data, and sample vibration data of the transmission line;

[0207] The sample meteorological data, sample icing data and sample vibration data are input into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined based on the relationship between the meteorological data, icing data, transmission line vibration data and the transmission line jump height.

[0208] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0209] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0210] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0211] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting the jump height of a transmission line, characterized in that: The method comprises: Acquiring monitoring data of an ice-covered power transmission line; the monitoring data comprising at least one of meteorological data, ice coverage data, and vibration data; The monitoring data is input into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

2. The method according to claim 1, characterized in that The step of inputting the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line includes: Preprocessing the monitoring data to obtain the preprocessed monitoring data; The preprocessed monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

3. The method according to claim 2, characterized in that The step of inputting the pre-processed monitoring data into the preset prediction model for prediction to obtain the jump height of the transmission line includes: Performing feature extraction on the preprocessed monitoring data to obtain a feature vector of the monitoring data; The characteristic vector of the monitoring data is input into the preset prediction model for prediction to obtain the jump height of the transmission line.

4. The method according to claim 2, characterized in that: The preprocessing includes data cleaning, data completion, and data normalization; the preprocessing of the monitoring data to obtain the preprocessed monitoring data includes: Performing the data cleaning process on the monitoring data to obtain the cleaned monitoring data; Performing the data completion process on the monitoring data after the cleaning process to obtain the monitoring data after the completion process; The data normalization process is performed on the monitoring data after the completion process to obtain the normalized monitoring data.

5. The method according to claim 1, characterized in that The method further comprises: Acquire the sample monitoring data; the sample monitoring data includes sample meteorological data, sample icing data, sample vibration data of the transmission line, and the corresponding jump height of the transmission line; The initial prediction model is trained according to the sample monitoring data to obtain the preset prediction model.

6. The method according to claim 5, characterized in that The obtaining of sample monitoring data includes: Obtaining sample meteorological data, sample icing data, and sample vibration data of the transmission line; The sample meteorological data, the sample icing data and the sample vibration data are input into a preset transmission line model for calculation to obtain the corresponding jump height of the transmission line; the preset transmission line model is determined according to the relationship between the meteorological data, the icing data, the transmission line vibration data and the transmission line jump height.

7. A device for predicting jump height of a transmission line, characterized in that: The device comprises: An acquisition module, used to acquire monitoring data of an ice-covered power transmission line; the monitoring data includes at least one of meteorological data, ice coverage data, and vibration data; A prediction module is used to input the monitoring data into a preset prediction model for prediction to obtain the jump height of the transmission line; the preset prediction model is trained based on sample monitoring data and an initial prediction model; the initial prediction model includes a convolutional neural network and a long short-term memory network.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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