Method, device, equipment and medium for predicting ice thickness of overhead transmission lines

Abstract

The present invention relates to the field of power grid safety technology, and discloses a method, device, equipment, and medium for predicting ice thickness of overhead transmission lines. The method comprises: obtaining historical data and future meteorological data of the overhead line to be predicted; preprocessing and feature engineering the historical data to obtain optimized historical data; dividing the optimized historical data into a training set and a test set, training multiple weak learner candidate models through the training set, calculating the score of each weak learner candidate model on the validation set, and determining a strong learner model; dividing the optimized historical data and future meteorological data into a total validation set and a total test set, training a meta-learner model through each strong learner model and the total validation set, inputting the prediction results of each strong learner model on the total test set into the meta-learner model, and obtaining the final ice thickness prediction value of the total test set. The present application predicts the future ice thickness through multi-model fusion, thereby improving the accuracy of the prediction of ice thickness of overhead transmission lines.

Description

Technical Field

[0001] The present invention relates to the field of power grid safety technology, and in particular to a method, device, equipment and medium for predicting ice thickness of overhead transmission lines. Background Art

[0002] Overhead line icing prediction is a key technical tool for disaster prevention and mitigation in power systems. It aims to scientifically predict the potential for icing on transmission lines under conditions such as low temperatures and high humidity, enabling proactive protective measures to ensure safe and stable grid operation. Icing can increase the mechanical load on conductors, increase sag, and even lead to serious accidents such as line breakage and tower collapse. Therefore, icing prediction is crucial to the reliability and economic efficiency of power systems.

[0003] Most of the existing methods for predicting icing on overhead lines use models with poor prediction and generalization capabilities, which restricts the accuracy of ice thickness prediction in actual production environments and cannot effectively solve the prediction problems under conditions such as nonlinear historical training data and large random fluctuations. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, equipment and medium for predicting ice thickness of overhead transmission lines.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for predicting ice thickness of overhead transmission lines, the method comprising:

[0007] Acquiring historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data, and historical ice thickness data;

[0008] Preprocessing the historical data to obtain preprocessed historical data, and performing feature engineering on the preprocessed historical data to obtain optimized historical data;

[0009] Dividing the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, training multiple weak learner candidate models using the training set, calculating a score of each of the weak learner candidate models on the validation set, and determining multiple strong learner models based on the score of each of the weak learner candidate models;

[0010] The optimized historical data and the future meteorological data are merged into a data set, and the data set is divided into a total validation set and a total test set. A meta-learner model is trained using each of the strong learner models and the total validation set, and the prediction results of each of the strong learner models on the total test set are input into the meta-learner model to obtain the final ice thickness prediction value of the total test set.

[0011] In an optional embodiment, preprocessing the historical data to obtain preprocessed historical data includes:

[0012] Sampling and aligning the historical data at preset time intervals to obtain aligned historical data;

[0013] Determining missing time period data in the aligned historical data, and performing mean filling on the missing time period data to obtain filled historical data;

[0014] Determining abnormal data in the filled historical data, and removing the abnormal data to obtain historical data after removal;

[0015] Determine the missing historical meteorological data in the eliminated historical data, and perform Nan value filling on the missing historical meteorological data to obtain the preprocessed historical data.

[0016] In an optional embodiment, performing feature engineering on the preprocessed historical data to obtain optimized historical data includes:

[0017] Normalizing the historical meteorological data and the historical ice thickness data to obtain normalized historical data;

[0018] Performing one-hot encoding on the line data and the device data to obtain one-hot encoded historical data;

[0019] Principal component analysis is performed on the one-hot encoded historical data, noise field columns in the one-hot encoded historical data are deleted, and data dimensionality reduction is performed on the one-hot encoded historical data to obtain the optimized historical data.

[0020] In an optional embodiment, the optimization historical data is divided into a training set and a test set, and the training set contains multiple validation sets, including:

[0021] Dividing the optimized historical data into the training set and the test set according to a preset ratio;

[0022] The training set is divided into multiple groups of first training sets by performing K-fold cross-division using the Bagging algorithm;

[0023] Each of the first training sets is divided into a validation set and multiple second training sets.

[0024] In an optional embodiment, the step of calculating a score of each of the weak learner candidate models on the validation set and determining a plurality of strong learner models according to the score of each of the weak learner candidate models comprises:

[0025] Predicting the validation set using each of the weak learner candidate models to obtain a predicted ice thickness value of each of the weak learner candidate models on the validation set;

[0026] Obtaining the actual value of ice thickness in the validation set, and obtaining the number of ice-covered lines and the number of time series segments in the validation set;

[0027] Calculating the score of each of the weak learner candidate models using a preset model score calculation formula based on the ice thickness prediction value, ice thickness prediction value, number of ice-covered routes, and number of time series segments of the validation set;

[0028] The scores of the weak learner candidate models are arranged in ascending order, and a preset number of weak learner candidate models are determined as the multiple strong learner models.

[0029] In an optional embodiment, the preset model score calculation formula is:

[0030]

[0031] Where, MAPE is the score of the weak learner candidate model, R is the number of ice-covered lines in the validation set, T is the number of time series segments in the validation set, and d rt is the actual value of ice thickness of the rth line in the tth time period in the verification set, p rt is the predicted value of ice thickness for the rth line in the validation set during time period t.

[0032] In an optional embodiment, the training of the meta-learner model using each of the strong learner models and the total validation set includes:

[0033] Predicting the total validation set by each of the strong learner models, and obtaining a predicted value of ice thickness of each of the strong learner models on the total validation set;

[0034] The actual value of ice thickness of the total validation set is obtained, the predicted value of ice thickness of the total validation set is used as a feature column, and the actual value of ice thickness of the total validation set is used as a target column to form a total training set, and the meta-learner model is trained by the total training set.

[0035] In a second aspect, the present invention provides a device for predicting ice thickness on overhead transmission lines, the device comprising:

[0036] An acquisition module is used to acquire historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data and historical ice thickness data;

[0037] a processing module, configured to preprocess the historical data to obtain preprocessed historical data, and perform feature engineering on the preprocessed historical data to obtain optimized historical data;

[0038] a training module, configured to divide the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, train multiple weak learner candidate models using the training set, calculate a score for each of the weak learner candidate models on the validation set, and determine multiple strong learner models based on the scores of each of the weak learner candidate models;

[0039] A prediction module is used to merge the optimized historical data and the future meteorological data into a data set, and divide the data set into a total validation set and a total test set, train a meta-learner model through each of the strong learner models and the total validation set, and input the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a final ice thickness prediction value of the total test set.

[0040] In a third aspect, an embodiment of the present disclosure provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for predicting ice thickness of overhead transmission lines described in the first aspect are implemented.

[0041] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting ice thickness of overhead transmission lines described in the first aspect are implemented.

[0042] Beneficial effects of this application:

[0043] The embodiment of the present application provides a method for predicting ice thickness of overhead transmission lines, which obtains historical data and future meteorological data of the overhead lines to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data and historical ice thickness data; preprocesses the historical data to obtain preprocessed historical data, and performs feature engineering on the preprocessed historical data to obtain optimized historical data; divides the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, trains multiple weak learner candidate models through the training set, calculates the score of each of the weak learner candidate models on the validation set, and determines multiple strong learner models based on the score of each of the weak learner candidate models; merges the optimized historical data and the future meteorological data into a data set, and divides the data set into a total validation set and a total test set, trains a meta-learner model through each of the strong learner models and the total validation set, and inputs the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a final ice thickness prediction value of the total test set. This application uses multi-model fusion to predict the future short-term ice thickness time series. By fusing and integrating multiple weak learners, the learning and generalization capabilities of ice thickness data are enhanced. At the same time, short-term meteorological data of the future prediction time period are introduced. By comprehensively considering the historical and future covariate factors that affect the evolution of ice thickness, the system's ability to capture the temporal characteristics of ice evolution is improved, and the accuracy of ice thickness prediction for overhead transmission lines is improved.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be considered as limiting the scope. A person of ordinary skill in the art can also derive other relevant drawings based on these drawings without inventive effort. Similar components are numbered similarly in the various drawings.

[0046] Figure 1 A flowchart of a method for predicting ice thickness on overhead transmission lines provided in an embodiment of the present application is shown;

[0047] Figure 2 A flowchart of another method for predicting ice thickness of overhead transmission lines provided in an embodiment of the present application is shown;

[0048] Figure 3A flowchart of another method for predicting ice thickness of overhead transmission lines provided in an embodiment of the present application is shown;

[0049] Figure 4 A curve comparison diagram of the absolute error of predictions of different prediction strategies provided in an embodiment of the present application is shown;

[0050] Figure 5 A schematic structural diagram of an overhead transmission line ice thickness prediction device provided in an embodiment of the present application is shown;

[0051] Figure 6 A structural diagram of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0052] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0053] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in the template description herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0055] Example 1

[0056] like Figure 1 FIG. 1 is a flow chart of a method for predicting ice thickness of an overhead transmission line in an embodiment of the present application. The method for predicting ice thickness of an overhead transmission line provided in an embodiment of the present application includes the following steps:

[0057] Step S110 , obtaining historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data and historical ice thickness data.

[0058] In this embodiment, historical data and future meteorological data for the overhead lines to be predicted in the predicted area are first obtained. The historical data includes historical meteorological data (with fields such as "time," "rainfall," "air pressure," "humidity," "temperature," "wind direction," and "wind speed"), line data (with fields such as "line ID," "line code," "line name," "voltage level," and "city name"), equipment data (with fields such as "device code," "tower number," "tower code," "operation and maintenance unit," "phase," "manufacturer," "conductor radius," "conductor density," "design thickness," "reference value," and "tension value"), and historical ice thickness data sampled at the hourly level.

[0059] By acquiring historical data on the overhead lines to be predicted, as well as future meteorological data, this method ensures that the prediction model comprehensively considers multiple influencing factors, improving both accuracy and comprehensiveness of the prediction. This data provides a solid foundation for subsequent preprocessing, feature engineering, and model training, and is crucial for ensuring the performance of the prediction model.

[0060] Step S120 , preprocessing the historical data to obtain preprocessed historical data, and performing feature engineering on the preprocessed historical data to obtain optimized historical data.

[0061] It is understandable that after obtaining the historical data of the overhead line to be predicted, in order to ensure the data quality and make it more suitable for subsequent model training, the historical data needs to be preprocessed. The specific process is as follows:

[0062] (1) Since data may come from different sensors or data sources, the sampling frequency may be inconsistent. In order to unify the data format, it is necessary to sample and align the historical data at a preset time interval (for example, every hour) to obtain the aligned historical data;

[0063] (2) After sampling alignment, for the missing time period data, the mean filling method is used to obtain the filled historical data. This means that for a missing time period, the average value of the data of the adjacent hours before and after it can be calculated and used to fill the missing data. If there is no data before and after the missing hour (for example, the beginning or end of the data set), it may be necessary to consider using the mean of the entire data set or other appropriate filling strategies;

[0064] (3) Abnormal data may be caused by sensor failure, data entry errors, etc. For ice thickness data, abnormal data may have a negative impact on model training. Therefore, it is necessary to remove these abnormal data to obtain the eliminated historical data. Methods for removing abnormal data may include: using statistical methods (such as the 3σ principle) to identify and remove outliers, using visualization tools such as box plots to identify outliers, setting reasonable thresholds based on domain knowledge, and removing outliers that exceed the thresholds.

[0065] (4) For the missing historical meteorological data in the eliminated historical data, the Nan value filling method can be used to obtain the preprocessed historical data. This means that for a missing historical meteorological data point, it is marked as NaN (Not a Number) and processed in the subsequent feature engineering or model training process.

[0066] Furthermore, in order to convert the original preprocessed historical data into features that can be understood by the subsequent model, feature engineering is required for the preprocessed historical data. The specific process is as follows:

[0067] (1) For historical meteorological data and historical ice thickness data, since their value ranges may vary greatly (for example, the value ranges of temperature and ice thickness may be completely different), they need to be normalized to convert data of different dimensions to the same scale to obtain normalized historical data. Normalization methods can include minimum-maximum normalization and Z-score normalization;

[0068] (2) For line data and equipment data, since they are categorical variables and their values ​​are discrete, they need to be one-hot encoded to convert the categorical variables into numerical variables and obtain the one-hot encoded historical data. This means that for each categorical variable, a new binary feature column will be created. If a sample belongs to a certain category of the categorical variable, the corresponding feature column is 1, otherwise it is 0;

[0069] (3) Principal Component Analysis (PCA) is a commonly used data dimensionality reduction method. It reduces the dimensionality of data by calculating the principal components of the data (i.e., the main direction of change of the data). In the overhead line icing prediction, due to the large number of feature columns (including meteorological data, line data, and equipment data, etc.), some of these feature columns may be redundant or contain noise. Therefore, PCA is performed on the historical data after one-hot encoding to remove these redundant or noisy feature columns, and data dimensionality reduction is performed to obtain the final optimized historical data.

[0070] The above method effectively improves data quality and integrity through methods such as sampling alignment, mean padding, outlier removal, and Nan value filling, reducing prediction errors caused by missing or abnormal data. Normalization converts data of different dimensions to the same scale, facilitating model processing; one-hot encoding converts categorical variables to numerical variables, enhancing the model's expressiveness; and PCA dimensionality reduction removes redundant and noisy features, simplifying the model structure and improving computational efficiency.

[0071] Step S130: Divide the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, train multiple weak learner candidate models through the training set, calculate the score of each weak learner candidate model on the validation set, and determine multiple strong learner models based on the score of each weak learner candidate model.

[0072] Understandably, if Figure 2 As shown, first, the optimized historical data is divided into a training set and a test set according to a preset ratio (for example, 9:1), and the training set is divided into M groups (for example, M=5) of first training sets by using the Bagging algorithm. Each group of first training sets is then divided into a validation set and multiple second training sets. Then, all weak learner candidate models are trained using the M groups of first training sets. In this embodiment, the weak learner candidate models may include XGBoost, LightGBM, SVR, linear regression model, KNNR, Chronos, etc., which is not limited in this application.

[0073] Next, the weak learner candidate models obtained through all training are used to predict the validation set to obtain the ice thickness prediction value of each weak learner candidate model on the validation set. Then, based on the actual ice thickness value, the number of ice-covered lines, and the number of time series segments obtained in the validation set, the score of each weak learner candidate model is calculated using the preset model score calculation formula. In this application, the score of each weak learner candidate model is represented by the mean absolute percentage error (MAPE). The specific calculation formula is:

[0074]

[0075] Where, MAPE is the score of the weak learner candidate model, R is the number of ice-covered lines in the validation set, T is the number of time series segments in the validation set, and d rt To verify the actual value of ice thickness of the rth line in time period t, p rt is the predicted value of ice thickness for the rth line in time period t in the verification set.

[0076] Furthermore, the scores of the weak learner candidate models are arranged in ascending order. The smaller the score, the better the model effect. Therefore, a preset number (for example, 3) of weak learner candidate models are selected to determine multiple strong learner models.

[0077] By training multiple weak learner candidate models and calculating scores on a validation set, this method ensures model diversity and complementarity, providing a rich set of options for subsequent model fusion. Evaluating weak learners using metrics such as MAPE accurately reflects the model's predictive performance, providing a reliable basis for determining strong learner models.

[0078] Step S140: Merge the optimized historical data and the future meteorological data into a data set, and divide the data set into a total validation set and a total test set; train a meta-learner model using each of the strong learner models and the total validation set; and input the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a predicted value of ice thickness for the total test set.

[0079] Understandably, if Figure 3 As shown, the optimized historical data and future meteorological data are ultimately fused into a data set, which is then divided into a total validation set and a total test set. The multiple strong learner models obtained in step S130 are used to predict the total validation set to obtain predicted ice thickness values ​​for the total validation set. The predicted ice thickness values ​​for the total validation set are used as feature columns, and the actual ice thickness values ​​obtained for the total validation set are used as target columns. The target column and feature columns are reassembled into a total training set, which is then used to train a meta-model (typically a simple regression model, such as a linear regression model).

[0080] The task of the meta-model is to learn how to fuse the prediction results of the strong learner models and generate the final prediction results. The specific steps are as follows: predict the total test set through multiple strong learner models to obtain the ice thickness prediction value of each strong learner model on the total test set; input the ice thickness prediction value of each strong learner model on the total test set into the meta-learner model to obtain the final ice thickness prediction value of the total test set.

[0081] This method, combined with stacking technology, combines the predictions of multiple strong learner models to train a meta-model to generate the final prediction. The core concept is to leverage the diversity of different models to capture different patterns in the data, thereby improving overall performance and enhancing the accuracy and robustness of the predictions. By comprehensively considering historical and future influencing factors, as well as the prediction results of multiple models, the system can more comprehensively and accurately predict the ice thickness of overhead transmission lines.

[0082] In an optional embodiment, as Figure 4 As shown, this is a comparison chart of the prediction absolute error curves under three different prediction strategies (including: ① strong learner model, ② strong learner model + Chronos, ③ strong learner model + Chronos + future meteorological data) on a certain line. It can be seen that the strong learner model + Chronos (weak learner) + future meteorological data is the prediction strategy adopted by this application, and the error value is the smallest. It can be seen that this application further improves the accuracy of the prediction system in predicting the ice thickness of overhead transmission lines.

[0083] The embodiment of the present application provides a method for predicting ice thickness of overhead transmission lines, which obtains historical data and future meteorological data of the overhead lines to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data and historical ice thickness data; preprocesses the historical data to obtain preprocessed historical data, and performs feature engineering on the preprocessed historical data to obtain optimized historical data; divides the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, trains multiple weak learner candidate models through the training set, calculates the score of each of the weak learner candidate models on the validation set, and determines multiple strong learner models based on the score of each of the weak learner candidate models; merges the optimized historical data and the future meteorological data into a data set, and divides the data set into a total validation set and a total test set, trains a meta-learner model through each of the strong learner models and the total validation set, and inputs the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a final ice thickness prediction value of the total test set. This application uses multi-model fusion to predict the future short-term ice thickness time series. By fusing and integrating multiple weak learners, the learning and generalization capabilities of ice thickness data are enhanced. At the same time, short-term meteorological data of the future prediction time period are introduced. By comprehensively considering the historical and future covariate factors that affect the evolution of ice thickness, the system's ability to capture the temporal characteristics of ice evolution is improved, and the accuracy of ice thickness prediction for overhead transmission lines is improved.

[0084] Example 2

[0085] like Figure 5 FIG. 5 is a schematic diagram of a device 500 for predicting ice thickness on an overhead transmission line according to an embodiment of the present application, wherein the device comprises:

[0086] An acquisition module 510 is configured to acquire historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data, and historical ice thickness data;

[0087] The processing module 520 is configured to preprocess the historical data to obtain preprocessed historical data, and perform feature engineering on the preprocessed historical data to obtain optimized historical data.

[0088] a training module 530 configured to divide the optimization history data into a training set and a test set, wherein the training set includes multiple validation sets, train multiple weak learner candidate models using the training set, calculate a score for each of the weak learner candidate models on the validation set, and determine multiple strong learner models based on the scores of each of the weak learner candidate models;

[0089] The prediction module 540 is used to merge the optimized historical data and the future meteorological data into a data set, and divide the data set into a total validation set and a total test set, train a meta-learner model using each of the strong learner models and the total validation set, and input the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain the final ice thickness prediction value of the total test set.

[0090] The device for predicting ice thickness of overhead transmission lines provided in the embodiment of the present application can implement each process of the method for predicting ice thickness of overhead transmission lines corresponding to Example 1, and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0091] The overhead transmission line ice thickness prediction device provided in the embodiment of the present application predicts the future short-term ice thickness time series through multi-model fusion. By fusing and integrating multiple weak learners, the learning and generalization capabilities of ice thickness data are enhanced. At the same time, short-term meteorological data of the future prediction time period is introduced. By comprehensively considering the historical and future covariate factors that affect the evolution of ice thickness, the system's ability to capture the temporal characteristics of ice evolution is improved, thereby improving the accuracy of ice thickness prediction for overhead transmission lines.

[0092] Example 3

[0093] The present application also provides a computer device. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0094] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 6 with a memory 61, a processor 62, and a network interface 63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0095] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0096] The memory 61 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or D slot compatibility test memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as the hard disk or memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 6. Of course, the memory 61 may also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions for the slot compatibility test method. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0097] In some embodiments, the processor 62 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other overhead transmission line ice thickness prediction chip. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to execute computer-readable instructions or process data stored in the memory 61, such as computer-readable instructions for executing the slot compatibility testing method.

[0098] The network interface 63 may include a wireless network interface or a wired network interface. The network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0099] The computer device provided in this embodiment can execute the above-mentioned method for predicting the thickness of ice covering an overhead transmission line. Here, the method for predicting the thickness of ice covering an overhead transmission line can be the method for predicting the thickness of ice covering an overhead transmission line in each of the above-mentioned embodiments.

[0100] Example 4

[0101] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for predicting ice thickness of overhead transmission lines in the embodiment are implemented.

[0102] In this embodiment, the computer-readable storage medium includes flash memory, hard disks, multimedia cards, card-type memories (e.g., SD or DX memories), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disks, optical disks, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped with the computer device, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Of course, the computer-readable storage medium may also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are about to be output.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or flowchart, and the combination of boxes in the structure diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0104] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0105] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and other media that can store program codes.

[0106] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for predicting ice thickness of overhead transmission lines, characterized in that: The method comprises: Acquiring historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data, and historical ice thickness data; Preprocessing the historical data to obtain preprocessed historical data, and performing feature engineering on the preprocessed historical data to obtain optimized historical data; Dividing the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, training multiple weak learner candidate models using the training set, calculating a score of each of the weak learner candidate models on the validation set, and determining multiple strong learner models based on the score of each of the weak learner candidate models; Merging the optimized historical data and the future meteorological data into a data set, and dividing the data set into a total validation set and a total test set, training a meta-learner model using each of the strong learner models and the total validation set, and inputting the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a final ice thickness prediction value of the total test set; The step of calculating the score of each weak learner candidate model on the validation set and determining a plurality of strong learner models according to the score of each weak learner candidate model comprises: Predicting the validation set using each of the weak learner candidate models to obtain a predicted ice thickness value of each of the weak learner candidate models on the validation set; Obtaining the actual value of ice thickness in the validation set, and obtaining the number of ice-covered lines and the number of time series segments in the validation set; Calculating the score of each of the weak learner candidate models using a preset model score calculation formula based on the ice thickness prediction value, ice thickness prediction value, number of ice-covered routes, and number of time series segments of the validation set; Arrange the scores of the weak learner candidate models in ascending order, and determine the first preset number of weak learner candidate models as the multiple strong learner models; The preset model score calculation formula is: Where, is the score of the weak learner candidate model, is the number of ice-covered lines in the validation set data, is the number of time series segments of the data in the validation set, The validation set line The actual value of ice thickness during the time period, The validation set line Predicted ice thickness for the time period; The step of training the meta-learner model using the strong learner models and the total validation set includes: Predicting the total validation set by each of the strong learner models, and obtaining a predicted value of ice thickness of each of the strong learner models on the total validation set; The actual value of ice thickness of the total validation set is obtained, the predicted value of ice thickness of the total validation set is used as a feature column, and the actual value of ice thickness of the total validation set is used as a target column to form a total training set, and the meta-learner model is trained by the total training set.

2. The method for predicting ice thickness of overhead transmission lines according to claim 1, characterized in that: The preprocessing of the historical data to obtain the preprocessed historical data includes: Sampling and aligning the historical data at preset time intervals to obtain aligned historical data; Determining missing time period data in the aligned historical data, and performing mean filling on the missing time period data to obtain filled historical data; Determining abnormal data in the filled historical data, and removing the abnormal data to obtain historical data after removal; Determine the missing historical meteorological data in the eliminated historical data, and perform Nan value filling on the missing historical meteorological data to obtain the preprocessed historical data.

3. The method for predicting ice thickness of overhead transmission lines according to claim 1, characterized in that: The performing feature engineering on the preprocessed historical data to obtain optimized historical data includes: Normalizing the historical meteorological data and the historical ice thickness data to obtain normalized historical data; Performing one-hot encoding on the line data and the device data to obtain one-hot encoded historical data; Principal component analysis is performed on the one-hot encoded historical data, noise field columns in the one-hot encoded historical data are deleted, and data dimensionality reduction is performed on the one-hot encoded historical data to obtain the optimized historical data.

4. The method for predicting ice thickness of overhead transmission lines according to claim 1, wherein: The optimization historical data is divided into a training set and a test set, wherein the training set contains multiple validation sets, including: Dividing the optimized historical data into the training set and the test set according to a preset ratio; The training set is divided into multiple groups of first training sets by performing K-fold cross-division using the Bagging algorithm; Each of the first training sets is divided into a validation set and multiple second training sets.

5. A device for predicting ice thickness of overhead transmission lines, characterized in that: The device comprises: An acquisition module is used to acquire historical data and future meteorological data of the overhead line to be predicted, wherein the historical data includes historical meteorological data, line data, equipment data and historical ice thickness data; a processing module, configured to preprocess the historical data to obtain preprocessed historical data, and perform feature engineering on the preprocessed historical data to obtain optimized historical data; a training module, configured to divide the optimized historical data into a training set and a test set, wherein the training set includes multiple validation sets, train multiple weak learner candidate models using the training set, calculate a score for each of the weak learner candidate models on the validation set, and determine multiple strong learner models based on the scores of each of the weak learner candidate models; a prediction module, configured to combine the optimized historical data and the future meteorological data into a data set, divide the data set into a total validation set and a total test set, train a meta-learner model using each of the strong learner models and the total validation set, and input the prediction results of each of the strong learner models on the total test set into the meta-learner model to obtain a final ice thickness prediction value for the total test set; The step of calculating the score of each weak learner candidate model on the validation set and determining a plurality of strong learner models according to the score of each weak learner candidate model comprises: Predicting the validation set using each of the weak learner candidate models to obtain a predicted ice thickness value of each of the weak learner candidate models on the validation set; Obtaining the actual value of ice thickness in the validation set, and obtaining the number of ice-covered lines and the number of time series segments in the validation set; Calculating the score of each of the weak learner candidate models using a preset model score calculation formula based on the ice thickness prediction value, ice thickness prediction value, number of ice-covered routes, and number of time series segments of the validation set; Arrange the scores of the weak learner candidate models in ascending order, and determine the first preset number of weak learner candidate models as the multiple strong learner models; The preset model score calculation formula is: Where, is the score of the weak learner candidate model, is the number of ice-covered lines in the validation set data, is the number of time series segments of the data in the validation set, The validation set line The actual value of ice thickness during the time period, The validation set line Predicted ice thickness for the time period; The step of training the meta-learner model using the strong learner models and the total validation set includes: Predicting the total validation set by each of the strong learner models, and obtaining a predicted value of ice thickness of each of the strong learner models on the total validation set; The actual value of ice thickness of the total validation set is obtained, the predicted value of ice thickness of the total validation set is used as a feature column, and the actual value of ice thickness of the total validation set is used as a target column to form a total training set, and the meta-learner model is trained by the total training set.

6. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for predicting ice thickness of overhead transmission lines according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting ice thickness of overhead transmission lines according to any one of claims 1 to 4 are implemented.

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