Icing strength real-time monitoring and predicting method and system based on AI

Abstract

The invention provides an AI-based real-time monitoring and predicting method and system for icing strength, an icing environment parameter representation array is an implicit representation of related parameters for determining the icing strength of a monitored area in a monitored period, and an icing strength parameter representation array is an implicit representation of the icing strength of the monitored area in the monitored period. If the distribution coding array represents the position of the array element in the monitored icing correlation representation array set, the icing intensity of the prediction period is inferred by combining an integrated icing correlation representation array set formed by the icing intensity parameter representation array, the icing environment parameter representation array and the distribution coding array; and the reasoning precision of the icing strength can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an AI-based real-time monitoring and prediction method and system for icing intensity. Background Art

[0002] In the field of power system operation and maintenance, especially in key infrastructure such as high-voltage transmission lines and wind power generation facilities, icing is a factor that seriously threatens the safe and stable operation of the system. Icing may not only lead to a decrease in power transmission efficiency, but also cause accidents such as line breakage and tower collapse, causing huge economic losses and social impacts. Therefore, accurately predicting the intensity of icing has become an important task in the power industry. Traditional icing intensity monitoring methods mainly rely on manual inspections or simple sensor equipment. These methods have problems such as limited monitoring range, slow response speed, and weak data processing capabilities. With the development of Internet of Things technology, more and more smart sensors are being used in power grid monitoring, which has led to a sharp increase in the amount of data collected. However, how to effectively use these big data resources for efficient and accurate icing intensity prediction is still a technical challenge that needs to be solved. Summary of the invention

[0003] In view of this, the embodiment of the present application at least provides a method and system for real-time monitoring and prediction of icing intensity based on AI. The technical solution of the present application is implemented as follows:

[0004] On the one hand, an embodiment of the present application provides an AI-based real-time monitoring and prediction method for icing intensity, the method comprising: splicing an icing intensity parameter characterization array and an icing environment parameter characterization array of a monitoring area in the same monitored period to obtain a monitored icing associated characterization array of the monitored period, and obtaining a monitored icing associated characterization array set based on monitored icing associated characterization arrays of multiple different monitored periods; the icing environment parameter characterization array is an implicit representation of relevant parameters that determine the icing intensity of the monitoring area in the monitored period; the icing intensity parameter characterization array is an implicit representation of the icing intensity of the monitoring area in the monitored period. The invention is expressed in the formula; according to the distribution of each array element in the monitored icing association representation array set in the monitored icing association representation array set, the distribution coding array corresponding to each array element is determined; each array element includes an icing intensity parameter representation array and an icing environment parameter representation array under the multiple different monitored periods; each array element in the monitored icing association representation array set is respectively integrated with the corresponding distribution coding array to obtain an integrated icing association representation array set; and reasoning is performed based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods.

[0005] In a second aspect, the present application provides a computer system, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the steps in the above-described method when executing the program.

[0006] Beneficial effects of the present application: The AI-based real-time monitoring and prediction method and system for icing intensity provided in the embodiments of the present application splices the icing intensity parameter characterization array and the icing environment parameter characterization array of the monitoring area under the same monitored period to obtain a monitored icing associated characterization array of the monitored period, obtains a monitored icing associated characterization array set according to the monitored icing associated characterization arrays of multiple different monitored periods, determines the distribution coding arrays corresponding to each array element according to the distribution of each array element in the monitored icing associated characterization array set in the monitored icing associated characterization array set, and the distribution coding array represents the position information of the array element in the monitored icing associated characterization array set, each array element includes the icing intensity parameter characterization array and the icing environment parameter characterization array under multiple different monitored periods, integrates each array element in the monitored icing associated characterization array set with the corresponding distribution coding array, obtains an integrated icing associated characterization array set, and performs reasoning based on the integrated icing associated characterization array set to obtain the icing intensity of the monitoring area in one or more prediction periods. Because the icing environment parameter representation array is an implicit representation of the relevant parameters that determine the icing intensity of the monitored area during the monitored period, the icing intensity parameter representation array is an implicit representation of the icing intensity of the monitored area during the monitored period, and the distribution coding array represents the position of the array elements in the monitored icing related representation array set, then the integrated icing related representation array set composed of the icing intensity parameter representation array, the icing environment parameter representation array and the distribution coding array can be used to infer the icing intensity of the prediction period, which can increase the reasoning accuracy of the icing intensity.

[0007] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0009] Figure 1 A schematic diagram of the implementation flow of an AI-based real-time monitoring and prediction method for icing intensity provided in an embodiment of the present application.

[0010] Figure 2 A hardware entity diagram of a computer system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application are further elaborated in detail below in conjunction with the drawings and embodiments. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0012] The embodiment of the present application provides a method for real-time monitoring and prediction of icing intensity based on AI, which can be executed by a processor of a computer system. The computer system can refer to a device with data processing capabilities such as a server, a laptop, a tablet computer, and a desktop computer.

[0013] Figure 1 A schematic diagram of the implementation process of an AI-based real-time monitoring and prediction method for icing intensity provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0014] Step S100: splice the icing intensity parameter characterization array and the icing environment parameter characterization array of the monitoring area in the same monitored period to obtain a monitored icing associated characterization array of the monitored period, and obtain a monitored icing associated characterization array set according to the monitored icing associated characterization arrays of multiple different monitored periods; the icing environment parameter characterization array is an implicit representation of the relevant parameters that determine the icing intensity of the monitoring area in the monitored period; the icing intensity parameter characterization array is an implicit representation of the icing intensity of the monitoring area in the monitored period.

[0015] In the embodiment of the present application, the task performed by the computer system in step S100 is to splice the icing intensity parameters and environmental parameter characterization arrays under a specific monitored period in the monitoring area to form a monitored icing associated characterization array. Subsequently, the system constructs a set of monitored icing associated characterization arrays based on the monitored icing associated characterization arrays of multiple different monitored periods. The icing environmental parameter characterization array and the icing intensity parameter characterization array are both implicit representations related to the icing intensity in the monitoring area during the monitoring period. The former covers various environmental factors that affect icing, and the latter directly reflects the actual observed icing intensity during the monitoring period.

[0016] Specifically, the computer system first collects various environmental parameters in the monitoring area during a certain monitoring period, such as temperature, humidity, wind speed, etc. These parameters are converted into digital form to form an array of ice environment parameter representations. For example, if the average temperature recorded in monitoring area A from August 1 to August 7, 2024 is zero degrees, the relative humidity is eighty percent, and the average wind speed is five meters per second, the system will create an array of environmental parameter representations, represented as [E_1, E_2,..., E_n], where E_1 represents the average temperature, E_2 represents the relative humidity, and E_n represents the average wind speed. Similarly, the system records the actual ice intensity of monitoring area A during the same monitoring period, and converts these data into digital representations to form an array of ice intensity parameter representations. If the average ice thickness during the monitoring period is 1 mm, the system creates an array of ice intensity parameter representations [I], where I represents the average ice thickness. Then, the computer system splices the above two arrays to form a complete array of monitored ice association representations. Taking the previous example, the system concatenates the environmental parameter representation array [E_1, E_2, ..., E_n] and the icing intensity parameter representation array [I] to form a monitored icing association representation array [C] = [E_1, E_2, ..., E_n, I]. Array C contains the information of environmental parameters and icing intensity parameters for subsequent processing.

[0017] Subsequently, the system repeats the above process to collect and process data from multiple different monitoring periods. For example, the system not only processes the data from August 1 to August 7, but also processes the data from August 8 to August 14, August 15 to August 21, and other monitoring periods, forming multiple monitored icing association representation arrays C1, C2, ..., Cm. These arrays together constitute the set of monitored icing association representation arrays.

[0018] In the process of building an array collection, the computer system further processes the data to make it more suitable for training machine learning models. For example, the system may standardize these arrays so that all values ​​are in the same range to reduce the impact of dimensional differences. After standardization, the system may use methods such as normalization to scale the values ​​to between 0 and 1. Assuming that the original value of an environmental parameter is X and the value after standardization is X', the standardization formula can be expressed as X'=(X-X_min) / (X_max-X_min), where X_min and X_max are the minimum and maximum values ​​of the parameter in the entire data set, respectively.

[0019] After completing the above steps, the computer system obtains a set of multiple arrays of monitored icing correlation representations, which provides the necessary input data for the subsequent steps. These data will be used to train the icing monitoring neural network, which will learn to predict icing intensity from environmental parameters and improve the accuracy of icing intensity prediction. For example, during the training process, the system uses the mean square error (MSE) as a loss function to measure the gap between the predicted value and the actual icing intensity. The loss function is defined as Where y_i represents the actual icing intensity, Represents the predicted icing intensity, and n represents the number of samples. By continuously adjusting the weights of the neural network, the loss function value is gradually reduced, and finally a model that can better predict the icing intensity is obtained.

[0020] In summary, step S100 lays the foundation for the entire prediction process. By collating and preprocessing historical monitoring data, the computer system provides high-quality input data for subsequent analysis and prediction.

[0021] Step S200: Determine the distribution coding arrays corresponding to each array element according to the distribution of each array element in the monitored icing association representation array set; each array element includes an icing intensity parameter representation array and an icing environment parameter representation array under multiple different monitored periods.

[0022] The task of the computer system in step S200 is to determine the distribution coding arrays corresponding to the array elements according to the distribution of the array elements in the monitored icing association characterization array set. This process aims to give each array element its position information in the time series so that subsequent processing can better utilize the temporal correlation.

[0023] Specifically, the computer system first loads a set of monitored icing association representation arrays, which consists of multiple monitored icing association representation arrays, and each array element includes an icing intensity parameter representation array and an icing environment parameter representation array. Taking monitoring area A as an example, assume that the system has obtained icing association representation arrays for three monitoring cycles: C1, C2, and C3, each of which contains environmental parameters (such as temperature, humidity, wind speed, etc.) and icing intensity parameters (such as icing thickness). For example, C1 may be represented as [T1, H1, F1, I1], where T1 represents the average temperature in the first monitoring cycle, H1 represents the average humidity, F1 represents the average wind speed, and I1 represents the average icing thickness in the cycle. The system then determines their distribution coding arrays for these array elements. The distribution coding array reflects the position of the array element in the time series. To illustrate this point, assume that C1, C2, and C3 represent the data of three consecutive monitoring cycles, then the position of C1 in the time series is 1, the position of C2 is 2, and the position of C3 is 3. The system generates a distribution encoding array for each array element, which encodes the time position information of the element.

[0024] One of the specific ways the system achieves this is through a pre-trained ice monitoring neural network that can understand patterns in time series data. For example, the system can use a long short-term memory network (LSTM), which is a recurrent neural network (RNN) designed specifically to process sequence data. LSTM is able to capture long-term dependencies and remember important historical information, which is very important for predicting ice intensity. The network parameters in the LSTM include target time series distribution information, which is determined during the commissioning process.

[0025] When determining the distribution coding array, the system first loads the monitored icing association representation array set into the debugged icing monitoring neural network. For each array element, the system determines the distribution coding array corresponding to the element in the target time series distribution information based on the position of the element in the monitored icing association representation array set. Taking C1 as an example, since it is the data of the first monitoring cycle, the system will assign it a position code, such as [1.0, 0.0, 0.0], which means that its position in the time series is 1. Similarly, C2 and C3 will have their respective position codes [0.0, 1.0, 0.0] and [0.0, 0.0, 1.0].

[0026] In order to more accurately represent the position information, the system can also use sine and cosine functions to generate position encoding. For example, for the element at the i-th position, its position encoding can be expressed as:

[0027] pos_encoding[i]=[sin(i / 10000^(2*j / d)),cos(i / 10000^(2*j / d+1))]

[0028] Where d is the dimension of the encoding and j is the index from 0 to d - 1. This approach can better capture the relationship between positions rather than just simple integer positions.

[0029] After the system completes the generation of the distributed coding arrays, these coding arrays will be combined with the original array elements to form a new integrated icing association representation array set. In this way, the system adds time and position information to each array element, thereby enhancing the expressiveness of the data and making subsequent reasoning more accurate.

[0030] In summary, step S200 increases the information of the time dimension by allocating a distribution coding array to each array element in the monitored icing association representation array set, thereby providing a richer feature representation for subsequent predictions.

[0031] Step S300: Integrate each array element in the monitored icing association representation array set with the corresponding distribution coding array to obtain an integrated icing association representation array set.

[0032] The task of the computer system in step S300 is to integrate each array element in the monitored icing association representation array set with the corresponding distribution coding array, so as to obtain an integrated icing association representation array set. The core of this operation is to enhance the time characteristics of the original data set so that it can better reflect the trend and law of data changes over time in the subsequent analysis and prediction process.

[0033] Taking the previously acquired monitored icing-related characterization array set as an example, assume that there are three monitoring period data, namely C1 = [T1, H1, F1, I1], C2 = [T2, H2, F2, I2] and C3 = [T3, H3, F3, I3], where T represents temperature, H represents humidity, F represents wind speed, and I represents icing intensity. Assume that the system has assigned corresponding distribution coding arrays according to the positions of these array elements in the time series, for example, the distribution coding corresponding to C1 is E1 = [1.0, 0.0, 0.0], the distribution coding corresponding to C2 is E2 = [0.0, 1.0, 0.0], and the distribution coding corresponding to C3 is E3 = [0.0, 0.0, 1.0].

[0034] In the integration stage, the computer system first reads the set of monitored icing-related representation arrays and the corresponding distribution coding arrays. Then, for each array element, the system merges it with the corresponding distribution coding array. The specific merging method can be achieved through a simple splicing operation, that is, appending the distribution coding array to the corresponding icing-related representation array. For example, the result of merging C1 and E1 is C1'=[T1,H1,F1,I1,1.0,0.0,0.0]; the result of merging C2 and E2 is C2'=[T2,H2,F2,I2,0.0,1.0,0.0]; the result of merging C3 and E3 is C3'=[T3,H3,F3,I3,0.0,0.0,1.0].

[0035] After completing the above operations, the computer system obtains an integrated icing association representation array set {C1', C2', C3'}. At this time, each array element not only contains the original environmental parameters and icing intensity parameters, but also contains additional time and location information, thus forming a richer and more comprehensive data representation.

[0036] The integrated array collection is further used to train a machine learning model, such as a model based on a combination of a convolutional neural network (CNN) and a long short-term memory network (LSTM). This model can simultaneously process spatial features (extracted by CNN) and time series features (captured by LSTM). During the training process, the system will use an appropriate loss function to measure the gap between the predicted value and the actual value. Common loss functions include mean square error (MSE) or root mean square error (RMSE).

[0037] Throughout the training process, the computer system will continuously evaluate the model performance and adjust the model structure or parameter settings based on the evaluation results. Once the model training is completed and passes the validation test, the system can use the model to make real-time predictions of future icing intensity, thus providing a scientific basis for icing warning and prevention measures.

[0038] Through the integration operation in step S300, the computer system not only enhances the time dimension information of the original data, but also provides a more complete and accurate data basis for the subsequent AI-based real-time monitoring and prediction of icing intensity.

[0039] Step S400: performing reasoning based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods.

[0040] The task of the computer system in step S400 is to perform reasoning based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods. This step is a key link in the entire AI-based icing intensity real-time monitoring and prediction method, which aims to use the processed historical data to predict future icing intensity.

[0041] Specifically, the computer system first loads and integrates the array set of icing-related representations. This set contains the array elements integrated by step S300, and each element not only contains the environmental parameters (such as temperature, humidity, wind speed, etc.) and icing intensity parameters within the monitoring period, but also has additional time and location information. For example, assume that the array set after system integration is {C1', C2', C3'}, where C1' = [T1, H1, F1, I1, 1.0, 0.0, 0.0], C2' = [T2, H2, F2, I2, 0.0, 1.0, 0.0], and C3' = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Here, T, H, and F represent temperature, humidity, and wind speed, respectively, I represents icing intensity, and the following 1.0 and 0.0 combination represents time and location coding.

[0042] Next, the system will perform inference on the set of arrays that integrate the icing association representations. This process usually involves inputting the set of arrays into a pre-trained machine learning model that can predict future icing intensity based on historical data. In this case, a debugged ice monitoring neural network with an intensity inference component can be used for inference. For example, a model that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) can be used. CNN is used to extract spatial features of environmental parameters, while LSTM is used to capture time series features.

[0043] During the inference process, the system first performs array mapping on the set of integrated icing association representation arrays. This process can be achieved by summing or averaging the array elements in the same integrated icing association representation array to form a new array. For example, for C1', C2', and C3', the system may calculate the average values ​​of their respective environmental parameters and icing intensity parameters, and retain the time and position information. Assuming that the average values ​​of C1', C2', and C3' are [T_avg, H_avg, F_avg, I_avg], the system will form a new target array representation G = [T_avg, H_avg, F_avg, I_avg, 1.0, 0.0, 0.0]. The time and position information here remains unchanged because the time and position information is crucial for reasoning about future icing intensity.

[0044] The system then loads the target array representation into the intensity inference component for inference. The intensity inference component can be a multilayer perceptron (MLP) or other type of neural network that accepts the target array representation as input and outputs the icing intensity within the prediction period. For example, assuming that the system needs to predict the icing intensity for the next three periods, the intensity inference component will output three prediction values ​​[I_pred1, I_pred2, I_pred3], where I_pred1 is the icing intensity for the first prediction period, I_pred2 is the icing intensity for the second prediction period, and I_pred3 is the icing intensity for the third prediction period. During the training process, the system uses an appropriate loss function to measure the difference between the predicted value and the actual value. Common loss functions include mean square error (MSE) or root mean square error (RMSE). In addition, if there are multiple sets of monitored icing association representation arrays, each set corresponds to a different monitoring period, the system also needs to merge the target array representations obtained from each set. For example, if there are two array sets of monitored icing association representations, the system will obtain the target array representations G1 and G2 respectively, and then merge G1 and G2 into a merged array representation M = [G1, G2]. The merged array representation M is then loaded into the intensity reasoning component for reasoning to obtain the icing intensity of multiple different prediction periods.

[0045] Through the inference operation in step S400, the computer system can use the integrated historical data to predict the future icing intensity, thereby providing a scientific basis for icing warning and preventive measures. This process not only improves the accuracy of the prediction, but also enhances the reliability of the system in practical applications.

[0046] As an implementation manner, step S200, according to the distribution of each array element in the monitored icing association representation array set in the monitored icing association representation array set, determines the distribution code array corresponding to each array element, which may specifically include:

[0047] Step S210: loading the monitored icing association representation array set into the debugged icing monitoring neural network; the network parameter variables of the icing monitoring neural network include the target time series distribution information; the debugged icing monitoring neural network is used to infer the icing intensity of the target area in the prediction period; the target time series distribution information is determined when the debugged icing monitoring neural network is obtained during debugging;

[0048] Step S220: for each array element in the monitored icing association representation array set, according to the distribution of the array element in the monitored icing association representation array set, determine the distribution coding array corresponding to the array element in the target time series distribution information.

[0049] In the implementation of step S200, the computer system determines the distribution coding arrays corresponding to the array elements according to the distribution of the array elements in the monitored icing association representation array set. This process is specifically divided into two steps: step S210 and step S220.

[0050] In step S210, the computer system first loads the monitored icing association representation array set into the debugged icing monitoring neural network. This icing monitoring neural network is a trained model, and its network parameters include target time series distribution information, which is determined during the debugging process. The debugged icing monitoring neural network is mainly used to infer the icing intensity of the target area within the prediction period.

[0051] For example, suppose that the computer system has collected data from three monitoring cycles, forming a set of monitored icing association representation arrays {C1, C2, C3}, where C1 = [T1, H1, F1, I1], C2 = [T2, H2, F2, I2], and C3 = [T3, H3, F3, I3]. Here, T represents temperature, H represents humidity, F represents wind speed, and I represents icing intensity. The distribution of these array elements in the time series is that C1 corresponds to the first monitoring cycle, C2 corresponds to the second monitoring cycle, and C3 corresponds to the third monitoring cycle.

[0052] The computer system loads these arrays into a debugged ice monitoring neural network. The neural network may be a long short-term memory network (LSTM) because LSTM is good at capturing long-term dependencies in time series data. During the debugging process, the system has determined the target time series distribution information, which is stored in the network's parameters for use during network inference.

[0053] In step S220, for each array element in the monitored icing association representation array set, the computer system determines the distribution coding array corresponding to the array element in the target time series distribution information according to the distribution of the array element in the set (ie, the time position). This process can be illustrated by a specific example.

[0054] Assume that the system has determined the target time series distribution information, which can be a series of position codes generated by sine and cosine functions. For example, for the data C1 of the first monitoring cycle, its position code can be [1.0, 0.0, 0.0]; for the data C2 of the second monitoring cycle, its position code can be [0.0, 1.0, 0.0]; for the data C3 of the third monitoring cycle, its position code can be [0.0, 0.0, 1.0]. These position codes represent the positions of each array element in the time series.

[0055] Specifically, for the data C1 of the first monitoring cycle, the system will find its corresponding position code in the target time series distribution information and assign it to C1. Similarly, for C2 and C3, the system will also find and assign the corresponding position codes respectively. These position codes reflect the order of each array element in the time series, which helps the neural network better understand and process time series data.

[0056] When determining the distribution encoding array, the system may use sine and cosine functions to generate position encodings. For example, for the element at the i-th position, its position encoding can be expressed as:

[0057] pos_encoding[i]=[sin(i / 10000 2*j / d ),cos(i / 10000 2*j / d+1 )];

[0058] Where d is the dimension of the encoding and j is the index from 0 to d - 1. This approach can better capture the relationship between positions rather than just simple integer positions.

[0059] In this way, the computer system assigns a distribution coding array to each array element. These coding arrays are combined with the original array elements to form a new integrated ice association representation array set. For example, C1 is combined with its corresponding distribution coding array to become C1'=[T1,H1,F1,I1,1.0,0.0,0.0]; C2 is combined to become C2'=[T2,H2,F2,I2,0.0,1.0,0.0]; C3 is combined to become C3'=[T3,H3,F3,I3,0.0,0.0,1.0].

[0060] After completing the above steps, the computer system obtains an integrated icing association representation array set {C1', C2', C3'}. Each element in this set contains not only the original environmental parameters and icing intensity parameters, but also the time location information. In this way, the system can better utilize the information of the time dimension in the subsequent reasoning process and improve the accuracy of the prediction.

[0061] Throughout the process, the computer system loads the monitored icing association representation array set into the debugged icing monitoring neural network, and determines the corresponding distribution coding array according to the distribution of each array element in the set, thus realizing the effective use of historical data. This process not only enhances the temporal characteristics of the data, but also provides a richer feature representation for subsequent predictions.

[0062] As an implementation mode, the debugged icing monitoring neural network further includes an intensity reasoning component; based on this, step S400, reasoning is performed based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods, which may specifically include:

[0063] Step S410: performing array mapping on the integrated icing association representation array set (for example, summing or averaging array elements in the same integrated icing association representation array, so that the calculated values ​​of each integrated icing association representation array constitute a new array) to obtain a target array representation;

[0064] Step S420: Load the target array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in one or more prediction periods.

[0065] In step S400, the computer system infers the integrated icing association representation array set based on the intensity reasoning component in the debugged icing monitoring neural network to obtain the icing intensity of the monitoring area in one or more prediction periods. The process specifically includes steps S410 and S420.

[0066] In step S410, the computer system performs an array mapping operation on the integrated icing-related representation array set. This operation aims to obtain a new array representation, namely the target array representation, by summing or averaging the array elements at the same position. To illustrate this process with a specific example, assume that the integrated icing-related representation array set contains three arrays: C1' = [T1, H1, F1, I1, 1.0, 0.0, 0.0], C2' = [T2, H2, F2, I2, 0.0, 1.0, 0.0], C3' = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Here, T, H, and F represent temperature, humidity, and wind speed, respectively, I represents icing intensity, and the last three elements are position codes. The computer system performs mapping operations on these arrays. For example, the first element (i.e., temperature) of all arrays can be averaged to obtain a new temperature characteristic value T_avg = (T1 + T2 + T3) / 3; similarly, the humidity, wind speed, and icing intensity are also averaged to obtain H_avg, F_avg, and I_avg. In addition, for the position coding part, since they are fixed, they can be directly retained without further processing. The new array finally formed is represented as the target array representation G = [T_avg, H_avg, F_avg, I_avg, 1.0, 1.0, 1.0].

[0067] In step S420, the computer system loads the target array representation obtained in the previous step into the strength reasoning component for reasoning. The strength reasoning component is usually composed of one or more neural network layers, which are used to process input data and output prediction results. In this example, the strength reasoning component may include multiple fully connected layers (Fully Connected Layers) and activation functions (such as ReLU), as well as an output layer. The computer system inputs the target array representation G into the first layer of the strength reasoning component, which applies a set of weights W1 and biases b1 for linear transformation, and then obtains the hidden layer representation through the activation function. For example, the hidden layer representation can be expressed as H=ReLU(W1*G+b1). Next, the hidden layer representation H will be passed to the next layer, and this process will be repeated until the last layer, which is responsible for outputting the predicted icing intensity.

[0068] During the training phase, in order to optimize the weights and bias parameters in the intensity inference component, the computer system uses a loss function to measure the difference between the predicted value and the actual value. A common loss function is the mean square error, which is defined as the average of the sum of the squares of the differences between the predicted value and the true value. For example, assuming that the predicted icing intensity is I_pred and the true icing intensity is I_true, the loss L can be expressed as L = (I pred -I true ) 2 To minimize the loss function, the computer system updates the network parameters using back-propagation and gradient descent or other optimization algorithms.

[0069] In the inference phase, the computer system takes the target array representation G as input and obtains the predicted icing intensity I through the intensity inference component. pred This forecast result represents the level of icing intensity that may occur in the monitored area in one or more future forecast periods. In this way, the computer system can not only evaluate the current icing situation, but also predict the icing trend in the future, thereby providing timely and effective warning information to relevant departments.

[0070] In summary, the computer system implements the icing intensity prediction based on the integrated icing association representation array set through steps S410 and S420. This process not only covers the preprocessing and feature extraction of data, but also includes the specific implementation method of reasoning through the intensity reasoning component. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0071] As an implementation manner, step S410, performing array mapping on the integrated icing association representation array set to obtain a target array representation, may specifically include:

[0072] Step S411: for each integrated icing association representation array in the integrated icing association representation array set, perform operations on each array element in the integrated icing association representation array to obtain an implicit representation operation value corresponding to the integrated icing association representation array;

[0073] Step S412: according to the distribution of the integrated icing association representation arrays in the integrated icing association representation array set, the implicit representation operation values ​​corresponding to each integrated icing association representation array are arranged in time sequence to obtain the target array representation.

[0074] The task of the computer system in step S410 is to perform array mapping on the integrated icing association representation array set to obtain a target array representation. This process is specifically divided into two steps: step S411 and step S412.

[0075] In step S411, the computer system processes each integrated icing association representation array in the integrated icing association representation array set, and operates each array element in each array to obtain an implicit representation operation value corresponding to the array. This operation can be summing, averaging or other mathematical operations on the array elements, in order to extract key features of each array.

[0076] For example, suppose that the computer system already has an integrated icing-related representation array set {C1', C2', C3'}, where C1' = [T1, H1, F1, I1, 1.0, 0.0, 0.0], C2' = [T2, H2, F2, I2, 0.0, 1.0, 0.0], C3' = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Here, T, H, and F represent temperature, humidity, and wind speed, respectively, I represents icing intensity, and the last three elements are position codes. The computer system will perform operations on each array element in each array, such as calculating the average, to obtain the implicit representation operation value of each array.

[0077] Specifically, for C1', the computer system calculates the average value of T1, H1, F1 and I1 to obtain the implicit representation operation value C1'_avg = (T1 + H1 + F1 + I1) / 4. Similarly, for C2' and C3', the average values ​​of T2, H2, F2, I2 and T3, H3, F3, I3 are calculated respectively to obtain the implicit representation operation values ​​C2'_avg = (T2 + H2 + F2 + I2) / 4 and C3'_avg = (T3 + H3 + F3 + I3) / 4.

[0078] In step S412, the computer system arranges the implicit representation operation values ​​corresponding to each integrated icing association representation array in time sequence according to the distribution of the integrated icing association representation array in the integrated icing association representation array set, thereby obtaining a target array representation. This process is to arrange the implicit representation operation values ​​of each array in time sequence to form a new array representation.

[0079] Continuing with the above example, assuming that C1', C2' and C3' correspond to three different monitoring periods, their positions in the time series are 1, 2 and 3 respectively. The computer system arranges C1'_avg, C2'_avg and C3'_avg in chronological order to form a new array representation G = [C1'_avg, C2'_avg, C3'_avg].

[0080] Specifically, if C1'_avg = 2.5, C2'_avg = 3.0, C3'_avg = 2.8, then the target array representation G = [2.5, 3.0, 2.8]. This target array representation contains the implicit representation operation values ​​of each monitoring period and is arranged in chronological order.

[0081] In practical applications, the computer system may further process the target array representation, such as normalization or standardization, to ensure that the data has better comparability and consistency in subsequent processing. For example, assuming that the original value range of an environmental parameter is large, it can be scaled to the [0,1] interval through the normalization formula. The normalization formula is:

[0082]

[0083] Among them, x' is the normalized value, x is the original value, and x min and x max are the minimum and maximum values ​​of the parameter in the entire data set, respectively.

[0084] After completing the above steps, the computer system obtains the target array representation G, which contains the implicit representation operation values ​​of each array in the integrated icing association representation array set and is arranged in chronological order. In this way, the system provides a more concise and effective data representation for the next step of reasoning.

[0085] In summary, the computer system implements the array mapping operation of the integrated icing association representation array set through steps S411 and S412, thereby obtaining the target array representation. This process not only extracts the key features of each array, but also enhances the time characteristics of the data through time series arrangement, providing a richer feature representation for subsequent predictions.

[0086] As an implementation manner, there are multiple monitored icing association representation array sets, and each monitored period corresponding to different monitored icing association representation array sets has one or more different monitored periods; based on this, step S420, loading the target array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in one or more prediction periods, may specifically include:

[0087] Step S421: performing a merging operation on the target array representations obtained from each monitored icing association representation array set to obtain a merged array representation;

[0088] Step S422: Load the merged array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in multiple different prediction periods.

[0089] The task of the computer system in step S420 is to load the target array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in one or more prediction periods. This process is specifically divided into two steps: step S421 and step S422.

[0090] In step S421, the computer system merges the target array representations obtained from each monitored ice association representation array set to obtain a merged array representation. This process is intended to integrate data from different monitored periods so that subsequent reasoning can comprehensively consider information from multiple periods.

[0091] Let's take a specific example to illustrate this process. Assume that the computer system already has two sets of arrays representing the association of monitored icing: Set A and Set B. Set A contains data from three monitoring cycles, and the target array formed is represented by GA1 = [T1, H1, F1, I1, 1.0, 0.0, 0.0], GA2 = [T2, H2, F2, I2, 0.0, 1.0, 0.0], GA3 = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Set B contains data from two monitoring cycles, and the target array formed is represented by GB1 = [T4, H4, F4, I4, 1.0, 0.0], GB2 = [T5, H5, F5, I5, 0.0, 1.0]. Here, T, H, and F represent temperature, humidity, and wind speed, respectively, I represents icing intensity, and the last element is the position code.

[0092] The computer system arranges the target array representation of set A in time sequence to obtain GA = [GA1, GA2, GA3]. Similarly, the target array representation of set B is arranged in time sequence to obtain GB = [GB1, GB2]. Next, the computer system merges GA and GB to form a merged array representation M = [GA1, GA2, GA3, GB1, GB2]. This merging operation ensures that the data of all monitoring periods are comprehensively considered, thereby improving the accuracy and robustness of the prediction.

[0093] In step S422, the computer system loads the merged array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in multiple different prediction periods. The intensity reasoning component is usually composed of one or more neural network layers for processing input data and outputting prediction results. In this example, the intensity reasoning component may include multiple fully connected layers and activation functions (such as ReLU), and an output layer.

[0094] The computer system inputs the merged array representation M to the first layer of the intensity inference component, which applies a set of weights W1 and biases b1 for linear transformation, and then obtains the hidden layer representation through the activation function. For example, the hidden layer representation can be expressed as H = ReLU (W1*M+b1). Next, the hidden layer representation H is passed to the next layer, and the process is repeated until the last layer, which is responsible for outputting the predicted icing intensity.

[0095] During the training phase, in order to optimize the weights and bias parameters in the intensity inference component, the computer system uses a loss function to measure the difference between the predicted value and the actual value. A common loss function is the mean squared error (MSE), which is defined as the average of the sum of the squares of the differences between the predicted value and the true value. For example, assuming that the predicted icing intensity is I_pred and the true icing intensity is I_true, the loss L can be expressed as:

[0096]

[0097] Where n is the number of samples. In order to minimize the loss function, the computer system will use the backpropagation algorithm and the gradient descent method or other optimization algorithms to update the network parameters.

[0098] In the inference phase, the computer system takes the merged array representation M as input and obtains the predicted icing intensity I_pred through the intensity inference component. This prediction result represents the icing intensity level that may occur in the monitoring area in one or more future prediction periods. In this way, the computer system can not only evaluate the current icing situation, but also predict the icing trend in the future, thereby providing timely and effective warning information to relevant departments.

[0099] In summary, through steps S421 and S422, the computer system realizes the merging of target array representations of multiple monitored icing association representation array sets, and performs reasoning through the intensity reasoning component to obtain the icing intensity of the monitoring area in multiple different prediction periods. This process not only covers the comprehensive processing and feature extraction of data, but also includes the specific implementation method of reasoning through the intensity reasoning component. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0100] As an implementation method, the process of obtaining a debugged icing monitoring neural network may include the following steps:

[0101] Step S10: determining the distribution coding arrays corresponding to the array elements in the template icing association representation array set in the distribution information of the network parameter variables of the icing monitoring neural network that needs to be debugged; the template icing association representation array set includes a plurality of template icing association representation arrays in a first monitored period, and the template icing association representation array in the first monitored period includes an icing intensity parameter representation array and an icing environment parameter representation array of the monitoring area in the first monitored period;

[0102] Step S20: Integrate each array element in the template icing association representation array set with the corresponding distribution coding array, and infer the icing intensity of the monitoring area in one or more second monitored periods according to the integration result;

[0103] Step S30: According to the icing intensity of the monitoring area in one or more second monitored periods obtained by reasoning, optimize the timing distribution information of the icing monitoring neural network until the state of the icing monitoring neural network is stable, and use the icing monitoring neural network in the stable state as the debugged icing monitoring neural network; the timing distribution information in the stable state is the target timing distribution information.

[0104] The computer system, in the process of obtaining the debugged icing monitoring neural network, specifically includes the following steps: step S10, step S20 and step S30.

[0105] In step S10, the computer system first determines the distribution coding arrays corresponding to each array element in the template icing association representation array set in the distribution information of the network parameter variables of the icing monitoring neural network that needs to be debugged. The template icing association representation array set includes multiple template icing association representation arrays under the first monitored period, and each array includes an icing intensity parameter representation array and an icing environment parameter representation array of the monitoring area under the first monitored period.

[0106] Specifically, assume that the computer system has collected data from three monitoring cycles to form a template icing association representation array set {C1, C2, C3}, where C1 = [T1, H1, F1, I1], C2 = [T2, H2, F2, I2], and C3 = [T3, H3, F3, I3]. Here T represents temperature, H represents humidity, F represents wind speed, and I represents icing intensity. The distribution of these array elements in the time series is that C1 corresponds to the first monitoring cycle, C2 corresponds to the second monitoring cycle, and C3 corresponds to the third monitoring cycle. The computer system needs to assign corresponding distribution coding arrays to these array elements. Assuming that the positions of each array element in the template icing association representation array set in the time series are 1, 2, and 3, respectively, then they can be assigned corresponding distribution coding arrays. For example, for the data C1 of the first monitoring cycle, its position encoding may be [1.0, 0.0, 0.0]; for the data C2 of the second monitoring cycle, its position encoding may be [0.0, 1.0, 0.0]; and for the data C3 of the third monitoring cycle, its position encoding may be [0.0, 0.0, 1.0].

[0107] In step S20, the computer system integrates each array element in the template icing association representation array set with the corresponding distribution coding array, and infers the icing intensity of the monitoring area in one or more second monitored periods based on the integration result. This process is intended to train the neural network through the integrated data set so that it can learn the relationship between icing intensity and environmental parameters.

[0108] Specifically, the computer system integrates the template icing association representation arrays C1, C2, and C3 with the corresponding distribution coding arrays. For example, C1 is combined with its corresponding distribution coding array to become C1'=[T1, H1, F1, I1, 1.0, 0.0, 0.0]; C2 is combined to become C2'=[T2, H2, F2, I2, 0.0, 1.0, 0.0]; C3 is combined to become C3'=[T3, H3, F3, I3, 0.0, 0.0, 1.0]. In this way, a new integrated icing association representation array set {C1', C2', C3'} is formed. Next, the computer system uses the integrated array set {C1', C2', C3'} to train the icing monitoring neural network. During the training process, the system uses these arrays as input and the corresponding icing intensity as the output label. In this way, the system can learn how to predict icing intensity based on environmental parameters.

[0109] In step S30, the computer system optimizes the time series distribution information of the icing monitoring neural network according to the icing intensity of the monitoring area in one or more second monitoring cycles obtained by reasoning, until the state of the icing monitoring neural network is stable. The icing monitoring neural network in a stable state is regarded as the debugged icing monitoring neural network, and the time series distribution information in a stable state is regarded as the target time series distribution information.

[0110] Specifically, during the training process, the computer system uses a loss function to measure the difference between the predicted value and the actual value. A common loss function is the mean squared error, which is defined as the average of the sum of the squares of the differences between the predicted value and the true value.

[0111] During the training process, the computer system will continuously monitor the value of the loss function. When the value of the loss function no longer decreases significantly, it indicates that the model has converged and reached a stable state. At this time, the ice monitoring neural network is regarded as a debugged ice monitoring neural network, and its internal time series distribution information is also determined as the final target time series distribution information.

[0112] In this way, the computer system not only optimizes the parameters of the icing monitoring neural network, but also ensures that the model can perform well in future prediction tasks. Ultimately, the debugged icing monitoring neural network can be used in practical applications to predict the icing intensity of the monitoring area in one or more prediction periods.

[0113] In summary, the computer system implements the debugging process of the icing monitoring neural network through steps S10, S20 and S30. This process not only includes the processing and feature extraction of historical data, but also covers the specific implementation methods of reasoning and optimization through neural networks. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0114] As an implementation manner, step S20, integrating each array element in the template icing association representation array set with the corresponding distribution coding array, and inferring the icing intensity of the monitoring area in one or more second monitored periods according to the integration result, may include:

[0115] Step S21: Integrate each array element in the template icing association representation array set with the corresponding distribution coding array to obtain a template integrated icing association representation array set;

[0116] Step S22: performing array mapping on the template integrated icing association representation array set to obtain a template mapping array representation;

[0117] Step S23: Load the template mapping array representation into the intensity reasoning component of the icing monitoring neural network to reason about the icing intensity of the monitoring area in one or more second monitored periods.

[0118] The task of the computer system in step S20 is to integrate each array element in the template icing association representation array set with the corresponding distribution coding array, and infer the icing intensity of the monitoring area in one or more second monitored periods based on the integration result. This process is specifically divided into three steps: step S21, step S22 and step S23.

[0119] In step S21, the computer system integrates each array element in the template icing association representation array set with the corresponding distribution coding array to obtain a template integrated icing association representation array set. This integration process is intended to add time position information to each array element, thereby enhancing the temporal characteristics of the data. Specifically, assuming that data from three monitoring cycles have been collected, a template icing association representation array set {C1, C2, C3} is formed, where C1 = [T1, H1, F1, I1], C2 = [T2, H2, F2, I2], and C3 = [T3, H3, F3, I3]. Here T represents temperature, H represents humidity, F represents wind speed, and I represents icing intensity. The distribution of these array elements in the time series is that C1 corresponds to the first monitoring cycle, C2 corresponds to the second monitoring cycle, and C3 corresponds to the third monitoring cycle.

[0120] The computer system needs to assign corresponding distribution coding arrays to these array elements. Assuming that the positions of each array element in the template icing association representation array set in the time series are 1, 2, and 3, respectively, then corresponding distribution coding arrays can be assigned to them. For example, for the data C1 of the first monitoring cycle, its position coding can be [1.0, 0.0, 0.0]; for the data C2 of the second monitoring cycle, its position coding can be [0.0, 1.0, 0.0]; for the data C3 of the third monitoring cycle, its position coding can be [0.0, 0.0, 1.0].

[0121] Next, the computer system integrates the template ice association representation array with the corresponding distribution coding array. For example, C1 is combined with its corresponding distribution coding array to become C1' = [T1, H1, F1, I1, 1.0, 0.0, 0.0]; C2 is combined to become C2' = [T2, H2, F2, I2, 0.0, 1.0, 0.0]; C3 is combined to become C3' = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. In this way, a new template integrated ice association representation array set {C1', C2', C3'} is formed.

[0122] In step S22, the computer system performs array mapping on the template integrated icing association representation array set to obtain a template mapping array representation. This process aims to extract key features and form a new array representation by further processing the integrated array.

[0123] Specifically, the computer system can perform an average operation on each array in the template integrated icing association representation array set to obtain a new array representation. For example, assuming that the average values ​​of C1', C2' and C3' are [T_avg, H_avg, F_avg, I_avg, 1.0, 0.0, 0.0], the system will form a new template mapping array representation G = [T_avg, H_avg, F_avg, I_avg, 1.0, 0.0, 0.0].

[0124] In step S23, the computer system loads the template mapping array representation into the intensity reasoning component of the icing monitoring neural network to reason about the icing intensity of the monitoring area in one or more second monitored periods. This process aims to predict the future icing intensity based on the integrated data set through the trained neural network model.

[0125] Specifically, assuming that the template mapping array representation G is ready, the computer system loads G as input into the strength reasoning component of the ice monitoring neural network. The strength reasoning component is usually composed of one or more neural network layers for processing input data and outputting prediction results. In this example, the strength reasoning component may contain multiple fully connected layers and activation functions (such as ReLU), as well as an output layer.

[0126] The computer system inputs the template map array representation G into the first layer of the intensity inference component, which applies a set of weights W1 and biases b1 for linear transformation, and then obtains the hidden layer representation through the activation function. For example, the hidden layer representation can be expressed as H = ReLU (W1*G+b1). Next, the hidden layer representation H will be passed to the next layer, and this process will be repeated until the last layer, which is responsible for outputting the predicted ice intensity.

[0127] During the training phase, in order to optimize the weights and bias parameters in the intensity reasoning component, the computer system uses a loss function to measure the difference between the predicted value and the actual value. A common loss function is the mean square error (MSE), which is defined as the average of the sum of the squares of the difference between the predicted value and the true value. During the reasoning phase, the computer system takes the template mapping array representation G as input and obtains the predicted icing intensity I_pred through the intensity reasoning component. This prediction result represents the level of icing intensity that may occur in the monitoring area in one or more future forecast periods. In this way, the computer system can not only evaluate the current icing situation, but also predict the icing trend in the future, thereby providing timely and effective warning information to relevant departments.

[0128] In summary, the computer system realizes the integration, mapping and reasoning process of the template icing association representation array set through steps S21, S22 and S23. This process not only covers the comprehensive processing and feature extraction of data, but also includes the specific implementation method of reasoning through the intensity reasoning component. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0129] As another implementation of step S200, that is, according to the distribution of each array element in the monitored icing association representation array set in the monitored icing association representation array set, determining the distribution code array corresponding to each array element may include:

[0130] Step S201: for each array element in the monitored icing association representation array set, determining the distribution of the array element in the monitored icing association representation array to which it belongs, and obtaining first distribution information;

[0131] Step S202: determining the distribution of the monitored icing association representation array to which the array element belongs in the set of monitored icing association representation arrays to obtain second distribution information;

[0132] Step S203: Determine a distribution coding array of array elements according to the first distribution information and the second distribution information.

[0133] In another embodiment of step S200, the computer system determines the distribution coding arrays corresponding to the array elements according to the distribution of the array elements in the monitored icing association characterization array set in the set. This process is specifically divided into three steps: step S201, step S202 and step S203.

[0134] In step S201, the computer system determines the distribution of each array element in the monitored icing associated representation array set in the monitored icing associated representation array to which it belongs, and obtains the first distribution information. This process is intended to assign each array element its position information in the array to which it belongs. Specifically, it is assumed that the computer system already has a monitored icing associated representation array set {C1, C2, C3}, where C1 = [T1, H1, F1, I1, 1.0, 0.0, 0.0], C2 = [T2, H2, F2, I2, 0.0, 1.0, 0.0], and C3 = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Here, T represents temperature, H represents humidity, F represents wind speed, I represents icing intensity, and the last three elements are position codes. For each element in C1, the computer system will determine its position in the array C1. For example, the position of T1 in C1 is 1, the position of H1 is 2, and so on.

[0135] In step S202, the computer system determines the distribution of the monitored icing association characterization array to which the array element belongs in the monitored icing association characterization array set to obtain second distribution information. This process aims to assign position information of each array element in the set.

[0136] Specifically, for array element T1, it belongs to array C1, and the position of C1 in the monitored icing association representation array set is 1; for array element T2, it belongs to array C2, and the position of C2 in the monitored icing association representation array set is 2; for array element T3, it belongs to array C3, and the position of C3 in the monitored icing association representation array set is 3.

[0137] In step S203, the computer system determines the distribution coding array of the array elements according to the first distribution information and the second distribution information. This process aims to generate a coding array for each array element including its position information in the array to which it belongs and its position information in the set.

[0138] Specifically, assuming that for array element T1, its first distribution information (position in C1) is 1, and its second distribution information (position in the set) is 1, then it can be assigned a distribution coding array [1,1]. For array element T2, its first distribution information is 1, and its second distribution information is 2, then it can be assigned a distribution coding array [1,2]. For array element T3, its first distribution information is 1, and its second distribution information is 3, then it can be assigned a distribution coding array [1,3].

[0139] For other elements, such as H1, H2, and H3, the distribution coding arrays can also be assigned according to the above method. For example, for H1, its first distribution information is 2 and its second distribution information is 1, so it can be assigned a distribution coding array [2,1]; for H2, its first distribution information is 2 and its second distribution information is 2, so it can be assigned a distribution coding array [2,2]; for H3, its first distribution information is 2 and its second distribution information is 3, so it can be assigned a distribution coding array [2,3].

[0140] In this way, the computer system generates a distributed coding array for each array element, which contains its position information in the array to which it belongs and its position information in the set. These distributed coding arrays enhance the temporal characteristics of the data, so that the information of the temporal dimension can be better utilized in the subsequent reasoning process. Specifically, assuming that the computer system generates a distributed coding array for each element in C1, the set of distributed coding arrays of C1 can be expressed as E1 = [[1,1], [2,1], [3,1], [4,1], [5,1], [6,1], [7,1]]. Similarly, the set of distributed coding arrays of C2 and C3 can be expressed as E2 = [[1,2], [2,2], [3,2], [4,2], [5,2], [6,2], [7,2]] and E3 = [[1,3], [2,3], [3,3], [4,3], [5,3], [6,3], [7,3]].

[0141] Through the above steps, the computer system successfully assigns a distribution encoding array to each array element in the monitored ice association representation array set. These encoding arrays contain not only the position information of each element in the array to which it belongs, but also its position information in the entire set, thus providing a richer feature representation for subsequent reasoning.

[0142] In summary, through steps S201, S202 and S203, the computer system determines the distribution of each array element in the monitored icing association characterization array set, and generates a distribution coding array for each array element. This process not only enhances the temporal characteristics of the data, but also provides a richer feature representation for subsequent reasoning. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0143] As an implementation manner, step S203, determining the distribution coding array of the array elements according to the first distribution information and the second distribution information, may specifically include:

[0144] Step S2031: determining a distribution information embedding operator (for example, an embedding function) corresponding to the array element according to the first distribution information; the distribution information embedding operator is used to obtain a distribution coding array corresponding to the array element, and the distribution information embedding operator includes a first variable and a second variable, the first variable is a parameter variable characterizing the distribution of the array element in the monitored icing association representation array to which it belongs, and the second variable is a parameter variable characterizing the distribution of the monitored icing association representation array to which the array element belongs in the set of monitored icing association representation arrays;

[0145] Step S2032: configure the first variable in the distribution information embedding operator to the first distribution information, configure the second variable in the distribution information embedding operator to the second distribution information, determine the result of the distribution information embedding operator, and obtain the distribution coding array of the array elements.

[0146] In a specific implementation of step S203, the computer system determines the distribution coding array of the array element according to the first distribution information and the second distribution information, and this process includes step S2031 and step S2032. In step S2031, the computer system determines the distribution information embedding operator corresponding to the array element according to the first distribution information, and the operator is an embedding function used to obtain the distribution coding array corresponding to the array element. The distribution information embedding operator includes two parameters, the first variable represents the distribution of the array element in the monitored icing association representation array to which it belongs, and the second variable represents the distribution of the monitored icing association representation array to which the array element belongs in the set of monitored icing association representation arrays. In step S2032, the computer system configures the first variable in the distribution information embedding operator to the first distribution information, and configures the second variable to the second distribution information, thereby determining the result of the distribution information embedding operator and obtaining the distribution coding array of the array element.

[0147] For example, assume that the computer system has processed a series of monitoring data related to icing intensity and organized these data into multiple monitored icing-related representation arrays, such as C1 = [T1, H1, F1, I1, 1.0, 0.0, 0.0], C2 = [T2, H2, F2, I2, 0.0, 1.0, 0.0], C3 = [T3, H3, F3, I3, 0.0, 0.0, 1.0]. Here, T represents temperature, H represents humidity, F represents wind speed, I represents icing intensity, and the last three elements are position codes. The computer system first determines that the position information of array element T1 in array C1 is 1, and the position information in the set of monitored icing-related representation arrays is 1. Therefore, for T1, the first distribution information is 1, and the second distribution information is also 1.

[0148] The computer system then defines a distribution information embedding operator E(x,y), where x is the first variable and y is the second variable. For T1, the operator is configured as E(1,1). The operator may be a lookup operation or a neural network layer, and its function is to map the input distribution information to a point in a multidimensional space, which constitutes a distribution encoding array. For example, E(1,1) may be mapped to [0.1,-0.2,0.3], so that the distribution encoding array of T1 is determined to be [0.1,-0.2,0.3].

[0149] In practical applications, the distributed information embedding operator can be a multi-layer perceptron (MLP), whose structure may be that the input layer has two nodes (corresponding to x and y), the hidden layer has several nodes (depending on the complexity of the task), and the output layer has three nodes (corresponding to the dimension of the distributed encoding array). For the operator E(x,y), when x=1, y=1, through forward propagation, the input passes through the linear combination of the weight matrix W and the bias term b, and then passes through the activation function (such as ReLU), and finally obtains the distributed encoding array.

[0150] Assuming that W and b are trained through historical data, the goal of the training process is to minimize the prediction error. The loss function L can be defined as the mean square error (MSE) between the predicted value and the true value.

[0151] The computer system continues to process other elements, such as H1, F1, etc., until all elements are converted into corresponding distribution encoding arrays. A similar process is performed for the elements in C2 and C3. For example, the distribution information embedding operator of T2 is configured as E(1,2), and the mapping result is [0.2, -0.3, 0.4]; the distribution information embedding operator of T3 is configured as E(1,3), and the mapping result is [0.3, -0.4, 0.5].

[0152] Through the above steps, the computer system completes the conversion from the original ice association representation array to the distribution coding array. This step provides a richer and more discriminative feature representation for the subsequent AI-based real-time monitoring and prediction of ice intensity.

[0153] As an implementation manner, step S2031, determining the distribution information embedding operator corresponding to the array element according to the first distribution information, may specifically include:

[0154] Step S20311: when the first distribution information is an odd number position, determining that the distribution information embedding operator corresponding to the array element is the first operator (cosine);

[0155] Step S20312: When the first distribution information is an even number position, determine that the distribution information embedding operator corresponding to the array element is the second operator (sine).

[0156] The task of the computer system in step S2031 is to determine the distribution information embedding operator corresponding to the array element according to the first distribution information. This process is specifically divided into two steps: step S20311 and step S20312.

[0157] In step S20311, when the first distribution information is an odd position, the computer system determines that the distribution information embedding operator corresponding to the array element is the first operator, namely the cosine function. This process is intended to assign the position information of the array element in the array to which it belongs and encode it using the cosine function.

[0158] Specifically, it is assumed that the computer system has processed a series of monitoring data related to icing intensity and organized these data into multiple arrays of monitored icing correlation representations, such as:

[0159] C1=[T1,H1,F1,I1,1.0,0.0,0.0];

[0160] C2=[T2,H2,F2,I2,0.0,1.0,0.0];

[0161] C3=[T3,H3,F3,I3,0.0,0.0,1.0].

[0162] Here T represents temperature, H represents humidity, F represents wind speed, I represents icing intensity, and the last three elements are position codes.

[0163] For array C1, the computer system determines the position information of each element in the array. For example, T1 is at position 1 in C1, H1 is at position 2, F1 is at position 3, and I1 is at position 4. Since T1 is at position 1, which is an odd position, the computer system determines that the distribution information embedding operator corresponding to T1 is the first operator, i.e., the cosine function. Similarly, F1 is at position 3, which is also an odd position, so its corresponding distribution information embedding operator is also a cosine function.

[0164] For arrays C2 and C3, the computer system also determines the position information of each element in the array. For example, the position of T2 in C2 is 1, which is also an odd position, so its corresponding distribution information embedding operator is the cosine function; the position of F2 in C2 is 3, which is also an odd position, so its corresponding distribution information embedding operator is also a cosine function.

[0165] In step S20312, when the first distribution information is an even number position, the computer system determines that the distribution information embedding operator corresponding to the array element is the second operator, namely, the sine function. This process is intended to assign the position information of the array element in the array to which it belongs and encode it using the sine function.

[0166] Specifically, for array C1, the computer system determines the position information of each element in the array. For example, the position of H1 in C1 is 2, and the position of I1 is 4. Since the position of H1 is 2, which is an even position, the computer system determines that the distribution information embedding operator corresponding to H1 is the second operator, that is, the sine function. Similarly, the position of I1 is 4, which is also an even position, so its corresponding distribution information embedding operator is also a sine function.

[0167] For arrays C2 and C3, the computer system also determines the position information of each element in the array. For example, H2 is at position 2 in C2, which is also an even position, so its corresponding distribution information embedding operator is a sine function; I2 is at position 4 in C2, which is also an even position, so its corresponding distribution information embedding operator is also a sine function.

[0168] In this way, the computer system generates a distribution coding array for each array element, which contains its position information in the array. Specifically, assuming that the position information is p, the distribution information embedding operator can be defined as: when p is an odd position, the cosine function is used for encoding; when p is an even position, the sine function is used for encoding.

[0169] Through the above steps, the computer system successfully assigns distribution information embedding operators to each array element in the set of monitored ice association representation arrays. These embedding operators not only contain the position information of each element in the array to which it belongs, but also provide richer feature representations for subsequent reasoning.

[0170] In summary, through steps S20311 and S20312, the computer system determines the distribution of each array element in the monitored icing association representation array set, and generates a distribution information embedding operator for each array element. This process not only enhances the temporal characteristics of the data, but also provides a richer feature representation for subsequent reasoning. Through such a technical solution, the computer system can effectively process complex data sets and provide a scientific basis for real-time monitoring and prediction of icing intensity.

[0171] As an implementation manner, step S300, integrating each array element in the monitored icing association representation array set with the corresponding distribution coding array to obtain an integrated icing association representation array set, may specifically include:

[0172] Step S310: for each monitored icing association representation array in the monitored icing association representation array set, add a corresponding distribution coding array to each array element of the monitored icing association representation array to obtain an integrated icing association representation array corresponding to the monitored icing association representation array;

[0173] Step S320: according to the distribution of the monitored icing association representation arrays in the monitored icing association representation arrays, each integrated icing association representation array is arranged in time sequence to obtain an integrated icing association representation array set.

[0174] In step S300, the computer system is responsible for integrating each array element in the monitored icing association representation array set with the corresponding distribution coding array, thereby obtaining an integrated icing association representation array set. This process is specifically divided into two steps: step S310 and step S320.

[0175] In step S310, for each array in the set of monitored icing association representation arrays, the computer system adds a corresponding distribution coding array to each array element of the array, thereby obtaining an integrated icing association representation array corresponding to the monitored icing association representation array. For example, consider a monitored icing association representation array A = [T1, H1, F1, I1] consisting of temperature (T), humidity (H), wind speed (F) and icing intensity (I), where T1, H1, F1, I1 represent the temperature, humidity, wind speed and icing intensity values ​​measured at a certain point in time, respectively. According to the method described in step S2031, the computer system generates a corresponding distribution coding array for each element in array A. For example, the distribution coding array for T1 is E_T1 = [cos(1 / 10000 (2*0 / D) ),cos(1 / 10000 (2*2 / D) ),...], the distribution encoding array for H1 is E_H1 = [sin(2 / 10000 (2*0 / D) ),sin(2 / 10000 (2*2 / D) ),...], and so on. Next, the computer system merges these distribution coding arrays with the original array elements to form a new integrated icing association representation array A'=[T1+E_T1,H1+E_H1,F1+E_F1,I1+E_I1]. Here, the plus sign represents the element-by-element addition operation of the vector, that is, combining the original measurement value with its corresponding position code.

[0176] In step S320, the computer system arranges each integrated icing-related characterization array in time sequence according to the distribution of the monitored icing-related characterization arrays in the set, and finally obtains an integrated icing-related characterization array set. This means that the computer system arranges all integrated arrays in the order in which they are monitored to form a new array set. Assuming that there are three integrated arrays A', B' and C', which represent measurement data at different time points, the computer system will sort these arrays according to the timestamps to obtain an ordered array set [A', B', C'] or [C', B', A'], depending on the actual time sequence. Such sorting helps to maintain the temporal continuity of the data, so that subsequent analysis can correctly capture the patterns in the time series.

[0177] While performing the above steps, the computer system may also apply certain machine learning models to further process the integrated data. For example, the computer system may use a long short-term memory network (LSTM) to predict future icing intensity. LSTM is a special recurrent neural network (RNN) that is good at processing time series data. In a specific example, the computer system can use the integrated icing association representation array set as the input of the LSTM model, and learn the changing pattern of icing intensity in the time series by training the LSTM model. During the training process, the computer system will minimize the gap between the predicted value and the actual observed value, and the loss function usually used is the mean square error (MSE). By continuously adjusting the model parameters to reduce the MSE, the computer system can improve the accuracy of the model's prediction of future icing intensity.

[0178] Through the above steps, the computer system can not only effectively utilize the temporal characteristics in historical monitoring data, but also enhance the data feature representation by integrating distributed coding information, thereby improving the performance of the AI-based real-time monitoring and prediction system for icing intensity.

[0179] In some feasible embodiments, the present application may also perform abnormal monitoring on the icing condition of the current monitoring area and analyze whether there is abnormal icing condition. Specifically, the following steps may be included:

[0180] Step S1000: Acquire a monitoring data set of a monitoring area;

[0181] Step S2000: identifying high-risk areas in the monitoring data set, extracting the high-risk areas, and obtaining a high-risk data subset of the monitoring area;

[0182] Step S3000: extracting an icing intensity characterization array of a high-risk data subset;

[0183] Step S4000: dividing the high-risk data subset into icing intensity regions based on the icing intensity characterization array to obtain icing boundaries of the high-risk data subset; dividing the high-risk data subset into icing types based on the icing intensity characterization array to obtain abnormal classification of abnormal icing regions in the high-risk data subset;

[0184] Step S5000: Based on the icing boundary, determine the area range of the abnormal icing area in the high-risk data subset;

[0185] Step S6000: Based on the abnormal classification and area range of the abnormal icing areas in each high-risk data subset, determine the abnormal icing area identification result of the monitoring area.

[0186] In some feasible embodiments, the computer system performs abnormal monitoring of the icing condition in the current monitoring area and analyzes whether there is abnormal icing condition. This process specifically includes the following steps: step S1000, step S2000, step S3000, step S4000, step S5000 and step S6000.

[0187] In step S1000, the computer system obtains a monitoring data set of the monitoring area. This data set contains the environmental parameters and icing intensity data of each monitoring point in the monitoring area within a specific time period. For example, suppose that the computer system has collected data from multiple monitoring points in a monitoring area, and the data set of each monitoring point contains information such as temperature (T), humidity (H), wind speed (F) and icing intensity (I). These data can be organized into an array set of multiple monitoring periods in chronological order, such as C1 = [T1, H1, F1, I1], C2 = [T2, H2, F2, I2], ..., Cn = [Tn, Hn, Fn, In].

[0188] In step S2000, the computer system identifies high-risk areas in the monitoring data set and extracts these high-risk areas to obtain a high-risk data subset of the monitoring area. This process is intended to screen out areas with a higher risk of icing for further analysis. The computer system can use a clustering algorithm (such as K-Means) or an anomaly detection algorithm (such as IsolationForest) to identify high-risk areas. For example, using the K-Means clustering algorithm, the monitoring point data can be divided into multiple clusters, where clusters with higher icing intensity are considered to be high-risk areas. Assuming that two high-risk areas R1 and R2 are identified by the clustering algorithm, the computer system will extract the data of these two areas from the original data set to form a high-risk data subset.

[0189] In step S3000, the computer system extracts the icing intensity characterization array of the high-risk data subset. This process is intended to extract data related to icing intensity in high-risk areas for further analysis. For example, for high-risk areas R1 and R2, the computer system will extract the icing intensity data of these two areas from the monitoring data set to form an icing intensity characterization array. Assuming that the icing intensity data of R1 and R2 are IR1 = [I1, I2, ..., Ia] and IR2 = [Ib, Ic, ..., Id] respectively, the computer system organizes these data into an icing intensity characterization array.

[0190] In step S4000, the computer system divides the high-risk data subset into ice intensity regions based on the ice intensity characterization array to obtain the ice boundary of the high-risk data subset; at the same time, the high-risk data subset is divided into ice types based on the ice intensity characterization array to obtain the abnormal classification of abnormal ice regions in the high-risk data subset. This process is intended to divide different ice regions according to ice intensity data and identify abnormal ice regions. For example, the computer system can use a threshold method to determine the ice boundary, set an ice intensity threshold, and the area above the threshold is considered to be a high ice intensity area. Assuming that the set threshold is I_threshold, the computer system will identify the area with an ice intensity greater than I_threshold and mark it as a high ice intensity area. In addition, the computer system can also identify the type of abnormal ice area through a machine learning model (such as a decision tree, a support vector machine, etc.). For example, a support vector machine model is trained to identify areas with abnormally high ice intensity and classify them as abnormal ice areas.

[0191] In step S5000, the computer system determines the regional scope of the abnormal icing area in the high-risk data subset based on the icing boundary. This process is intended to determine the specific geographical scope of the abnormal icing area. For example, for high-risk areas R1 and R2, the computer system will determine the regional scope where the icing intensity is greater than I_threshold and mark it as the geographical coordinates of the abnormal icing area. Assuming that the abnormal icing area in R1 ranges from [Lat1, Lon1] to [Lat2, Lon2], and the abnormal icing area in R2 ranges from [Lat3, Lon3] to [Lat4, Lon4], the computer system will record these regional scopes.

[0192] In step S6000, the computer system determines the abnormal icing area identification results of the monitoring area based on the abnormal classification and regional scope of the abnormal icing areas in each high-risk data subset. This process is intended to summarize the information of all abnormal icing areas and generate a final abnormal icing area identification report. For example, the computer system will generate a detailed report based on the regional scope and abnormal classification determined in step S5000, which lists the location, scope and type of all abnormal icing areas. Assuming that the abnormal icing areas in R1 and R2 are Type 1 and Type 2 respectively, the computer system will record this information in detail in the report and provide it to relevant personnel for reference.

[0193] Through the above steps, the computer system successfully realized the abnormal monitoring of icing conditions in the monitoring area and identified the abnormal icing areas. This process not only improved the accuracy of icing intensity prediction, but also provided timely and effective early warning information to relevant departments, thus ensuring the safe and stable operation of the power system.

[0194] As an implementation mode, step S2000, identifying high-risk areas in the monitoring data set, extracting the high-risk areas, and obtaining a high-risk data subset of the monitoring area includes:

[0195] Step S2100: determining the average result of the data items of the monitoring data set;

[0196] Step S2200: Based on the data item average result, a plurality of first target data items are determined in the monitoring data set; wherein the difference between the data value of the first target data item and the data item average result is greater than a first reference value or less than a second reference value, the first reference value is greater than zero, and the second reference value is less than zero;

[0197] Step S2300: determining a data range consisting of a plurality of first target data items in a monitoring data set;

[0198] Step S2400: extracting various data ranges from the monitoring data set to obtain a high-risk data subset of the monitoring area.

[0199] In one implementation, the computer system performs step S2000 in a process including step S2100, step S2200, step S2300 and step S2400. This process is used to identify high-risk areas in the monitoring data set and extract these high-risk areas to obtain a high-risk data subset of the monitoring area.

[0200] In step S2100, the computer system determines the average result of the data items of the monitoring data set. To achieve this, the computer system first counts the values ​​of all data items in the monitoring data set, and then calculates the average value of these data items. For example, in the monitoring data set, the data items of each monitoring point include temperature (T), humidity (H), wind speed (F) and icing intensity (I), and the computer system will calculate the average value of these data items. Assuming that there are n monitoring points in the monitoring data set, and the data items of each monitoring point are D = [T, H, F, I], the computer system will calculate the average value M = [MT, MH, MF, MI] of the data items of all monitoring points, where MT = (ΣTi) / n, MH = (ΣHi) / n, MF = (ΣFi) / n, MI = (ΣIi) / n.

[0201] In step S2200, the computer system determines multiple first target data items in the monitoring data set based on the average result of the data items. The first target data items refer to those data items whose difference between the data value and the average result of the data item is greater than the first reference value or less than the second reference value, wherein the first reference value is greater than zero and the second reference value is less than zero. For example, the computer system sets the first reference value to ε1=5°C and the second reference value to ε2=-5°C. Then, the computer system will identify those monitoring point data items whose temperature T data values ​​are 5°C higher than MT or 5°C lower than MT as the first target data items. Similarly, for humidity H, wind speed F and icing intensity I, the computer system also sets the corresponding first reference value and second reference value, and identifies the corresponding first target data items.

[0202] In step S2300, the computer system determines the data range consisting of multiple first target data items in the monitoring data set. This process is intended to determine the distribution range of the first target data item in the monitoring data set. For example, after the computer system identifies the first target data item, it sorts these data items according to geographical location and determines the geographical area they cover. Assuming that the first target data item is distributed in the northeast of the monitoring area, the computer system will determine the range of the northeast and consider this area to be a high-risk area.

[0203] In step S2400, the computer system extracts each data range from the monitoring data set to obtain a high-risk data subset for the monitoring area. This process is intended to extract a data subset identified as a high-risk area. For example, after the computer system determines that the northeastern region is a high-risk area, it will extract all monitoring point data in the region from the monitoring data set to form a high-risk data subset. Assuming that the monitoring point data for the northeastern region in the monitoring data set is D_NE = [T_NE, H_NE, F_NE, I_NE], then the computer system will group these data items into a high-risk data subset.

[0204] In this process, if the application of machine learning models is involved, such as using anomaly detection algorithms to identify high-risk areas, the computer system can use the Isolation Forest algorithm. Isolation Forest is an unsupervised learning algorithm for detecting outliers. It constructs multiple trees by randomly selecting features and randomly splitting feature values ​​to isolate outliers. During the training process, Isolation Forest attempts to isolate a data point with as few splits as possible. The fewer the number of isolations, the more likely the data point is an outlier. Assuming that each data point in the monitoring data set is represented by a four-dimensional vector X = [T, H, F, I], the computer system can identify outliers by training the Isolation Forest model. After the training is completed, Isolation Forest assigns an anomaly score to each data point. The higher the score, the more likely the data point is an outlier. The computer system can set a threshold T_threshold, identify data points with anomaly scores higher than the threshold as high-risk data items, and extract these data items as high-risk data subsets.

[0205] In the process of training the Isolation Forest model, the calculation of the loss function may be involved. Although Isolation Forest does not directly use the loss function, its training effect can be measured by evaluating the average path length of the isolation tree. Assuming that the average path length in a tree is L(T), then the average path length of the entire forest E[L(T)] can be used to evaluate the performance of the model. In addition, Isolation Forest can also evaluate the effect of the model by calculating the anomaly score of the data point. Assuming that the average path length of the data point Xi in the forest is Li(Xi), then the anomaly score of the data point can be defined as S(Xi)=2 (-E[Li(Xi)] / c(n)) , where c(n) is the expected value of the tree height.

[0206] Through the above steps, the computer system successfully identified the high-risk areas in the monitoring data set and extracted the high-risk data subset from it. This process not only helps to more accurately identify potential abnormal icing areas, but also provides a more accurate data basis for subsequent icing intensity monitoring and prediction.

[0207] As an implementation manner, step S2100, determining the average result of the data items of the monitoring data set, may include:

[0208] Step S2110: determining a second target data item in the monitoring data set based on the data values ​​of each data item in the monitoring data set; wherein the data value of the second target data item is greater than a third reference value and less than a fourth reference value, and the third reference value is less than the fourth reference value;

[0209] Step S2120: Obtain the mean of the data values ​​of each second target data item to obtain the average result of the data items of the monitoring data set.

[0210] The process of determining the average result of the data items of the monitoring data set in step S2100 by the computer system specifically includes step S2110 and step S2120. This process is intended to identify and filter out extreme values, thereby obtaining a more robust average result of the data items.

[0211] In step S2110, the computer system determines the second target data item in the monitoring data set based on the data values ​​of each data item in the monitoring data set. Specifically, the computer system selects data items whose data values ​​are greater than the third reference value and less than the fourth reference value as the second target data item. This screening process is intended to exclude those extremely abnormal data points, thereby ensuring that the average value calculated subsequently is more accurate and reliable.

[0212] For example, suppose that the computer system has collected data from multiple monitoring points in the monitoring area, and the data items of each monitoring point include temperature (T), humidity (H), wind speed (F) and icing intensity (I). The computer system first needs to determine the third reference value and the fourth reference value. Assuming that the third reference value is -10°C and the fourth reference value is 30°C, the computer system will identify all monitoring point data items with temperature T data values ​​between -10°C and 30°C as the second target data items. Similarly, for humidity H, wind speed F and icing intensity I, the computer system also sets the corresponding third reference value and fourth reference value, and identifies the corresponding second target data items.

[0213] In step S2120, the computer system obtains the mean of the data values ​​of each second target data item to obtain the average result of the data item of the monitoring data set. This process is intended to calculate the average value of the second target data item, thereby obtaining a more robust average result of the data item.

[0214] For example, the computer system has screened out all monitoring point data items whose temperature T data values ​​are between -10℃ and 30℃. Assuming that the values ​​of these data items are T1, T2, ..., Tn, the computer system will calculate the average value of these data items. The specific calculation formula is as follows:

[0215]

[0216] Similarly, for humidity H, wind speed F and icing intensity I, the computer system will also calculate the corresponding average values ​​MH, MF and MI. Assuming that the values ​​of these data items are H1, H2, ..., Hn; F1, F2, ..., Fn; I1, I2, ..., In, the corresponding average calculation formulas are:

[0217]

[0218] Through the above steps, the computer system successfully determined the average results of the data items of the monitoring data set. This process not only eliminates the influence of extreme values, but also ensures the robustness and reliability of the calculation results.

[0219] For example, suppose that a computer system has collected data from multiple monitoring points in a monitoring area, and the data items of each monitoring point include temperature (T), humidity (H), wind speed (F) and icing intensity (I). Suppose that there are 100 monitoring points in the monitoring data set, and the data values ​​of temperature T are [-15℃, -12℃, -10℃, ..., 28℃, 32℃]. The computer system sets the third reference value to -10℃ and the fourth reference value to 30℃. Then, the computer system will identify all data items with temperature T data values ​​between -10℃ and 30℃ as the second target data items. Suppose the values ​​of these data items are T1, T2, ..., T98, respectively, then the computer system will calculate the average value of these data items:

[0220]

[0221] Similarly, for humidity H, wind speed F and icing intensity I, the computer system will also calculate the corresponding average values ​​MH, MF and MI.

[0222] In this way, the computer system can not only eliminate the influence of extreme values, but also obtain more accurate and reliable average results of data items in the monitoring data set, thereby providing a more accurate data basis for subsequent identification of high-risk areas.

[0223] As an implementation manner, after obtaining the high-risk data subset of the monitoring area, the method may further include: performing data standardization processing on the high-risk data subset to obtain a standardized high-risk data subset.

[0224] After obtaining the high-risk data subset of the monitoring area, the computer system can also perform data standardization on the high-risk data subset to obtain a standardized high-risk data subset. This process is intended to make the data more comparable and consistent in subsequent analysis and modeling processes.

[0225] Specifically, the computer system first obtains the data values ​​of each data item in the high-risk data subset. For example, assume that the computer system has determined that the data items in the high-risk data subset include temperature (T), humidity (H), wind speed (F), and icing intensity (I). The computer system organizes the values ​​of these data items into an array or matrix. Assume that the data items in the high-risk data subset are D = [T1, H1, F1, I1; T2, H2, F2, I2; ...; Tn, Hn, Fn, In], where each row represents the data of a monitoring point, and each column represents a type of environmental parameter or icing intensity.

[0226] Next, the computer system normalizes these data items. Normalization usually involves converting the values ​​of each data item to the same scale, such as converting the values ​​of all data items to the interval [0,1]. The purpose of this is to eliminate the impact of dimensional differences between different data items, making the data easier to compare in subsequent analysis and modeling.

[0227] Computer systems can use the Min-Max standardization method to perform data standardization. The formula for Min-Max standardization is:

[0228]

[0229] Where X new is the normalized value, X is the original value, and X min is the minimum value of the data item in the entire data set, X max It is the maximum value of this data item in the entire data set.

[0230] For example, for the data item of temperature T, assuming the minimum value is -10℃ and the maximum value is 30℃, the computer system will standardize each temperature data value. Assuming the temperature value of a certain monitoring point is 20℃, the result of the standardized temperature value is:

[0231]

[0232] Similarly, the computer system also uses the same formula to standardize humidity H, wind speed F and icing intensity I. Assuming that the humidity value of a certain monitoring point is 80%, the maximum value is 100%, and the minimum value is 30%, then the result of the humidity value after standardization is:

[0233]

[0234] In this way, the computer system standardized all data items in the high-risk data subset to obtain a standardized high-risk data subset. The standardized data subset not only eliminates the dimensional differences between different data items, but also makes the data easier to compare in subsequent analysis and modeling processes.

[0235] In subsequent steps, the computer system can use the standardized high-risk data subset for further analysis and modeling. For example, the computer system can use machine learning models (such as support vector machines, random forests, or neural networks) to identify abnormal icing conditions in high-risk areas. By standardizing the processed data, the model can better capture the patterns and regularities in the data, thereby improving the accuracy and reliability of the prediction.

[0236] For example, a computer system can use a support vector machine (SVM) model to identify abnormal icing conditions in high-risk areas. SVM is a commonly used classification algorithm that maximizes the interval between different categories by finding an optimal hyperplane. When training the SVM model, the computer system can use a standardized high-risk data subset as training data. Assuming that the training process of the SVM model uses soft margin maximization, the loss function can be expressed as:

[0237]

[0238] Where w is the weight vector, b is the bias term, ξ i is the slack variable, C is the penalty parameter, and n is the number of samples. By minimizing the above loss function, the computer system can obtain an optimal classification hyperplane to identify abnormal icing conditions in high-risk areas.

[0239] Through the above steps, the computer system can not only identify the high-risk data subsets in the monitoring area, but also make these data more suitable for subsequent analysis and modeling through data standardization. This process not only improves the efficiency and accuracy of data processing, but also provides a more reliable data basis for subsequent icing intensity monitoring and prediction.

[0240] As an implementation manner, step S5000, based on the icing boundary, determining the area range of the abnormal icing area in the high-risk data subset may include:

[0241] Step S5100: based on the icing boundary and the size of the high-risk data subset before data normalization, the icing boundary is restored to obtain the initial range of the abnormal icing area in the high-risk data subset;

[0242] Step S5200: Based on the initial range, determine the regional range of the abnormal icing area in the high-risk data subset.

[0243] The computer system determines the area range of the abnormal icing area in the high-risk data subset based on the icing boundary in step S5000, which specifically includes steps S5100 and S5200. This process aims to restore the data size before standardization and determine the actual range of the abnormal icing area based on it.

[0244] In step S5100, the computer system restores the icing boundary based on the icing boundary and the size of the high-risk data subset before data normalization to obtain the initial range of the abnormal icing area in the high-risk data subset. This process aims to restore the icing boundary after normalization to the size of the original data, thereby obtaining the initial range of the abnormal icing area.

[0245] The size of the high-risk data subset refers to the range or size of the data in the data set. In this context, size specifically refers to the range of values ​​or values ​​in the data set, especially the range of values ​​of the data will change before and after data standardization. In order to ensure the consistency and accuracy of data processing, it is necessary to keep the original size of the data unchanged before and after standardization.

[0246] Specifically, the size of the high-risk data subset can be understood as the value range of each feature in the data set (such as temperature, humidity, wind speed, and icing intensity) in the original data set. For example, the value range of temperature T may be from -10°C to 30°C, the value range of humidity H may be from 30% to 100%, and so on.

[0247] When normalizing data, the computer system converts the data to a uniform scale, usually in the interval [0,1]. This process changes the range of the data, but does not change the physical meaning of the data itself. Therefore, in the subsequent steps, the normalized data needs to be restored to the size of the original data to ensure the accuracy and interpretability of the data in practical applications.

[0248] Specifically, assume that the computer system has determined the ice boundaries in the high-risk data subset. These ice boundaries are determined on the standardized data set and represent the boundaries of the abnormal ice area. For example, assume that the standardized ice boundaries are [0.1, 0.8], which means that the standardized temperature T has a value between 0.1 and 0.8. In order to obtain the ice boundaries in the original data set, the computer system needs to restore the standardized boundary values ​​to the size range of the original data.

[0249] Assume that the minimum value of the original temperature T is T_min = -10°C and the maximum value is T_max = 30°C. The computer system can use the Min-Max denormalization formula to restore the normalized boundary value. The denormalization formula is:

[0250] X original =X new ·(X max -X min )+X min ;

[0251] The normalized ice boundary value [0.1, 0.8] is restored to the original data size, and the original ice boundary is obtained as follows:

[0252]

[0253] In this way, the computer system restores the standardized ice boundary to the original data size and obtains the initial range of the abnormal ice area in the high-risk data subset.

[0254] In step S5200, the computer system determines the regional range of the abnormal icing area in the high-risk data subset based on the initial range. This process is intended to further determine the actual geographical range of the abnormal icing area based on the initial range.

[0255] Specifically, the computer system needs to combine the initial range with the geographic coordinate information of the monitoring points to determine the actual geographic range of the abnormal icing area. Assume that each monitoring point has its corresponding geographic coordinates (latitude, longitude), and the data items of each monitoring point include temperature T, humidity H, wind speed F, and icing intensity I. Based on the initial range, the computer system will screen out monitoring points with a temperature T between -6°C and 22°C, and further determine the geographic coordinate range of these monitoring points.

[0256] Assuming that the geographical coordinate range of the selected monitoring points is [(lat1, lon1), (lat2, lon2)], where (lat1, lon1) is the coordinate of the lower left corner of the range, and (lat2, lon2) is the coordinate of the upper right corner of the range. Then, the computer system will determine this geographical coordinate range as the regional range of the abnormal icing area.

[0257] In this way, the computer system successfully determined the regional extent of the abnormal ice area in the high-risk data subset. This process not only restored the standardized ice boundaries to the original data size, but also determined the actual geographical extent of the abnormal ice area based on this.

[0258] For example, suppose the computer system has determined that the ice boundary in the high-risk data subset is [0.1, 0.8]. Through the denormalization formula, the computer system restores these boundary values ​​to the original data size, and the original ice boundary obtained is [-6℃, 22℃]. The computer system further selects monitoring points with temperatures T between -6℃ and 22℃, and determines the geographic coordinate range of these monitoring points to be [(lat1, lon1), (lat2, lon2)]. Assuming that the geographic coordinate range of these monitoring points is [(30.123, 120.456), (30.789, 121.012)], the computer system will determine this geographic coordinate range as the regional range of the abnormal ice area.

[0259] Through the above steps, the computer system can not only determine the initial scope of abnormal icing areas in the high-risk data subset based on the icing boundary, but also further determine the actual geographical scope of these areas. This process not only improves the accuracy and reliability of data processing, but also provides more detailed and accurate abnormal icing area information for subsequent icing intensity monitoring and prediction.

[0260] As an implementation manner, steps S3000 and S4000, extracting the icing intensity characterization array of the high-risk data subset, dividing the high-risk data subset into icing intensity regions based on the icing intensity characterization array, and obtaining the icing boundaries of the high-risk data subset, may include:

[0261] Step S3100: extracting ice intensity characterization arrays of high-risk data subsets at different data scales;

[0262] Step S3200: integrating the icing intensity representation arrays at different data scales to obtain a first target representation array;

[0263] Step S4100: dividing the first target characterization array into icing intensity regions through a restoration mapping component to obtain icing boundaries of a high-risk data subset.

[0264] The computer system extracts the icing intensity characterization arrays of the high-risk data subset in steps S3000 and S4000, and divides the high-risk data subset into icing intensity regions based on these characterization arrays, thereby obtaining the icing boundaries of the high-risk data subset. This process specifically includes steps S3100, S3200, and S4100.

[0265] In step S3100, the computer system extracts the icing intensity characterization arrays of the high-risk data subset at different data scales. This process aims to obtain the icing intensity characteristics at different scales so as to more comprehensively describe the changes in icing intensity.

[0266] Specifically, the computer system first determines the icing intensity data of each monitoring point in the high-risk data subset. Assume that the data items in the high-risk data subset include temperature (T), humidity (H), wind speed (F), and icing intensity (I). The computer system will extract the data item of icing intensity (I) from these data, and further decompose it into icing intensity representation arrays at different levels of data scales. For example, the computer system can group the icing intensity data by time window to form icing intensity representation arrays at different time scales.

[0267] Assume that there are 100 monitoring points in the high-risk data subset, and each monitoring point has icing intensity data at 100 time points. The computer system can group these data into groups of 10 time points to form an array of icing intensity representations at 10 time scales. For example, the icing intensity representation array of the first group of time scales is I1=[I 11 ,I 12 ,...,I 110 ], the second set of time scale ice intensity characterization array is I2 = [I 21 ,I 22 ,...,I 210 ], and so on.

[0268] In step S3200, the computer system integrates the icing intensity representation arrays at different data scales to obtain a first target representation array. This process aims to integrate the icing intensity features at different scales into a unified representation array for subsequent processing.

[0269] Specifically, the computer system can integrate the icing intensity characterization arrays at different data scales in a variety of ways. For example, the average value of the icing intensity characterization array at each time scale can be calculated to form a unified characterization array. Assuming that the icing intensity characterization array of the first set of time scales is I1 and the icing intensity characterization array of the second set of time scales is I2, the computer system can calculate the average value of these arrays:

[0270]

[0271] In this way, the computer system can obtain average values ​​of icing intensity at different time scales and integrate these average values ​​into a first target characterization array.

[0272] In step S4100, the computer system divides the first target representation array into regions of icing intensity by restoring the mapping component to obtain icing boundaries of the high-risk data subset. This process is intended to process the first target representation array through a machine learning model or algorithm to determine the regional boundaries of icing intensity.

[0273] Specifically, the computer system may use a clustering algorithm (such as K-Means) to process the first target representation array. Assume that the first target representation array is M = [MI1, MI2, ..., MI 10 ], the computer system can use the K-Means algorithm to divide these data points into different clusters. For example, suppose the K-Means algorithm divides the data points into two clusters, representing low icing intensity areas and high icing intensity areas. In this way, the computer system can determine the boundaries of the icing intensity areas.

[0274] Assume that the two cluster centers determined by the K-Means algorithm are C1 and C2, where C1 represents the center of the low icing intensity area and C2 represents the center of the high icing intensity area. Then, the computer system can determine the regional boundaries of icing intensity based on the two cluster centers. For example, assuming that the value of C1 is 10 and the value of C2 is 30, then the computer system can determine the area with icing intensity between 10 and 30 as the high icing intensity area.

[0275] Through the above steps, the computer system successfully extracted the icing intensity characterization arrays of the high-risk data subset, and divided the high-risk data subset into icing intensity regions based on these characterization arrays, thereby obtaining the icing boundaries of the high-risk data subset. This process not only improves the accuracy and reliability of data processing, but also provides more detailed and accurate icing intensity region information for subsequent icing intensity monitoring and prediction.

[0276] As an implementation manner, step S4000, based on the icing intensity characterization array, divides the high-risk data subset into icing types to obtain abnormal classification of abnormal icing areas in the high-risk data subset, which may include:

[0277] Step S4000A: taking the icing intensity characterization array corresponding to the minimum data scale as the second target characterization array;

[0278] Step S4000B: Based on the fully connected layer, the second target representation array is divided into ice types to obtain abnormal classification of abnormal ice areas in the high-risk data subset.

[0279] In step S4000, the computer system classifies the high-risk data subset into icing types based on the icing intensity characterization array to obtain abnormal classification of abnormal icing areas in the high-risk data subset. This process specifically includes step S4000A and step S4000B.

[0280] In step S4000A, the computer system uses the icing intensity characterization array corresponding to the minimum data scale as the second target characterization array. This process is intended to select the most detailed level of icing intensity data in order to more accurately classify icing types.

[0281] Specifically, it is assumed that the computer system has extracted the icing intensity characterization arrays at different data scales for the high-risk data subset in step S3000. For example, it is assumed that the computer system has extracted icing intensity characterization arrays at three different time scales: the icing intensity characterization array I1 = [I_11, I_12, ..., I_110] at the first time scale, the icing intensity characterization array I2 = [I_21, I_22, ..., I_210] at the second time scale, and the icing intensity characterization array I3 = [I_31, I_32, ..., I_310] at the third time scale. Among them, the third time scale is the most detailed level.

[0282] The computer system will select the icing intensity characterization array I3 of the third time scale as the second target characterization array. This is because the smallest data scale usually contains more detailed information and can more accurately reflect the changes in icing intensity.

[0283] In step S4000B, the computer system classifies the second target characterization array into ice types based on the fully connected layer to obtain abnormal classification of abnormal ice areas in the high-risk data subset. This process aims to classify the ice intensity data through a neural network model to identify abnormal ice areas. Specifically, the computer system can use a neural network model containing a fully connected layer (FullyConnectedLayer) to perform ice type classification. Assuming that the second target characterization array is I3 = [I_31, I_32, ..., I_310], the computer system uses this array as input and processes it through the fully connected layer.

[0284] Each neuron in the fully connected layer is connected to each element in the input vector and is transformed nonlinearly through weighted summation and activation function. Assume that there are N neurons in the fully connected layer, and each neuron corresponds to a type of ice. The computer system can set a softmax layer as the output layer to convert the output of the fully connected layer into a probability distribution, thereby obtaining the probability of each ice type.

[0285] For example, assuming that the output of the fully connected layer is Z = [z_1, z_2, ..., z_N], then the softmax function can be expressed as:

[0286]

[0287] where exp(z i ) represents z i Through the softmax function, the computer system can obtain the probability distribution of each icing type, thereby determining the abnormal classification of abnormal icing areas.

[0288] In order to train this neural network model, the computer system needs to prepare a labeled data set containing samples of normal icing areas and abnormal icing areas. Assume that each sample in the data set contains an array of icing intensity representations and a corresponding label (normal or abnormal). The computer system can use the cross-entropy loss function to measure the gap between the probability distribution predicted by the model and the actual label.

[0289] By minimizing the cross entropy loss function, the computer system can train a neural network model that can accurately identify abnormal icing areas. During the training process, the computer system can use the gradient descent method or other optimization algorithms to adjust the parameters of the model so that the loss function is gradually reduced.

[0290] Through the above steps, the computer system successfully classified the icing types of the high-risk data subset based on the icing intensity characterization array, and obtained the abnormal classification of abnormal icing areas in the high-risk data subset. This process not only improves the accuracy and reliability of data processing, but also provides more detailed and accurate abnormal icing area information for subsequent icing intensity monitoring and prediction.

[0291] It should be noted that in the embodiments of the present application, if the above-mentioned AI-based real-time monitoring and prediction method for icing intensity is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application can essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present application.

[0292] An embodiment of the present application provides a computer system including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0293] The embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, some or all of the steps in the above method are implemented. The computer-readable storage medium can be transient or non-transient.

[0294] Figure 2 A hardware entity diagram of a computer system provided in an embodiment of the present application is shown in FIG. Figure 2As shown, the hardware entity of the computer system 1000 includes: a processor 1001 and a memory 1002, wherein the memory 1002 stores a computer program that can be run on the processor 1001, and the processor 1001 implements the steps in the method of any of the above embodiments when executing the program.

[0295] The above are only implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A real-time monitoring and prediction method for icing intensity based on AI, characterized in that: The method comprises: The icing intensity parameter characterization array and the icing environment parameter characterization array of the monitoring area in the same monitored period are spliced ​​to obtain the monitored icing association characterization array of the monitored period, and a set of monitored icing association characterization arrays is obtained according to the monitored icing association characterization arrays of multiple different monitored periods; the icing environment parameter characterization array is an implicit representation of the relevant parameters that determine the icing intensity of the monitoring area in the monitored period; the icing intensity parameter characterization array is an implicit representation of the icing intensity of the monitoring area in the monitored period; Determine the distribution coding arrays corresponding to the array elements according to the distribution of the array elements in the monitored icing association representation array set; the array elements include the icing intensity parameter representation arrays and the icing environment parameter representation arrays under the multiple different monitored periods; Integrate each array element in the monitored icing association representation array set with the corresponding distribution coding array to obtain an integrated icing association representation array set; Reasoning is performed based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods.

2. The method according to claim 1, characterized in that The determining, according to the distribution of each array element in the monitored icing association representation array set in the monitored icing association representation array set, respectively, the distribution coding array corresponding to each array element comprises: The monitored icing association representation array set is loaded into the debugged icing monitoring neural network; the network parameter variables of the icing monitoring neural network include target time series distribution information; the debugged icing monitoring neural network is used to infer the icing intensity of the target area in the prediction period; the target time series distribution information is determined when the debugged icing monitoring neural network is debugged; For each array element in the monitored icing association representation array set, a distribution coding array corresponding to the array element is determined in the target timing distribution information according to the distribution of the array element in the monitored icing association representation array set.

3. The method according to claim 2, characterized in that The debugged icing monitoring neural network also includes an intensity reasoning component; the reasoning based on the integrated icing association representation array set to obtain the icing intensity of the monitoring area in one or more prediction periods includes: Performing array mapping on the integrated icing association representation array set to obtain a target array representation; The target array representation is loaded into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in one or more prediction periods.

4. The method according to claim 3, characterized in that The performing array mapping on the integrated icing association representation array set to obtain a target array representation comprises: For each integrated icing association representation array in the integrated icing association representation array set, performing operations on each array element in the integrated icing association representation array to obtain an implicit representation operation value corresponding to the integrated icing association representation array; According to the distribution of the integrated icing association representation arrays in the integrated icing association representation array set, the implicit representation operation values ​​corresponding to each of the integrated icing association representation arrays are arranged in time sequence to obtain a target array representation.

5. The method according to claim 4, characterized in that There are a plurality of monitored icing association representation array sets, and each monitored period corresponding to different monitored icing association representation array sets has one or more different monitored periods; The step of loading the target array representation into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in one or more prediction periods includes: Performing a merging operation on the target array representations respectively obtained from each of the monitored icing association representation array sets to obtain a merged array representation; The merged array representation is loaded into the intensity reasoning component for reasoning to obtain the icing intensity of the monitoring area in multiple different prediction periods.

6. The method according to claim 2, characterized in that The process of obtaining the debugged icing monitoring neural network may include: In the distribution information of the network parameter variables of the icing monitoring neural network that needs to be debugged, the distribution coding arrays corresponding to the array elements in the template icing association representation array set are determined; the template icing association representation array set includes a plurality of template icing association representation arrays under a first monitored period, and the template icing association representation array of the first monitored period includes an icing intensity parameter representation array and an icing environment parameter representation array of the monitoring area under the first monitored period; Integrate each array element in the template icing association characterization array set with the corresponding distribution coding array, and infer the icing intensity of the monitoring area in one or more second monitored periods according to the integration result; According to the icing intensity of the monitoring area in one or more second monitored cycles obtained by reasoning, the timing distribution information of the icing monitoring neural network is optimized until the state of the icing monitoring neural network is stable, and the icing monitoring neural network in the stable state is used as the debugged icing monitoring neural network; the timing distribution information in the stable state is the target timing distribution information.

7. The method according to claim 6, characterized in that The step of integrating each array element in the template icing association characterization array set with the corresponding distribution coding array, and inferring the icing intensity of the monitoring area in one or more second monitored periods according to the integration result includes: Integrate each array element in the template icing association representation array set with the corresponding distribution coding array to obtain a template integrated icing association representation array set; Performing array mapping on the template-integrated icing association representation array set to obtain a template mapping array representation; The template mapping array representation is loaded into an intensity reasoning component of an icing monitoring neural network to infer the icing intensity of the monitoring area in one or more second monitored periods.

8. The method according to claim 1, characterized in that The determining, according to the distribution of each array element in the monitored icing association representation array set in the monitored icing association representation array set, respectively, the distribution coding array corresponding to each array element comprises: For each array element in the monitored icing association representation array set, determining a distribution of the array element in the monitored icing association representation array to which it belongs, and obtaining first distribution information; The distribution of the monitored icing association representation array to which the array element belongs in the monitored icing association representation array set is determined to obtain second distribution information.

9. The method according to claim 8, characterized in that The determining of the distribution of the monitored icing association representation array to which the array element belongs in the monitored icing association representation array set to obtain second distribution information includes: Determine a distribution information embedding operator corresponding to the array element according to the first distribution information; the distribution information embedding operator is used to obtain a distribution coding array corresponding to the array element, and the distribution information embedding operator includes a first variable and a second variable, the first variable is a parameter variable characterizing the distribution of the array element in the monitored icing associated representation array to which it belongs, and the second variable is a parameter variable characterizing the distribution of the monitored icing associated representation array to which the array element belongs in the set of monitored icing associated representation arrays; configuring the first variable in the distribution information embedding operator to be the first distribution information, configuring the second variable in the distribution information embedding operator to be the second distribution information, determining the result of the distribution information embedding operator, and obtaining the distribution code array of the array elements; The step of determining the distribution information embedding operator corresponding to the array element according to the first distribution information comprises: When the first distribution information is an odd position, determining that the distribution information embedding operator corresponding to the array element is a first operator; When the first distribution information is an even number position, determining that the distribution information embedding operator corresponding to the array element is a second operator; The step of integrating each array element in the monitored icing association representation array set with the corresponding distribution coding array to obtain the integrated icing association representation array set comprises: For each monitored icing association representation array in the monitored icing association representation array set, adding a corresponding distribution coding array to each array element of the monitored icing association representation array to obtain an integrated icing association representation array corresponding to the monitored icing association representation array; According to the distribution of the monitored icing association characterization arrays in the monitored icing association characterization arrays, each of the integrated icing association characterization arrays is arranged in time sequence to obtain an integrated icing association characterization array set.

10. A computer system comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, wherein: When the processor executes the program, the steps in the method according to any one of claims 1 to 9 are implemented.

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