A disaster loss prediction method and system based on multidimensional data fusion based on machine learning

Abstract

The present application provides a method and system for disaster loss prediction based on multidimensional data fusion based on machine learning, which relates to the technical field of distribution network disaster loss prediction. The method obtains data from different sources of the distribution network to obtain multidimensional data of the distribution network; performs relevant screening on each disaster variable in the multidimensional data of the distribution network to obtain a key disaster variable set, and determines a key sample data set based on the key disaster variable set; divides the key sample data set to obtain a training data set and a test data set, randomly samples the training data set to obtain multiple training subsets, and then determines the sample discreteness corresponding to each training subset; constructs a disaster loss decision forest based on the sample discreteness corresponding to the test data set and each training subset, and uses the disaster loss decision forest to predict disaster losses for the distribution network. The present application can effectively extract key variables in the multidimensional data and improve the calculation efficiency of the model, so as to improve the reliability of the model when performing disaster loss prediction.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network disaster loss prediction, and more specifically, to a disaster loss prediction method and system based on multi-dimensional data fusion based on machine learning. Background Art

[0002] Disaster loss prediction based on multi-dimensional data fusion of machine learning has important application value in distribution networks, especially in the face of natural disasters (such as typhoons, rainstorms, earthquakes, etc.). It can help predict the degree of damage and recovery of power grid facilities (such as substations, distribution lines, etc.), improve post-disaster recovery efficiency, and optimize emergency repair and scheduling plans.

[0003] However, in practical applications, the heterogeneous and high-dimensional nature of data poses challenges to multidimensional data fusion disaster loss prediction based on machine learning. Meteorological data, power grid equipment data, historical disaster data, and other data sources often come from different sources, with significant variations in format and quality. Furthermore, as the data dimension increases, the computational complexity of the model increases dramatically, requiring significant computing resources and time to train. Therefore, effectively extracting key variables from multidimensional data and improving the model's computational efficiency to enhance its reliability in disaster loss prediction remain challenges facing the industry. Summary of the Invention

[0004] The present application provides a disaster loss prediction method and system based on multidimensional data fusion based on machine learning, which can effectively extract key variables in multidimensional data and improve the model calculation efficiency, thereby improving the reliability of the model when performing disaster loss prediction.

[0005] In a first aspect, the present application provides a disaster loss prediction method based on multi-dimensional data fusion based on machine learning, the prediction method comprising the following steps:

[0006] Acquire data from different sources of the distribution network to obtain multi-dimensional data of the distribution network;

[0007] Performing relevant screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain a key disaster-causing variable set of the distribution network, and determining a key sample data set of the distribution network based on the key disaster-causing variable set;

[0008] Dividing the key sample data set to obtain a training data set and a test data set of the distribution network, randomly sampling the training data set to obtain multiple training subsets, and then determining the sample discreteness corresponding to each training subset;

[0009] A disaster loss decision forest of the distribution network is constructed according to the sample discreteness corresponding to the test data set and each training subset, and disaster loss prediction of the distribution network is performed using the disaster loss decision forest.

[0010] In this embodiment, the disaster-causing variables in the multidimensional data of the distribution network are screened for correlation, and the key disaster-causing variable set of the distribution network is obtained, which specifically includes:

[0011] Determining the variable information entropy of each disaster-causing variable in the multidimensional data of the distribution network;

[0012] Determining the redundancy between each disaster-causing variable in the multidimensional data of the distribution network;

[0013] Selecting a disastrous variable from all disastrous variables in the multidimensional data of the distribution network, determining a variable correlation sequence corresponding to the selected disastrous variable based on the variable information entropy of the selected disastrous variable and the redundancy between the selected disastrous variable and other disastrous variables, and then obtaining a variable correlation sequence corresponding to each disastrous variable in the multidimensional data of the distribution network;

[0014] A set of key disaster-causing variables of the distribution network is screened out based on all variable correlation sequences.

[0015] In this embodiment, the key disaster-causing variable set of the distribution network is screened out based on all variable-related sequences and specifically includes:

[0016] constructing a variable correlation matrix of the distribution network using all variable correlation sequences;

[0017] Screening out all key variable pairs based on the variable correlation matrix;

[0018] Eliminate the correlation of each key variable pair to obtain multiple key disaster-causing variables;

[0019] The set of all key disaster-causing variables is taken as the key disaster-causing variable set of the distribution network.

[0020] In this embodiment, the key sample data set of the distribution network is determined based on the key disaster variable set, which is to eliminate the disaster variables in the distribution network multidimensional data that are not in the key disaster variable set, and then use the distribution network multidimensional data after elimination as the key sample data set of the distribution network.

[0021] In this embodiment, the random sampling of the training data set is random sampling with replacement of the training data set.

[0022] In this embodiment, determining the sample discreteness corresponding to each training subset specifically includes:

[0023] For each training subset, determining the data similarity between each data sample in the training subset;

[0024] Extracting discrete data clusters of the training subset based on all data similarities;

[0025] The sample discreteness corresponding to the training subset is determined by the discrete data cluster, and then the sample discreteness corresponding to each training subset is obtained.

[0026] In this embodiment, constructing the disaster loss decision forest of the distribution network based on the sample discreteness corresponding to the test data set and each training subset specifically includes:

[0027] Construct the disaster loss decision tree corresponding to each training subset according to the sample discreteness corresponding to each training subset;

[0028] Using each disaster loss decision tree to predict the disaster loss of the test data set, and then obtaining the decision coefficient of each disaster loss decision tree;

[0029] A disaster loss decision forest of the distribution network is constructed based on each disaster loss decision tree and the corresponding decision coefficient.

[0030] In this embodiment, constructing a disaster loss decision tree corresponding to each training subset based on the sample discreteness corresponding to each training subset specifically includes:

[0031] For each training subset, the decision order of magnitude is determined according to the sample discreteness corresponding to the training subset;

[0032] Filtering data samples from the training subset by the decision magnitude;

[0033] Based on all the screened data samples, a disaster loss decision tree corresponding to the training subset is constructed, and then a disaster loss decision tree corresponding to each training subset is obtained.

[0034] In this embodiment, using the disaster loss decision forest to perform disaster loss prediction on the distribution network is to input the key sample data set of the distribution network into each disaster loss decision tree of the disaster loss decision forest to perform disaster loss prediction.

[0035] In a second aspect, the present application provides a disaster loss prediction system based on multidimensional data fusion of machine learning, which is used to perform a disaster loss prediction method based on multidimensional data fusion of machine learning. The prediction system includes:

[0036] The data acquisition module is used to obtain data from different sources of the distribution network and obtain multi-dimensional data of the distribution network;

[0037] a key variable determination module, configured to perform relevant screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain a key disaster-causing variable set of the distribution network, and determine a key sample data set of the distribution network based on the key disaster-causing variable set;

[0038] a discreteness determination module, configured to divide the key sample data set to obtain a training data set and a test data set of the distribution network, randomly sample the training data set to obtain a plurality of training subsets, and then determine the sample discreteness corresponding to each training subset;

[0039] A disaster loss prediction module is used to construct a disaster loss decision forest for the distribution network based on the sample discreteness corresponding to the test data set and each training subset, and use the disaster loss decision forest to predict disaster losses for the distribution network.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] By acquiring data from different sources of the distribution network, multidimensional data of the distribution network is obtained; each disaster-causing variable in the multidimensional data of the distribution network is screened to obtain a key disaster-causing variable set of the distribution network, and a key sample data set of the distribution network is determined based on the key disaster variable set; the key sample data set is divided to obtain a training data set and a test data set of the distribution network, the training data set is randomly sampled to obtain multiple training subsets, and the sample discreteness corresponding to each training subset is determined; a disaster loss decision forest of the distribution network is constructed based on the sample discreteness corresponding to the test data set and each training subset, and the disaster loss decision forest is used to predict disaster losses for the distribution network.

[0042] It can be seen that in this application, first, by performing relevant screening on disaster-causing variables in the multidimensional data of the distribution network, redundant disaster-causing variables can be removed, the dimension of the data can be significantly reduced, thereby reducing the computational complexity and helping to reduce the risk of model overfitting; then, through random sampling, the robustness and adaptability of the model can be enhanced, so that it can make reliable disaster loss predictions under different scenarios and disaster loss patterns, and by calculating the sample discreteness of the training subset, the degree of data discreteness of the training subset can be measured, which helps to assign different weights to different training subsets and improve the accuracy of the model's disaster loss prediction; finally, by integrating multiple disaster loss decision trees through the disaster loss decision forest, the deviation and overfitting problems that may occur in a single disaster loss decision tree can be reduced, the generalization ability of the model can be enhanced, and the reliability of disaster loss prediction can be improved.

[0043] In summary, the technical solution adopted in this application can effectively extract key variables from multidimensional data and improve the calculation efficiency of the model, so as to improve the reliability of the model in disaster loss prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0045] Figure 1 This is a flowchart of a disaster loss prediction method based on multidimensional data fusion based on machine learning provided by this application;

[0046] Figure 2 is an exemplary flow chart for determining the sample discreteness corresponding to each training subset provided in this application;

[0047] Figure 3 This is an exemplary flow chart for constructing a disaster loss decision forest for a distribution network according to the present application;

[0048] Figure 4 This is a module structure diagram of the disaster loss prediction system based on multi-dimensional data fusion based on machine learning provided by this application. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the examples and accompanying drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0050] Example 1

[0051] In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG, this figure is an exemplary flow chart of a disaster loss prediction method based on multi-dimensional data fusion based on machine learning according to this embodiment of the present application. The prediction method includes the following steps:

[0052] In step S1, data from different sources of the distribution network are acquired to obtain multi-dimensional data of the distribution network.

[0053] It should be noted that in this application, data from different sources of the distribution network include distribution line data, meteorological data and geographical environment data. The data from different sources of the distribution network can be integrated to obtain multi-dimensional data of the distribution network. The multi-dimensional data of the distribution network is a multi-dimensional data sample set related to the distribution network, wherein the data samples are composed of multi-dimensional disaster variables. For example, the disaster variables contained in a data sample may include the service life of the distribution line, historical fault information of the distribution line, the slope of the location of the distribution network equipment, the surface type of the location of the distribution network equipment, the maximum wind speed, the minimum wind speed, the average wind speed, the rainfall, etc., which are not limited here.

[0054] In step S2, relevant screening is performed on each disaster variable in the multidimensional data of the distribution network to obtain a key disaster variable set of the distribution network, and a key sample data set of the distribution network is determined based on the key disaster variable set.

[0055] In this embodiment, the following method is used to perform relevant screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain the key disaster-causing variable set of the distribution network, namely:

[0056] Determining the variable information entropy of each disaster-causing variable in the multidimensional data of the distribution network;

[0057] Determining the redundancy between each disaster-causing variable in the multidimensional data of the distribution network;

[0058] Selecting a disastrous variable from all disastrous variables in the multidimensional data of the distribution network, determining a variable correlation sequence corresponding to the selected disastrous variable based on the variable information entropy of the selected disastrous variable and the redundancy between the selected disastrous variable and other disastrous variables, and then obtaining a variable correlation sequence corresponding to each disastrous variable in the multidimensional data of the distribution network;

[0059] A set of key disaster-causing variables of the distribution network is screened out based on all variable correlation sequences.

[0060] In specific implementation, first, the variable information entropy of each disaster variable in the distribution network multidimensional data can be determined by the existing information entropy calculation method. The variable information entropy is used to measure the amount of information contained in the disaster variable. The larger the variable information entropy, the greater the contribution of the disaster variable to disaster loss prediction; then, the redundancy between each disaster variable in the distribution network multidimensional data can be determined, where the redundancy represents the information duplication between the disaster variables. Disaster variables with too high redundancy will lead to model overfitting. The Pearson correlation coefficient between the disaster variables can be used as the redundancy between the disaster variables.

[0061] In addition, in a specific implementation, a disaster variable can be selected from all the disaster variables in the multidimensional data of the distribution network as the selected disaster variable, so that the variable correlation sequence corresponding to the selected disaster variable can be determined according to the variable information entropy of the selected disaster variable and the redundancy between the selected disaster variable and other disaster variables. The variable correlation sequence is a sequence composed of multiple variable correlations. The variable correlation is an indicator used to measure the size of the correlation between disaster variables. In actual implementation, the variable correlation can be determined by the following method:

[0062]

[0063] Among them, α(a, b) represents the variable correlation between disaster variables a and disaster variables b, β(a, b) represents the redundancy between disaster variables a and disaster variables b, γ(a) represents the variable information entropy of disaster variable a, and γ(b) represents the variable information entropy of disaster variable b. Through the above method, the variable correlation sequence corresponding to the selected disaster variable can be obtained, thereby obtaining the variable correlation sequence corresponding to each disaster variable in the multidimensional data of the distribution network.

[0064] In this embodiment, the following method can be used to select the key disaster-causing variable set of the distribution network based on all variable correlation sequences, namely:

[0065] constructing a variable correlation matrix of the distribution network using all variable correlation sequences;

[0066] Screening out all key variable pairs based on the variable correlation matrix;

[0067] Eliminate the correlation of each key variable pair to obtain multiple key disaster-causing variables;

[0068] The set of all key disaster-causing variables is taken as the key disaster-causing variable set of the distribution network.

[0069] In specific implementation, first, all variable correlation sequences can be used to construct a variable correlation matrix of the distribution network, wherein the variable correlation matrix is a correlation matrix composed of variable correlations between all disaster-causing variables. The variable correlations in all variable correlation sequences can be arranged according to the corresponding relationship between the disaster-causing variables to obtain the variable correlation matrix of the distribution network; then, all key variable pairs can be screened out based on the variable correlation matrix, wherein the key variable pair consists of a pair of disaster-causing variables, and a variable correlation threshold can be set based on historical experiments and data analysis. The variable correlation threshold set in this application is 0.78. Other values can also be set in actual implementation, and are not limited here. Therefore, the disaster-causing variables corresponding to the variable correlations greater than the variable correlation threshold in the variable correlation matrix can be used as key variable pairs. All key variable pairs can be obtained in the above manner.

[0070] In addition, in the specific implementation, each key variable pair can be correlated and eliminated to obtain multiple key disaster variables, where the key disaster variable represents the disaster variable that has a greater contribution to the model prediction. For each key variable pair, the variable correlation mean between the two disaster variables in the key variable pair and the other disaster variables can be calculated based on the variable correlation matrix, so that the disaster variables with larger variable correlation mean are eliminated, and the remaining disaster variables are used as key disaster variables. In the above method, multiple key disaster variables can be obtained; finally, the set of all key disaster variables can be used as the key disaster variable set of the distribution network.

[0071] In this embodiment, the key sample data set of the distribution network is determined based on the key disaster variable set, which is to eliminate the disaster variables in the distribution network multidimensional data that are not in the key disaster variable set, and then use the distribution network multidimensional data after elimination as the key sample data set of the distribution network.

[0072] It should be noted that the disaster variables that do not belong to the key disaster variable set are eliminated from the multidimensional data of the distribution network. The purpose of elimination is to remove redundant data and improve the accuracy of the model. Non-key disaster variables do not contribute significantly to disaster loss prediction, or their information has been covered by other key disaster variables. Eliminating non-key disaster variables can reduce the complexity of the model and avoid overfitting of the machine learning model.

[0073] In addition, it should be noted that by performing relevant screening on the disaster-causing variables in the multidimensional data of the distribution network, redundant disaster-causing variables can be removed, which can significantly reduce the dimension of the data, thereby reducing the computational complexity and helping to reduce the risk of model overfitting.

[0074] In step S3, the key sample data set is divided to obtain a training data set and a test data set of the distribution network, the training data set is randomly sampled to obtain multiple training subsets, and then the sample discreteness corresponding to each training subset is determined.

[0075] In specific implementation, the key sample data set can be divided according to a pre-set division ratio to obtain a training data set and a test data set of the distribution network. For example, 70% of the data samples in the key sample data set can be divided into a training data set, and 30% of the data samples in the key sample data set can be divided into a test data set. I will not go into details here.

[0076] In this embodiment, random sampling of the training data set is random sampling with replacement of the training data set to obtain multiple training subsets; it should be noted that the number of data samples contained in each training subset is the same. Random sampling with replacement can increase the diversity of the data, making the model more adaptable to noise and changes in the data and enhancing its stability.

[0077] Preferably, in this embodiment, reference Figure 2 As shown in FIG, this figure is an exemplary flow chart of determining the sample discreteness corresponding to each training subset in an embodiment of the present application. In this embodiment, determining the sample discreteness corresponding to each training subset can be specifically implemented by the following steps:

[0078] First, in step S31, for each training subset, the data similarity between each data sample in the training subset is determined;

[0079] Then, in step S32, discrete data clusters of the training subset are extracted based on all data similarities;

[0080] Finally, in step S33, the sample discreteness corresponding to the training subset is determined by the discrete data cluster, and then the sample discreteness corresponding to each training subset is obtained.

[0081] In specific implementation, first, for each training subset, the data similarity between each data sample in the training subset can be calculated by cosine similarity; then, the discrete data cluster of the training subset can be extracted based on all data similarities, wherein the discrete data cluster is a data cluster containing multiple discrete data, and the similarity threshold can be set according to historical experience. The data similarity mean corresponding to each data sample in the training subset can be calculated, so that the data sample with the data similarity mean less than the similarity threshold is regarded as discrete data. Through the above method, all the discrete data of the training subset can be obtained, and the data cluster composed of all discrete data is regarded as the discrete data cluster of the training subset; finally, the sample discreteness corresponding to the training subset can be determined through the discrete data cluster, wherein the sample discreteness represents the proportion of discrete data in the training subset, and the ratio of the number of discrete data in the discrete data cluster to the number of data samples in the training subset can be regarded as the sample discreteness corresponding to the training subset. Through the above method, the sample discreteness corresponding to each training subset can be obtained.

[0082] It should be noted that random sampling can enhance the robustness and adaptability of the model, enabling it to make reliable disaster loss predictions in different scenarios and disaster loss patterns. By calculating the sample discreteness of the training subset, the degree of data discreteness of the training subset can be measured, which helps to assign different weights to different training subsets and improve the accuracy of the model's disaster loss prediction.

[0083] In step S4, a disaster loss decision forest of the distribution network is constructed based on the sample discreteness corresponding to the test data set and each training subset, and the disaster loss decision forest is used to predict the disaster loss of the distribution network.

[0084] Preferably, in this embodiment, reference Figure 3 As shown in FIG, this figure is an exemplary flow chart of constructing a disaster loss decision forest for a distribution network in an embodiment of the present application. In this embodiment, constructing the disaster loss decision forest for the distribution network based on the sample discreteness corresponding to the test data set and each training subset can be specifically implemented by the following steps:

[0085] First, in step S41, a disaster loss decision tree corresponding to each training subset is constructed based on the sample discreteness corresponding to each training subset;

[0086] Then, in step S42, each disaster loss decision tree is used to perform disaster loss prediction on the test data set, thereby obtaining a decision coefficient of each disaster loss decision tree;

[0087] Finally, in step S43, a disaster loss decision forest of the distribution network is constructed based on each disaster loss decision tree and the corresponding decision coefficient.

[0088] In specific implementation, first, a disaster loss decision tree corresponding to each training subset can be constructed based on the sample discreteness corresponding to each training subset; then, each disaster loss decision tree can be used to predict the disaster loss of the test data set, and then the decision coefficient of each disaster loss decision tree can be obtained, wherein the decision coefficient is used to represent the allocation weight of the corresponding disaster loss decision tree in the model, and the test data set can be input into each disaster loss decision tree for disaster loss prediction. During the disaster loss prediction process, each disaster loss decision tree will classify each data sample in the test data set to determine its disaster loss risk level or specific disaster loss value. The disaster loss decision tree is split according to the characteristics of the data sample until the leaf node obtains the final prediction result, thereby determining the accuracy of the disaster loss decision tree prediction result. The accuracy can be used as the decision coefficient of the disaster loss decision tree. The decision coefficient of each disaster loss decision tree can be obtained in the above manner; finally, a disaster loss decision forest for the distribution network can be constructed based on each disaster loss decision tree and the corresponding decision coefficient, that is, each decision coefficient can be used as the construction weight of the corresponding disaster loss decision tree to construct the disaster loss decision forest for the distribution network. It should be noted that the disaster loss decision forest used in this application is a random forest model based on machine learning.

[0089] In this embodiment, the following method can be used to construct the disaster loss decision tree corresponding to each training subset based on the sample discreteness corresponding to each training subset, namely:

[0090] For each training subset, the decision order of magnitude is determined according to the sample discreteness corresponding to the training subset;

[0091] Filtering data samples from the training subset by the decision magnitude;

[0092] Based on all the screened data samples, a disaster loss decision tree corresponding to the training subset is constructed, and then a disaster loss decision tree corresponding to each training subset is obtained.

[0093] In the specific implementation, first, for each training subset, the decision order of magnitude can be determined according to the sample discreteness corresponding to the training subset, wherein the decision order of magnitude represents the number of data samples that need to be screened, and the total number of data samples in the training subset can be obtained, thereby rounding down the product of the sample discreteness and the total number of data samples in the training subset, and taking the result as the decision order of magnitude of the training subset; then, the corresponding number of data samples can be screened out in the training subset according to the decision order of magnitude; finally, a disaster loss decision tree corresponding to the training subset can be constructed based on all the screened data samples, that is, a decision tree is constructed for all the screened data samples according to the existing decision tree algorithm, thereby obtaining a disaster loss decision tree corresponding to the training subset. The specific process will not be repeated here, and the disaster loss decision tree corresponding to each training subset can be obtained in the above manner.

[0094] In this embodiment, using the disaster loss decision forest to predict the distribution network damage is to input the key sample data set of the distribution network into each disaster loss decision tree of the disaster loss decision forest for disaster loss prediction; in specific implementation, each data sample in the key sample data set is input into each disaster loss decision tree in the disaster loss decision forest respectively. In each disaster loss decision tree, the input data sample is classified according to the path from the root node to the leaf node. The disaster loss decision tree calculates the degree of damage corresponding to the data sample based on the path (such as the loss of electricity, the degree of equipment damage, etc.). After obtaining the prediction result of each decision tree, the weighted average method is used to perform weighted summation on the prediction results of each disaster loss decision tree, wherein the decision coefficient corresponding to each disaster loss decision tree is used as the corresponding weight in the weighted average method, thereby obtaining a final numerical prediction (such as the loss of electricity or the degree of equipment damage).

[0095] It should be noted that by integrating multiple disaster loss decision trees through the disaster loss decision forest, the deviation and overfitting problems that may occur in a single disaster loss decision tree can be reduced, the generalization ability of the model can be enhanced, and the reliability of disaster loss prediction can be improved.

[0096] It can be seen that in this application, first, by performing relevant screening on disaster-causing variables in the multidimensional data of the distribution network, redundant disaster-causing variables can be removed, the dimension of the data can be significantly reduced, thereby reducing the computational complexity and helping to reduce the risk of model overfitting; then, through random sampling, the robustness and adaptability of the model can be enhanced, so that it can make reliable disaster loss predictions under different scenarios and disaster loss patterns, and by calculating the sample discreteness of the training subset, the degree of data discreteness of the training subset can be measured, which helps to assign different weights to different training subsets and improve the accuracy of the model's disaster loss prediction; finally, by integrating multiple disaster loss decision trees through the disaster loss decision forest, the deviation and overfitting problems that may occur in a single disaster loss decision tree can be reduced, the generalization ability of the model can be enhanced, and the reliability of disaster loss prediction can be improved.

[0097] In summary, the technical solution adopted in this application can effectively extract key variables from multidimensional data and improve the calculation efficiency of the model, so as to improve the reliability of the model in disaster loss prediction.

[0098] Example 2

[0099] This application provides a disaster loss prediction system based on multidimensional data fusion based on machine learning. Figure 4 As shown, this figure is a schematic diagram of the prediction system shown in this embodiment of the present application, and the prediction system includes:

[0100] The data acquisition module 100 is used to acquire data from different sources of the distribution network to obtain multi-dimensional data of the distribution network;

[0101] A key variable determination module 200 is configured to perform correlation screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain a key disaster-causing variable set of the distribution network, and determine a key sample data set of the distribution network based on the key disaster-causing variable set;

[0102] A dispersion determination module 300 is configured to divide the key sample data set to obtain a training data set and a test data set of the distribution network, randomly sample the training data set to obtain multiple training subsets, and then determine the sample dispersion corresponding to each training subset;

[0103] The damage prediction module 400 is used to construct a damage decision forest for the distribution network based on the sample discreteness corresponding to the test data set and each training subset, and use the damage decision forest to predict damage to the distribution network.

[0104] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A disaster loss prediction method based on multidimensional data fusion based on machine learning, characterized in that: The prediction method comprises the following steps: Acquire data from different sources of the distribution network to obtain multi-dimensional data of the distribution network; Performing relevant screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain a key disaster-causing variable set of the distribution network, and determining a key sample data set of the distribution network based on the key disaster-causing variable set; Dividing the key sample data set to obtain a training data set and a test data set of the distribution network, randomly sampling the training data set to obtain multiple training subsets, and then determining the sample discreteness corresponding to each training subset; Constructing a disaster loss decision forest for the distribution network based on the sample discreteness corresponding to the test data set and each training subset, and using the disaster loss decision forest to predict disaster losses for the distribution network; Determining the sample discreteness corresponding to each training subset specifically includes: For each training subset, determining the data similarity between each data sample in the training subset; Extracting discrete data clusters of the training subset based on all data similarities; Determining the sample discreteness corresponding to the training subset through the discrete data cluster, and then obtaining the sample discreteness corresponding to each training subset; Constructing the disaster loss decision forest of the distribution network based on the sample discreteness corresponding to the test data set and each training subset specifically includes: Construct the disaster loss decision tree corresponding to each training subset according to the sample discreteness corresponding to each training subset; Using each disaster loss decision tree to predict the disaster loss of the test data set, and then obtaining the decision coefficient of each disaster loss decision tree; A disaster loss decision forest of the distribution network is constructed based on each disaster loss decision tree and the corresponding decision coefficient.

2. The method for disaster loss prediction based on multidimensional data fusion based on machine learning according to claim 1, characterized in that: The disaster-causing variables in the multidimensional data of the distribution network are screened to obtain a set of key disaster-causing variables of the distribution network, specifically including: Determining the variable information entropy of each disaster-causing variable in the multidimensional data of the distribution network; Determining the redundancy between each disaster-causing variable in the multidimensional data of the distribution network; Selecting a disastrous variable from all disastrous variables in the multidimensional data of the distribution network, determining a variable correlation sequence corresponding to the selected disastrous variable based on the variable information entropy of the selected disastrous variable and the redundancy between the selected disastrous variable and other disastrous variables, and then obtaining a variable correlation sequence corresponding to each disastrous variable in the multidimensional data of the distribution network; A set of key disaster-causing variables of the distribution network is screened out based on all variable correlation sequences.

3. The method for disaster loss prediction based on multidimensional data fusion based on machine learning as claimed in claim 2, characterized in that: The key disaster-causing variable set of the distribution network is screened based on all variable correlation sequences, including: constructing a variable correlation matrix of the distribution network using all variable correlation sequences; Screening out all key variable pairs based on the variable correlation matrix; Eliminate the correlation of each key variable pair to obtain multiple key disaster-causing variables; The set of all key disaster-causing variables is taken as the key disaster-causing variable set of the distribution network.

4. The method for disaster loss prediction based on multidimensional data fusion based on machine learning according to claim 1, characterized in that: Determining the key sample data set of the distribution network based on the key disaster variable set is to eliminate the disaster variables in the distribution network multidimensional data that are not in the key disaster variable set, and then use the distribution network multidimensional data after elimination as the key sample data set of the distribution network.

5. The method for disaster loss prediction based on multidimensional data fusion based on machine learning according to claim 1, characterized in that: The random sampling of the training data set is a random sampling with replacement of the training data set.

6. The method for disaster loss prediction based on multidimensional data fusion based on machine learning as claimed in claim 1, characterized in that: Constructing the disaster loss decision tree corresponding to each training subset based on the sample discreteness corresponding to each training subset specifically includes: For each training subset, the decision order of magnitude is determined according to the sample discreteness corresponding to the training subset; Filtering data samples from the training subset by the decision magnitude; Based on all the screened data samples, a disaster loss decision tree corresponding to the training subset is constructed, and then a disaster loss decision tree corresponding to each training subset is obtained.

7. The method for disaster loss prediction based on multidimensional data fusion based on machine learning as claimed in claim 1, characterized in that: Using the disaster loss decision forest to predict the damage to the distribution network is to input the key sample data set of the distribution network into each disaster loss decision tree of the disaster loss decision forest to perform disaster loss prediction.

8. A disaster loss prediction system based on multidimensional data fusion of machine learning, used to execute the disaster loss prediction method based on multidimensional data fusion of machine learning according to any one of claims 1 to 7, characterized in that: The prediction system includes: The data acquisition module is used to obtain data from different sources of the distribution network and obtain multi-dimensional data of the distribution network; a key variable determination module, configured to perform relevant screening on each disaster-causing variable in the multidimensional data of the distribution network to obtain a key disaster-causing variable set of the distribution network, and determine a key sample data set of the distribution network based on the key disaster-causing variable set; a discreteness determination module, configured to divide the key sample data set to obtain a training data set and a test data set of the distribution network, randomly sample the training data set to obtain a plurality of training subsets, and then determine the sample discreteness corresponding to each training subset; A disaster loss prediction module is used to construct a disaster loss decision forest for the distribution network based on the sample discreteness corresponding to the test data set and each training subset, and use the disaster loss decision forest to predict disaster losses for the distribution network.

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