Distribution box and distribution box power monitoring system thereof

Abstract

The invention relates to the technical field of power monitoring, in particular to a distribution box and a distribution box power monitoring system thereof. The system comprises a data acquisition module used for acquiring parameters to form a feature row and a feature group, and a reasonability judgment module used for determining the final reasonability of the feature group according to the similarity of the voltage fluctuation of the feature group after combining the influence of the environmental parameters on the voltage fluctuation; the feature matrix construction module is used for determining the overall rationality according to the power difference between the feature group and the corresponding feature row and the final rationality, obtaining unique future power based on the overall rationality and future power weighting, and determining a feature matrix; and determining a regular contribution degree according to the overall rationality and the future power, inputting the feature matrix to obtain a decision tree and a regular term, and performing subsequent iteration to obtain the future power at the current moment so as to complete power monitoring. The energy utilization efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power monitoring, and in particular to a distribution box and a power monitoring system for the distribution box. Background Art

[0002] Predicting the future power of distribution boxes for power dispatch and load management is an important part of the operation of the power system. Through accurate power prediction, the distribution network can reasonably allocate power resources in different time periods, avoid overload, waste and system failures, thereby improving energy utilization efficiency, ensuring power supply safety, and reducing operating costs.

[0003] Existing methods generally use XGBoost to train a model based on a large amount of historical power data to predict the data of the distribution box within one minute in the future. Specifically, first, all features related to future power need to be selected, and a large number of them are converted into feature matrices through feature engineering as model input. Then XGBoost fits the residual of the current model by gradually building decision trees. The training goal of each tree is to optimize the error of the previous tree. At the same time, during the optimization process, in addition to minimizing the loss of training data, a regularization term is added to prevent the model from overfitting. The model is continuously adjusted through the gradient boosting method, and finally the prediction results of each tree are weighted and summed to improve the overall performance of the model.

[0004] In the existing algorithms, the collection of a large amount of historical data may result in many different future powers for the same feature group. At this time, it is difficult to obtain the future power situation that this set of features actually corresponds to, resulting in poor prediction ability. In addition, the regularization term of the decision tree generated in each iteration is closely related to the number of leaf nodes and the weight of the leaf nodes. However, if the weight of unavailable leaf nodes due to some emergencies such as short circuits and broken circuits is learned in this decision tree, XGBoost may regard these noise features as useful prediction information. At this time, the regularization term of this decision tree cannot prevent overfitting well. Summary of the invention

[0005] In order to solve the technical problem of poor prediction ability, the present application provides a distribution box and a distribution box power monitoring system thereof, and the technical solutions adopted are as follows: In the first aspect, the present application proposes a distribution box power monitoring system, which includes the following modules: The data acquisition module is used to collect parameters and future power through different sensors, take the parameters collected at the preset time as the feature group, and use the feature group and the future power as feature rows to construct an initial matrix. The parameters include power, ambient temperature, ambient humidity, ambient wind speed and voltage fluctuation; The rationality judgment module is used to obtain the rationality of a feature group according to the similarity of all power and voltage fluctuations in a feature group; calculate the influence of environmental parameters on voltage fluctuations according to the correlation between all environmental parameters and voltage fluctuations combined with the correlation between environmental parameters and voltage fluctuations in a feature group; obtain the final rationality of the feature group according to the rationality of the feature group and the influence of environmental parameters on voltage fluctuations; The feature matrix construction module is used to obtain the overall rationality of the feature row according to the difference between the power of each feature group and the future power of the corresponding feature row and the final rationality of the feature group; weight the future power of the feature row with the overall rationality of the feature row as the weight to obtain the unique future power corresponding to the feature group; determine the feature row of each feature group based on the unique future power to construct the feature matrix; The power monitoring module is used to obtain the regular contribution of the feature group based on the mean of the overall rationality of each feature group and the mean of the future power; the feature matrix is ​​used as input to obtain the decision tree, and the regular term of the decision tree is obtained by weighting the regular contribution; the prediction value and future power are obtained based on the decision tree, and the decision tree is iterated to obtain the prediction model, and the feature group corresponding to the current moment is used as the input of the prediction model to obtain the future power at the current moment, thereby completing power monitoring.

[0006] In the above scheme, compared with the problem that many different future powers may appear in the same feature group, it is difficult to obtain the future power that this group of features truly corresponds to. The present application starts from the rationality of the features, gradually obtains the availability of all future powers corresponding to this group of features, and uses the availability for weighting to obtain the future power that this group of features truly corresponds to, which solves the problem that one feature group corresponds to multiple future powers in the existing method, making it difficult to obtain future power and resulting in poor prediction effect; compared with the general XGBoost, the regularization term of the tree generated in each iteration process is greatly affected by the unavailable leaf node weights due to some emergencies such as short circuits and broken circuits. The present application weights the regularization contribution by the leaf node weight, so that the feature group of the regularization contribution can be better utilized, better prevent overfitting, and obtain more accurate prediction results.

[0007] In one embodiment, the method for constructing the initial matrix is: The preset collection time is taken as an interval, and the power collected in the next minute of the interval is taken as the future power corresponding to the interval; all parameter data collected in each interval are taken as feature groups, each feature group and future power are taken as feature rows, and the feature rows are taken as a row of the matrix. Multiple feature groups and their corresponding future powers are collected to construct the initial matrix.

[0008] In one embodiment, the method for obtaining the rationality of a feature group according to the similarity of all power and voltage fluctuations of a feature group is: Extract all powers of each feature group as a power feature sequence, and extract all voltage fluctuations of each feature group as a voltage fluctuation feature sequence; The cosine similarity of the power feature sequence and the voltage fluctuation feature sequence of each feature group is calculated, all cosine similarities are normalized by linear normalization after being negative, and each normalized value is used as the rationality of each feature group.

[0009] In one embodiment, the method for calculating the influence of environmental parameters on voltage fluctuation based on the correlation between all environmental parameters and voltage fluctuation combined with the correlation between environmental parameters and voltage fluctuation in a feature group is: All collected ambient temperatures, ambient humidity, ambient wind speeds and voltage fluctuations are sorted in time sequence to obtain an overall temperature sequence, an overall humidity sequence, an overall wind speed sequence and an overall voltage fluctuation sequence; Calculate the correlation between the overall voltage fluctuation sequence and the overall temperature sequence, the overall humidity sequence, and the overall wind speed sequence as the influence of temperature on voltage fluctuation, the influence of humidity on voltage fluctuation, and the influence of wind speed on voltage fluctuation; All ambient temperatures of a feature group are sorted in time series to obtain a temperature feature sequence, all ambient humidity of a feature group are sorted in time series to obtain a humidity feature sequence, and all ambient wind speeds of a feature group are sorted in time series to obtain a wind speed feature sequence; Taking the influence of environmental characteristics on voltage fluctuation as the weight, the correlation between environmental characteristics and voltage fluctuation in each feature group is weighted to obtain the influence of environmental parameters on voltage fluctuation.

[0010] In one embodiment, the expression for the influence of the environmental parameters on the voltage fluctuation is: , Indicates the ambient temperature, Indicates the ambient humidity. Indicates the ambient wind speed, represents the overall sequence corresponding to the Kth parameter in the i-th feature group, represents the overall voltage fluctuation sequence in the i-th feature group, represents the voltage fluctuation feature sequence in the i-th feature group, represents the feature sequence corresponding to the Kth parameter in the i-th feature group, represents the correlation between two series. Represents the influence of environmental parameters in the i-th feature group on voltage fluctuation.

[0011] In one embodiment, the method for obtaining the final rationality of the feature group according to the rationality of the feature group and the influence of the environmental parameters on the voltage fluctuation is: , represents the influence of environmental parameters in the i-th feature group on voltage fluctuation, Indicates the rationality of the i-th feature group, Indicates the final rationality of the i-th feature group.

[0012] In one embodiment, the method of obtaining the overall rationality of the feature row based on the difference between the power of each feature group and the future power of the corresponding feature row and the final rationality of the feature group is: , Indicates power, Indicates The power feature sequence in the feature group corresponding to the feature rows, Indicates The average power of the power feature sequence in the feature group corresponding to the feature rows, Indicates The final rationality of the feature group corresponding to the feature rows, represents the normalization function, Indicates The future power of the characteristic line, Indicates The overall rationality of the feature line.

[0013] In one embodiment, the method of weighting the future power of the feature row with the overall rationality of the feature row as the weight to obtain the unique future power corresponding to the feature group, and determining the feature row of each feature group based on the unique future power to construct the feature matrix is: Count all feature rows corresponding to a feature group, take the ratio of the overall rationality of each feature row to the sum of the overall rationality of all feature rows corresponding to the feature group as the weight of each feature row, and take the product of the weight of all feature rows and the future power as the unique future power of the feature group; Each feature group and unique future power are taken as a row of the matrix, all feature groups and their corresponding unique future powers are counted, and the feature matrix is ​​obtained by sorting them from top to bottom in chronological order.

[0014] In one embodiment, the method for obtaining the regularization item of the decision tree by weighting the regularization contribution is: , and To preset hyperparameters, represents the number of leaf nodes of the decision tree obtained by the kth iteration, represents the number of feature groups in the dth leaf node, represents the regular contribution of the u-th feature group in the d-th leaf node, represents the unique future power of the u-th feature group in the d-th leaf node, Represents the regularization term of the decision tree obtained in the kth iteration.

[0015] In the second aspect, an embodiment of the present application also provides a distribution box, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of a distribution box power monitoring system described in any one of the above items are implemented.

[0016] The beneficial effects of this application are: Compared with the problem that many different future powers may appear in the same feature group, and it is difficult to obtain the future power that this group of features truly corresponds to, the present application starts from the rationality of the features, gradually obtains the availability of all future powers corresponding to this group of features, and uses the availability for weighting to obtain the future power that this group of features truly corresponds to, which solves the problem that one feature group corresponds to multiple future powers in the existing method, making it difficult to obtain future power and resulting in poor prediction effect; compared with the general XGBoost, the regularization term of the generated tree in each iteration process is greatly affected by the unavailable leaf node weights due to some emergencies such as short circuits and open circuits. The present application weights the regularization contribution by the leaf node weight, so that the feature group of the regularization contribution can be better utilized, better prevent overfitting, and obtain more accurate prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart of a power monitoring system for a distribution box provided in one embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a distribution box and its distribution box power monitoring system proposed in accordance with the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0021] A distribution box and its distribution box power monitoring system embodiment: The specific scheme of a power distribution box power monitoring system provided by the present application is described in detail below with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flow chart of a power distribution box power monitoring system provided by an embodiment of the present application, the system includes the following modules: The data acquisition module uses the XGBoost model in this application to predict the power of the distribution box within the next minute. The power, ambient temperature, ambient humidity, and ambient wind speed are collected through smart meters, temperature sensors, humidity sensors, and wind speed sensors respectively. The maximum voltage and minimum voltage within one minute are collected through smart meters, and the difference between the maximum voltage and the minimum voltage is used as the voltage fluctuation. The power, ambient temperature, ambient humidity, ambient wind speed, and voltage fluctuation are used as the collected parameters.

[0023] The preset minutes are taken as an interval, and the time corresponding to the interval is repeatable. The power collected in the next minute of each interval is taken as the future power corresponding to the interval. All parameter data collected in each interval are taken as feature groups, each feature group and future power are taken as feature rows, and the feature rows are taken as a row of the matrix. Multiple feature groups and their corresponding future powers are collected to construct the initial matrix.

[0024] In this embodiment, parameter data is collected within 30 days, where every 10 minutes is taken as an interval, the parameter data from the start of collection to the 10th minute is taken as a feature group, the feature group and the power at the 11th minute are taken as the first row of the initial matrix, the parameter data from the 2nd minute to the 11th minute is taken as a feature group, the feature group and the power at the 12th minute are taken as the second row of the initial matrix, and so on.

[0025] At this point, the initial matrix is ​​obtained.

[0026] In the rationality judgment module, each set of features in the initial matrix obtained by the data acquisition module corresponds to a future power. When collecting data, different rows of the matrix may have the same feature group, and the same feature group may correspond to different future powers. That is, the same feature group may correspond to multiple different future powers, so this application needs to screen out the most suitable future power.

[0027] Since the subsequent application needs to analyze the regular contribution of each feature group through a decision tree, and short circuits and disconnections may cause problems in the collected feature group data, resulting in analysis deviations, it is first necessary to calculate whether each feature group is reasonable.

[0028] When the power in the distribution network increases, the voltage fluctuation decreases; when the power decreases, the voltage fluctuation increases. Therefore, for each feature group of the initial matrix, the more negative correlation there is between power and voltage fluctuation, the higher the rationality of the feature group.

[0029] All powers of each feature group are extracted as a power feature sequence, and all voltage fluctuations of each feature group are extracted as a voltage fluctuation feature sequence, wherein the power and voltage fluctuations in the power feature sequence and the voltage fluctuation feature sequence are respectively sorted in time sequence.

[0030] The cosine similarity of the power feature sequence and the voltage fluctuation feature sequence is calculated, and the cosine similarity of the power feature sequence and the voltage fluctuation feature sequence of each feature group is obtained. All cosine similarities are normalized by taking the negative number and then linearly normalized, and each normalized value is used as the rationality of each feature group. The larger the normalized value, the more negative correlation between power and voltage fluctuation is presented, and the higher the rationality of the feature group.

[0031] Voltage fluctuations are not necessarily caused entirely by power changes, but may also be due to environmental factors: when the temperature rises, the resistance of the wires increases, resulting in increased line losses when the load is high for a long time, and the voltage fluctuations become more significant; when the humidity in the air increases, the conductivity may rise, causing voltage fluctuations in the power grid in some extreme cases; wind speed may exert mechanical pressure on high-altitude transmission lines, transformers and other equipment, and may even cause the lines to loosen or be damaged, thereby triggering power fluctuations.

[0032] All the ambient temperatures, ambient humidity, ambient wind speeds and voltage fluctuations collected in this application are sorted in time sequence to obtain an overall temperature sequence, an overall humidity sequence, an overall wind speed sequence and an overall voltage fluctuation sequence.

[0033] The correlations between the overall voltage fluctuation sequence and the overall temperature sequence, the overall humidity sequence, and the overall wind speed sequence are calculated as the influence of temperature on voltage fluctuation, the influence of humidity on voltage fluctuation, and the influence of wind speed on voltage fluctuation. In this embodiment, the method for calculating the correlations between the overall voltage fluctuation sequence and the overall temperature sequence, the overall humidity sequence, and the overall wind speed sequence is the Pearson correlation coefficient.

[0034] All ambient temperatures of a feature group are sorted in time series to obtain a temperature feature sequence, all ambient humidity of a feature group are sorted in time series to obtain a humidity feature sequence, and all ambient wind speeds of a feature group are sorted in time series to obtain a wind speed feature sequence.

[0035] Taking the influence of environmental characteristics on voltage fluctuation as the weight, the influence of environmental parameters on voltage fluctuation is obtained by combining the correlation between environmental characteristics and voltage fluctuation in each feature group. The expression is: , Indicates the ambient temperature, Indicates the ambient humidity. Indicates the ambient wind speed, represents the overall sequence corresponding to the Kth parameter in the i-th feature group, represents the overall voltage fluctuation sequence in the i-th feature group, represents the voltage fluctuation feature sequence in the i-th feature group, represents the feature sequence corresponding to the Kth parameter in the i-th feature group, represents the correlation between two series. Represents the influence of environmental parameters in the i-th feature group on voltage fluctuation.

[0036] Through the above steps, the influence of other environmental characteristics on voltage fluctuation is obtained. Therefore, it is inaccurate to determine the rationality of each feature group of the initial matrix through the relationship between voltage fluctuation and power. Only the part that has not received the environmental influence is the part whose rationality is truly determined by the relationship between voltage fluctuation and power. Based on this, the final rationality of each feature group is obtained, and the expression is: , represents the influence of environmental parameters in the i-th feature group on voltage fluctuation, Indicates the rationality of the i-th feature group, Indicates the final rationality of the i-th feature group.

[0037] The greater the final rationality of each feature group, the less likely it is that the data corresponding to the feature group is caused by short circuit and open circuit.

[0038] Feature matrix construction module, since the distribution box is usually combined with the automatic control system for load regulation, the power change is slowly changed through regulation, so the future power corresponding to each feature group cannot be greatly different from the power change of the feature group. Therefore, on the basis of the final rationality, all future powers corresponding to each feature group are combined with the final rationality of the feature group to obtain the overall rationality of each feature row, and the expression is: , Indicates power, Indicates The power feature sequence in the feature group corresponding to the feature rows, Indicates The average power of the power feature sequence in the feature group corresponding to the feature rows, Indicates The final rationality of the feature group corresponding to the feature rows, represents the normalization function, Indicates The future power of the characteristic line, Indicates The overall rationality of the feature line.

[0039] When constructing the feature matrix, the result of the future power corresponding to each feature group needs to be unique. Therefore, all future powers corresponding to the same feature group are counted, and the overall rationality of each feature row is used as the weight. The future power of each feature row is weighted to obtain the unique future power of each feature group. The expression is: , Indicates The overall rationality of the characteristic rows, represents the number of feature rows corresponding to the i-th feature group, Indicates The future power of the characteristic line, represents the unique future power corresponding to the i-th feature group.

[0040] Then, the unique future powers of all different feature groups are obtained, and each different feature group and its corresponding unique future power are taken as a row of the matrix. Each row is sorted from top to bottom in chronological order, and the obtained matrix is ​​taken as the feature matrix. The number of columns of the feature matrix is ​​the same as that of the initial matrix.

[0041] Power monitoring module, XGBoost is an algorithm based on gradient boosting, the goal is to minimize the loss function by gradually building a series of decision trees. In each iteration, XGBoost will build a new tree to fit the residual (the residual is the difference between the predicted value of the previous tree and the true value). In order to prevent overfitting, it is usually necessary to calculate the regularization term for each tree obtained in the iteration.

[0042] For each decision tree, the regularization term consists of the number of leaf nodes and the weight of the leaf nodes. The weight of the leaf node is the predicted value of the leaf node, and the predicted value of the leaf node is determined by the average value of the future power of all feature rows that can fall on this node. These feature rows have the same contribution to regularization. If the weight of the leaf node is unavailable due to some sudden situations such as short circuit and open circuit, then the impact on regularization will be very large.

[0043] The larger the predicted value of the leaf node, the greater the regularity. In order to prevent the influence of short circuit and open circuit, its overall rationality should also be greater. Therefore, the regular contribution of each feature group is determined based on all the overall rationality and future power corresponding to each feature group. The expression is: , Indicates The overall rationality of the characteristic rows, represents the number of feature rows corresponding to the i-th feature group, represents the mean of all future powers corresponding to the i-th feature group, represents the normalization function, Represents the regularization contribution of the i-th feature group.

[0044] The feature matrix is ​​used as input, and a decision tree is constructed through XGBoost. The regular contribution of each feature group in the decision tree is obtained through the above steps. After the decision tree is constructed, the number of leaf nodes can be known.

[0045] When determining the weight of each leaf node of each decision tree, the application obtains the regularization term of the decision tree by weighting the regularization contribution of all feature groups falling on it: , and is the preset hyperparameter, represents the number of leaf nodes of the decision tree obtained by the kth iteration, represents the number of feature groups in the dth leaf node, represents the regular contribution of the u-th feature group in the d-th leaf node, represents the unique future power of the u-th feature group in the d-th leaf node, represents the regularization term of the decision tree obtained by the kth iteration. In this application, and The values ​​of are 6 and 1.

[0046] Based on this, the regular term of the decision tree obtained in each iteration process can be corrected, and then substituted into the XGBoost iterative fitting process to obtain a better anti-fitting model for predicting the power of the distribution box in the next minute.

[0047] By inputting the feature group corresponding to the current moment into the model, the power in the next minute from the current moment can be obtained, that is, the future power at the current moment, thus completing the power monitoring of the distribution box.

[0048] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a distribution box, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements any one of the modules in the above-mentioned distribution box power monitoring system.

[0049] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0050] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A power monitoring system for a distribution box, characterized in that: The system includes the following modules: The data acquisition module is used to collect parameters and future power through different sensors, take the parameters collected at the preset time as the feature group, and use the feature group and the future power as feature rows to construct an initial matrix. The parameters include power, ambient temperature, ambient humidity, ambient wind speed and voltage fluctuation; The rationality judgment module is used to obtain the rationality of a feature group according to the similarity of all power and voltage fluctuations in a feature group; calculate the influence of environmental parameters on voltage fluctuations according to the correlation between all environmental parameters and voltage fluctuations combined with the correlation between environmental parameters and voltage fluctuations in a feature group; The final rationality of the feature group is obtained according to the rationality of the feature group and the influence of environmental parameters on voltage fluctuation; The feature matrix construction module is used to obtain the overall rationality of the feature row according to the difference between the power of each feature group and the future power of the corresponding feature row and the final rationality of the feature group; weight the future power of the feature row with the overall rationality of the feature row as the weight to obtain the unique future power corresponding to the feature group; determine the feature row of each feature group based on the unique future power to construct the feature matrix; The power monitoring module is used to obtain the regular contribution of the feature group based on the mean of the overall rationality of each feature group and the mean of the future power; the feature matrix is ​​used as input to obtain the decision tree, and the regular term of the decision tree is obtained by weighting the regular contribution; the prediction value and future power are obtained based on the decision tree, and the decision tree is iterated to obtain the prediction model, and the feature group corresponding to the current moment is used as the input of the prediction model to obtain the future power at the current moment, thereby completing power monitoring.

2. A distribution box power monitoring system as claimed in claim 1, characterized in that: The method for constructing the initial matrix is: The preset collection time is taken as an interval, and the power collected in the next minute of the interval is taken as the future power corresponding to the interval; all parameter data collected in each interval are taken as feature groups, each feature group and future power are taken as feature rows, and the feature rows are taken as a row of the matrix. Multiple feature groups and their corresponding future powers are collected to construct the initial matrix.

3. A distribution box power monitoring system as claimed in claim 1, characterized in that: The method for obtaining the rationality of a feature group according to the similarity of all power and voltage fluctuations of a feature group is: Extract all powers of each feature group as a power feature sequence, and extract all voltage fluctuations of each feature group as a voltage fluctuation feature sequence; The cosine similarity of the power feature sequence and the voltage fluctuation feature sequence of each feature group is calculated, all cosine similarities are normalized by linear normalization after being negative, and each normalized value is used as the rationality of each feature group.

4. A distribution box power monitoring system as claimed in claim 3, characterized in that: The method for calculating the influence of environmental parameters on voltage fluctuations based on the correlation between all environmental parameters and voltage fluctuations combined with the correlation between environmental parameters and voltage fluctuations in a feature group is: All collected ambient temperatures, ambient humidity, ambient wind speeds and voltage fluctuations are sorted in time sequence to obtain an overall temperature sequence, an overall humidity sequence, an overall wind speed sequence and an overall voltage fluctuation sequence; Calculate the correlation between the overall voltage fluctuation sequence and the overall temperature sequence, the overall humidity sequence, and the overall wind speed sequence as the influence of temperature on voltage fluctuation, the influence of humidity on voltage fluctuation, and the influence of wind speed on voltage fluctuation; All ambient temperatures of a feature group are sorted in time series to obtain a temperature feature sequence, all ambient humidity of a feature group are sorted in time series to obtain a humidity feature sequence, and all ambient wind speeds of a feature group are sorted in time series to obtain a wind speed feature sequence; Taking the influence of environmental characteristics on voltage fluctuation as the weight, the correlation between environmental characteristics and voltage fluctuation in each feature group is weighted to obtain the influence of environmental parameters on voltage fluctuation.

5. A power distribution box power monitoring system as claimed in claim 4, characterized in that: The expression of the influence of the environmental parameters on the voltage fluctuation is: , Indicates the ambient temperature, Indicates the ambient humidity. Indicates the ambient wind speed, represents the overall sequence corresponding to the Kth parameter in the i-th feature group, represents the overall voltage fluctuation sequence in the i-th feature group, represents the voltage fluctuation feature sequence in the i-th feature group, represents the feature sequence corresponding to the Kth parameter in the i-th feature group, represents the correlation between two series. Represents the influence of environmental parameters in the i-th feature group on voltage fluctuation.

6. A distribution box power monitoring system as claimed in claim 1, characterized in that: The method for obtaining the final rationality of the feature group according to the rationality of the feature group and the influence of environmental parameters on voltage fluctuation is: , represents the influence of environmental parameters in the i-th feature group on voltage fluctuation, Indicates the rationality of the i-th feature group, Indicates the final rationality of the i-th feature group.

7. A power distribution box power monitoring system as claimed in claim 1, characterized in that: The method for obtaining the overall rationality of the feature row by combining the difference between the power of each feature group and the future power of the corresponding feature row with the final rationality of the feature group is: , Indicates power, Indicates The power feature sequence in the feature group corresponding to the feature rows, Indicates The average power of the power feature sequence in the feature group corresponding to the feature rows, Indicates The final rationality of the feature group corresponding to the feature rows, represents the normalization function, Indicates The future power of the characteristic line, Indicates The overall rationality of the feature line.

8. A power distribution box power monitoring system as claimed in claim 1, characterized in that: The method of weighting the future power of the feature row by taking the overall rationality of the feature row as the weight to obtain the unique future power corresponding to the feature group, and determining the feature row of each feature group based on the unique future power to construct the feature matrix is: Count all feature rows corresponding to a feature group, take the ratio of the overall rationality of each feature row to the sum of the overall rationality of all feature rows corresponding to the feature group as the weight of each feature row, and take the product of the weight of all feature rows and the future power as the unique future power of the feature group; Each feature group and unique future power are taken as a row of the matrix, all feature groups and their corresponding unique future powers are counted, and the feature matrix is ​​obtained by sorting them from top to bottom in chronological order.

9. A distribution box power monitoring system as claimed in claim 1, characterized in that: The method for obtaining the regularization item of the decision tree by weighting the regularization contribution is: , and To preset hyperparameters, represents the number of leaf nodes of the decision tree obtained by the kth iteration, represents the number of feature groups in the dth leaf node, represents the regular contribution of the u-th feature group in the d-th leaf node, represents the unique future power of the u-th feature group in the d-th leaf node, Represents the regularization term of the decision tree obtained in the kth iteration.

10. A distribution box, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a distribution box power monitoring system as described in any one of claims 1-9 are implemented.

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