CN116186536A - Risk prediction method, risk prediction device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a risk prediction method, a risk prediction device, electronic equipment and a storage medium. Acquiring original distribution network scheduling data, preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period; predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set; loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model. The accuracy of predicting the risk prediction grade is improved.
Description
Technical Field
The present invention relates to the field of computer application technologies, and in particular, to a risk prediction method, a risk prediction device, an electronic device, and a storage medium.
Background
With the continuous addition of massive distributed energy sources into a distribution network, the running risk of the system is increased, and the conventional distribution network scheduling risk prediction method is insufficient for being applied to the current distribution network scheduling running risk detection work.
At present, aiming at the risk detection of the complex distribution network scheduling operation, manual intervention is often needed, a person with more experience is needed, comparison analysis is carried out on the current and historical distribution network scheduling operation data to obtain the risk prediction grade of the current distribution network scheduling operation, the efficiency is low, and due to the redundancy of the historical distribution network scheduling operation data, part of important information is often ignored in manual analysis, so that the accuracy of the risk prediction grade prediction is poor.
Disclosure of Invention
The invention provides a risk prediction method, a risk prediction device, electronic equipment and a storage medium, which are used for solving the technical problem of poor accuracy of risk prediction grade prediction.
According to an aspect of the present invention, there is provided a risk prediction method, wherein the method includes:
acquiring original distribution network scheduling data, preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period;
Predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set;
loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model.
According to another aspect of the present invention, there is provided a risk prediction apparatus, wherein the apparatus includes:
the data acquisition module is used for acquiring original distribution network scheduling data, preprocessing the original distribution network scheduling data, and obtaining data to be predicted and risk data corresponding to the data to be predicted in a first time period;
the result prediction module is used for predicting the input data to be predicted through a remote control prediction model which is completed by training to obtain a remote control prediction result, wherein the remote control prediction model is obtained by training an isolated forest model through a training sample set;
The level prediction module is used for loading the terminal online rate corresponding to the data to be predicted, and determining the risk prediction level corresponding to the data to be predicted according to the remote control prediction result, the terminal online rate and the risk data corresponding to the data to be predicted, wherein the terminal online rate is obtained by predicting the input data to be predicted through a online rate prediction model.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the risk prediction method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a risk prediction method according to any one of the embodiments of the present invention.
According to the technical scheme, the original distribution network scheduling data are obtained, the original distribution network scheduling data are preprocessed, the data to be predicted and the risk data corresponding to the data to be predicted in the first time period are obtained, the obtained data are preprocessed, the digitalized data to be predicted and the risk data which possibly influence the remote control prediction result are obtained, so that the model can be conveniently identified and predicted, and the prediction efficiency and accuracy of the model on the remote control prediction result are improved; the input data to be predicted is predicted through a remote control prediction model which is completed through training, so that a remote control prediction result is obtained, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set, the remote control prediction result with high accuracy is obtained, and the prediction of a risk prediction grade can be more accurate; loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model. And the risk level is predicted based on the multidimensional data, so that the accuracy of predicting the risk prediction level is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a risk prediction method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a risk prediction method according to a second embodiment of the present invention;
FIG. 3 is a flow chart of a risk prediction method according to a third embodiment of the present invention;
FIG. 4 is an overall flow chart of a risk prediction method provided in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a risk prediction device according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device implementing a risk prediction method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a risk prediction method according to an embodiment of the present invention, where the method may be applied to a case of scheduling risk prediction, and the method may be performed by a risk prediction device, where the risk prediction device may be implemented in a form of hardware and/or software, and the risk prediction device may be configured in a computer. As shown in fig. 1, the method includes:
s110, acquiring original distribution network scheduling data, and preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period.
The original distribution network scheduling data can be understood as original distribution network scheduling data. Alternatively, the original distribution network scheduling data may be table data related to distribution network scheduling. In the embodiment of the present invention, the original distribution network scheduling data may be preset according to the scene requirement, which is not specifically limited herein. By way of example, the original distribution network schedule data may be schedule controller shift data, geographical location data of the schedule controller, schedule time data, schedule controller shift fault data, and the like.
The data to be predicted can be understood as data obtained after preprocessing the original distribution network scheduling data.
The first time period may be understood as a corresponding time period when risk data corresponding to the data to be predicted is obtained. In the embodiment of the present invention, the first period may be preset according to a scene requirement, which is not specifically limited herein. Alternatively, the first period of time may be a previous week of schedule time data for the original distribution network schedule data. The risk data may be understood as risk data corresponding to the data to be predicted. Alternatively, the risk data may be schedule controller shift fault data in the data to be predicted. Exemplary embodiments. The risk data may be switch failure data.
Specifically, preset table data related to distribution network scheduling is obtained, and preprocessing operations such as information system matching and natural language processing are performed on the table data to obtain the data to be predicted and risk data corresponding to the data to be predicted in the previous week.
S120, predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set.
The remote control prediction model can be understood as a model for predicting the input data to be predicted to obtain a remote control prediction result.
The remote control prediction result can be understood as a result obtained by predicting the input data to be predicted through a trained remote control prediction model. Optionally, the remote control prediction result may be a prediction result obtained by predicting displacement data of the scheduling controller in the data to be predicted. It can be understood that the geographical position data, the scheduling time data, the displacement fault data of the scheduling controller, the terminal online rate data and the like of the scheduling controller in the data to be predicted may affect the remote control prediction result of the displacement data of the scheduling controller in the data to be predicted. In the embodiment of the present invention, the output type of the remote control prediction result may be preset according to the scene requirement, which is not specifically limited herein. Illustratively, the remote control prediction result may be 0 or 1. Wherein, the remote control prediction result is 0, which can represent the success of remote control prediction; the remote control prediction result is 1, and the failure of remote control prediction can be represented. Alternatively, the remote control prediction result may be a prediction value of 0 to 1, and further, a prediction value not exceeding 0.5 may be determined as a remote control prediction success; and determining a predicted value exceeding 0.5 as a remote control prediction failure.
The training sample set may be understood as a sample set for training the isolated forest model. It is understood that the training sample data in the training sample set may be the same type of data as the data to be predicted.
The isolated forest model may be understood as a model for training the remote control predictive model.
It is to be understood that the isolated forest model is a classification model based on abnormal isolation, and is different from the traditional clustering classification model such as K-means and DBSCAN, and the main purpose is to isolate abnormal samples instead of statistic normal samples, and an iterative algorithm is established through the characteristic that the abnormal samples occupy a small number, so that the detection precision is greatly improved. When the isolated forest is trained, training samples of each tree are randomly extracted and generated, and distance indexes are not required to be calculated during calculation, so that the method has linear time complexity, can effectively reduce time cost, and is suitable for processing large-scale data. The basic idea of an isolated forest is similar to multi-dimensional hyperplane segmentation, assuming that a sample set S is initially owned, a random hyperplane is selected to cut the data set to generate 2 subspaces, then the hyperplane is randomly selected to cut the 2 subspaces, and the steps are repeated until each subspace only contains one sample point. Each sample point will thus correspond to a number of divisions, which describes the number of hyperplanes used to separate the sample point alone. Normal samples in the high density region should have a larger number of divisions, while outliers at the sample boundary will have a smaller number of divisions. The construction of the isolated forest is divided into 2 stages of training and integration, and the training process of a single tree (namely, an isolated tree) is as follows:
1) The psi points are randomly extracted from the original sample set S to form a root node sub-sample set.
2) Randomly selecting a dimension omega, and randomly selecting a cutting point p in the dimension data range.
3) And generating a hyperplane by using the cutting point p, dividing the training sample into 2 subspaces, and dividing samples smaller than p and larger than p in the selected dimension omega in the subspaces into 2 classes at the same time, so as to respectively form left and right branches of the node.
4) Repeating steps 2) and 3) to generate new leaf nodes continuously until the branch of the new child node contains only 1 sample point. The training process of a single tree is adopted, and the sample selection of an isolated forest and the selection of a hyperplane cutting point are random, so that repeated training is needed, and finally, all tree segmentation results are integrated and averaged. Assuming a total of T trees are trained, for sample point x, subsampleset ψ, its anomaly score s is calculated:
where h (x) represents the depth of the sample point at each isolated tree, E (h (x)) represents its average depth, and c (ψ) represents the average of the path lengths for a given sample ψ.
c (ψ) is used as normalization processing. If the value of s is close to 1, this point is an abnormal value, and if the value of s is far less than 0.5, this point is normal data.
S130, loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results, the terminal online rates and the risk data corresponding to the data to be predicted.
The terminal presence ratio may be understood as a terminal presence ratio. In the embodiment of the invention, the terminal online rate is obtained by predicting the input data to be predicted through an online rate prediction model. Illustratively, the terminal presence rate may be 90%, 80%, 70%, or the like.
The risk prediction grade may be understood as a risk grade corresponding to the predicted data to be predicted. Specifically, the risk prediction grade may be determined according to the remote control prediction result corresponding to the data to be predicted, the terminal online rate, and the risk data. In the embodiment of the present invention, the output type of the risk prediction level may be preset according to the scene requirement, which is not specifically limited herein. Alternatively, the risk prediction rating may be 0, 1, 2, or 3. Wherein the risk prediction grade is 0, and no risk can be represented; the risk prediction grade is 1, and mild risks can be represented; the risk prediction grade is 2, and medium risk can be represented; the risk prediction grade is 3, and the severe risk can be characterized. It can be understood that, in the data to be predicted, the larger the remote control prediction result is, the lower the terminal online rate is, and the more the risk data is, the larger the risk level corresponding to the data to be predicted is.
The online rate prediction model may be understood as a model for predicting the online rate of the terminal corresponding to the data to be predicted.
According to the technical scheme, the original distribution network scheduling data are obtained, the original distribution network scheduling data are preprocessed, the data to be predicted and the risk data corresponding to the data to be predicted in the first time period are obtained, the obtained data are preprocessed, the digitalized data to be predicted and the risk data which possibly influence the remote control prediction result are obtained, so that the model can be conveniently identified and predicted, and the prediction efficiency and accuracy of the model on the remote control prediction result are improved; the input data to be predicted is predicted through a remote control prediction model which is completed through training, so that a remote control prediction result is obtained, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set, the remote control prediction result with high accuracy is obtained, and the prediction of a risk prediction grade can be more accurate; loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model. And the risk level is predicted based on the multidimensional data, so that the accuracy of predicting the risk prediction level is improved.
Example two
Fig. 2 is a flowchart of a risk prediction method according to a second embodiment of the present invention, where the remote control prediction model, which is trained in the above embodiment, predicts the input data to be predicted, and obtains a remote control prediction result for addition. As shown in fig. 2, the method includes:
s210, training the isolated forest model through the training sample set to obtain a preliminary prediction model.
The preliminary prediction model may be understood as a model obtained by training the isolated forest model through the training sample set.
Optionally, the training the isolated forest model through the training sample set to obtain a preliminary prediction model includes:
predicting the input training sample data through the isolated forest model to obtain a first remote control result;
acquiring terminal online rate, target remote control result and risk data corresponding to the training sample data in a second time period, wherein the terminal online rate and the target remote control result correspond to the training sample data;
and adjusting the weights of risk influencing factors in the isolated forest model according to the first remote control result, the target remote control result, the terminal online rate and the risk data corresponding to the training sample data to obtain a preliminary prediction model.
The first remote control result can be understood as a remote control result obtained by predicting the input training sample data through the isolated forest model. Alternatively, the remote control result may be 0 or 1, or a predicted value of 0 to 1.
The target remote control result can be understood as an actual remote control result corresponding to the training sample data. It may be appreciated that the first remote control result and the target remote control result corresponding to the training sample data may be the same or different.
The second time period may be understood as a corresponding time period when the risk data corresponding to the training sample data is obtained. In the embodiment of the present invention, the second period may be preset according to a scene requirement, which is not specifically limited herein. Alternatively, the second time period may be a previous week of scheduled time data for the training sample data. The risk data may be understood as risk data corresponding to the training sample data. Alternatively, the risk data may be dispatch controller shift fault data in the training sample data. Exemplary embodiments. The risk data may be switch failure data.
The risk influencing factor can be understood as a factor which can be understood as a factor influencing the remote control result corresponding to the predicted training sample data. In the embodiment of the present invention, the risk influencing factor may be preset according to the scene requirement, which is not specifically limited herein. Alternatively, the risk influencing factor may be scheduling time data, geographical position data of the scheduling controller or displacement fault data of the scheduling controller in the training sample data, etc.
The weight can be understood as the influence degree of each risk influence factor on the prediction result of the isolated forest model. It will be appreciated that the degree of influence of each of the risk influencing factors on the predicted outcome of the isolated forest model may be the same or different. The greater the weight of the risk influencing factors, the greater the influence degree on the prediction result of the isolated forest model.
S220, testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model under the condition that preset conditions are met.
The test sample set may be understood as a sample set for testing the preliminary prediction model. It is understood that the test sample set may be the same type of data set as the training sample set.
The preset condition may be understood as a condition for judging whether the preliminary predictive model can be used as the remote control predictive model. In the embodiment of the present invention, the preset conditions may be preset according to the scene requirement, which is not specifically limited herein.
Optionally, the testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model when a preset condition is met, includes:
testing the preliminary prediction model through the test sample set to obtain model evaluation indexes corresponding to the preliminary prediction model, wherein the model evaluation indexes comprise at least one of accuracy rate, precision rate, recall rate and first evaluation indexes consisting of the accuracy rate, the precision rate and the recall rate;
and taking the preliminary prediction model as the remote control prediction model under the condition that the model evaluation index corresponding to the preliminary prediction model meets the preset condition.
Wherein the model evaluation index may be understood as an index for evaluating the preliminary prediction model. Optionally, the model evaluation index may include: accuracy, precision, recall, and a first evaluation index.
Specifically, the formula for calculating the accuracy of the preliminary prediction model may be:
wherein A represents accuracy, n TP Indicating the sample training data of which the target remote control result is remote control failure to be predicted as the data quantity of remote control failure, n TN Indicating that sample training data with target remote control result being remote control success is predicted as data quantity of remote control success, n FP Indicating the sample training data of which the target remote control result is remote control success to be predicted as the data quantity of remote control failure, n FN And the sample training data with the target remote control result of remote control failure is predicted to be the data quantity of remote control success.
Specifically, the formula for calculating the accuracy of the preliminary prediction model may be:
wherein P represents accuracy, n TP Indicating the sample training data of which the target remote control result is remote control failure to be predicted as the data quantity of remote control failure, n FP And the sample training data with the target remote control result being the remote control success is predicted to be the data quantity of remote control failure.
Specifically, the formula for calculating the recall ratio of the preliminary prediction model may be:
wherein R represents a recall rate, n TP Indicating the sample training data of which the target remote control result is remote control failure to be predicted as the data quantity of remote control failure, n FN And the sample training data with the target remote control result of remote control failure is predicted to be the data quantity of remote control success.
Specifically, the formula for calculating the first evaluation index of the preliminary prediction model may be:
wherein F is 1 And (3) representing a first evaluation index, wherein P represents accuracy rate, and R represents recall rate.
Specifically, a condition threshold may be preset, and the preliminary prediction model is used as the remote control prediction model when the accuracy, the precision, the recall and the first evaluation index corresponding to the preliminary prediction model exceed the preset condition threshold.
S230, acquiring original distribution network scheduling data, and preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period.
S240, predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set.
S250, loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results, the terminal online rates and the risk data corresponding to the data to be predicted.
According to the technical scheme, the training sample set is used for training the isolated forest model to obtain a preliminary prediction model; and testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model under the condition that the preset condition is met. The effect of improving the accuracy of the remote control prediction model is achieved.
Example III
Fig. 3 is a flowchart of a risk prediction method according to a third embodiment of the present invention, where the training of the isolated forest model through the training sample set to obtain a preliminary prediction model is performed for addition. As shown in fig. 3, the method includes:
s310, acquiring a historical distribution network scheduling data set, and preprocessing the historical distribution network scheduling data set to obtain a sample data set, and the risk data and the risk influence factors corresponding to the sample data set.
The historical distribution network scheduling data set can be understood as a historical distribution network scheduling data set. Alternatively, the historical distribution network schedule data set may be tabular data related to distribution network scheduling. In the embodiment of the present invention, the data in the historical distribution network scheduling data set may be data of the same data type as the original distribution network scheduling data. By way of example, the data in the historical distribution network scheduling data set may be scheduling controller shift data, geographical position data of the scheduling controller, scheduling time data, scheduling controller shift fault data, and the like.
Optionally, the preprocessing the historical distribution network scheduling data set to obtain a sample data set, and the risk data and the risk influencing factors corresponding to the sample data set, including:
obtaining a first sample set by carrying out information matching on the historical distribution network scheduling data set;
and performing natural language processing on the first sample set to obtain the sample data set, and the risk data and the risk influencing factors corresponding to the sample data set.
The first sample set may be understood as a sample set obtained by performing information matching on the historical distribution network scheduling data set. In particular. And matching the terminal information and the switch information in the historical distribution network scheduling data set, and deleting the terminal information and the switch information which have no matching relation to obtain the corresponding first sample set.
Optionally, the obtaining the sample data set and the risk data and the risk influencing factor corresponding to the sample data set by performing natural language processing on the first sample set includes:
obtaining a standardized sample set by carrying out standardized processing on the first sample set;
And carrying out ascending arrangement on the standardized sample sets, extracting key features of the standardized sample sets after ascending arrangement, and encoding the extracted key features to obtain the sample data set, wherein the sample data set comprises corresponding risk data and risk influence factors.
The normalized sample set may be understood as a sample set obtained by performing normalization processing on the first sample set.
The key features may be understood as key feature data in the normalized sample set. In the embodiment of the present invention, the key features may be preset according to the scene requirement, which is not specifically limited herein. Alternatively, the key feature may be geographical location data of the dispatch controller, dispatch time data, dispatch controller shift fault data, or the like. By way of example, the key features may be chinese fields such as city, county, power supply, substation, line, and switch name.
Specifically, a historical distribution network scheduling data set is obtained, information matching is carried out on terminal information and switch information in the historical distribution network scheduling data set, and data without matching relation are deleted to obtain a first sample set; further, the first sample set is standardized, and the field type and the data format of the first sample set are standardized uniformly to obtain a standardized sample set; and further, carrying out ascending order arrangement on the standardized sample set, extracting key features, and carrying out digital coding to obtain a sample data set, and the risk data and the risk influence factors corresponding to the sample data set.
S320, dividing the sample data set according to a preset proportion to obtain the training sample set and the test sample set.
The preset proportion may be a proportion for dividing the sample data set. In the embodiment of the present invention, the preset ratio may be preset according to the scene requirement, which is not specifically limited herein. Alternatively, the preset ratio may be 2:1.
S330, training the isolated forest model through the training sample set to obtain a preliminary prediction model.
And S340, testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model under the condition that the preset condition is met.
S350, acquiring original distribution network scheduling data, and preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period.
S360, predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set.
And S370, loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results, the terminal online rates and the risk data corresponding to the data to be predicted.
According to the technical scheme, the historical distribution network scheduling data set is obtained, and is preprocessed, so that a sample data set, the risk data corresponding to the sample data set and the risk influencing factors are obtained; dividing the sample data set according to a preset proportion to obtain the training sample set and the test sample set. The digital training sample set, the digital testing sample set, the corresponding risk data and the corresponding risk influence factors are obtained, so that the weight of the isolated forest model is adjusted, and the training efficiency of the model is improved.
Fig. 4 is an overall flowchart of a risk prediction method provided according to an embodiment of the present invention. As shown in fig. 4, the overall flow of the risk prediction method may be:
1. and preprocessing the historical distribution network scheduling data set. Deleting the data without counting the remote control result, standardizing the format of the starting remote control time of the dispatching controller, arranging the formats in ascending order, and extracting the quarterly and the remote control time period according to the remote control time;
2. And (5) terminal information matching. According to the switch terminal mapping corresponding table, matching the switch with terminal information;
3. and (3) natural language processing, extracting key features and standardization. The method comprises the steps of performing natural language processing on Chinese fields such as local and regional offices, county offices, power supply offices, substations, lines, switch names and the like, extracting key parts, then encoding, and assigning a success value to a remote control result and a failure value to a 1.
4. Dividing the sample data set, training the model and outputting the result. Dividing samples of each quarter into a training sample set and a testing sample set according to a ratio of 2:1, training an isolated forest model, and testing based on the testing sample set.
5. And loading risk data and online rate data, adjusting weights, and establishing a risk prediction system. And according to the output result of the model, the weight of various influencing factors is adjusted by combining the risk data and the online rate data of the dispatching operation of the distribution network, and a distribution network dispatching risk perception evaluation system containing mass distributed energy sources is established. The four gears are divided into: "no risk", "low risk", "medium risk", "high risk", the probability of failure increases in sequence.
According to the invention, through the training of the distribution network dispatching historical data, the optimization dispatching operation and the auxiliary decision algorithm, a method and a technology for intelligently analyzing the distribution network dispatching plan and dispatching operation under different time scales are formed, a power distribution network equipment risk perception model based on equipment multi-feature parameters is constructed, the machine learning technology is used for learning the historical data, and the model is used for carrying out predictive analysis and active warning on the dispatching controller behavior based on the behavior data of the previous week, so that the safety of emergency treatment is improved.
Example IV
Fig. 5 is a schematic structural diagram of a risk prediction apparatus according to a fourth embodiment of the present invention. As shown in fig. 5, the apparatus includes: a data acquisition module 410, a result prediction module 420, and a rank prediction module 430.
The data acquisition module 410 is configured to acquire original distribution network scheduling data, and pre-process the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first period of time;
the result prediction module 420 is configured to predict the input data to be predicted by using a remote control prediction model that is completed by training, so as to obtain a remote control prediction result, where the remote control prediction model is obtained by training an isolated forest model by using a training sample set;
the level prediction module 430 is configured to load a terminal online rate corresponding to the data to be predicted, and determine a risk prediction level corresponding to the data to be predicted according to the remote control prediction result corresponding to the data to be predicted, the terminal online rate, and the risk data, where the terminal online rate is obtained by predicting the input data to be predicted through a online rate prediction model.
According to the technical scheme, the original distribution network scheduling data are obtained, the original distribution network scheduling data are preprocessed, the data to be predicted and the risk data corresponding to the data to be predicted in the first time period are obtained, the obtained data are preprocessed, the digitalized data to be predicted and the risk data which possibly influence the remote control prediction result are obtained, so that the model can be conveniently identified and predicted, and the prediction efficiency and accuracy of the model on the remote control prediction result are improved; the input data to be predicted is predicted through a remote control prediction model which is completed through training, so that a remote control prediction result is obtained, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set, the remote control prediction result with high accuracy is obtained, and the prediction of a risk prediction grade can be more accurate; loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model. And the risk level is predicted based on the multidimensional data, so that the accuracy of predicting the risk prediction level is improved.
Optionally, the risk prediction device further includes: the model training module and the model testing module.
The model test module is used for training the isolated forest model through the training sample set before the remote control prediction result is obtained by predicting the input data to be predicted through the trained remote control prediction model, so as to obtain a preliminary prediction model;
the model test module is used for testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model under the condition that the preset condition is met.
Optionally, the model training module is configured to:
predicting the input training sample data through the isolated forest model to obtain a first remote control result;
acquiring terminal online rate, target remote control result and risk data corresponding to the training sample data in a second time period, wherein the terminal online rate and the target remote control result correspond to the training sample data;
and adjusting the weight of risk influencing factors in the first prediction model according to the first remote control result, the target remote control result, the terminal online rate and the risk data corresponding to the training sample data to obtain a preliminary prediction model.
Optionally, the model test module is used for:
testing the preliminary prediction model through the test sample set to obtain model evaluation indexes corresponding to the preliminary prediction model, wherein the model evaluation indexes comprise at least one of accuracy rate, precision rate, recall rate and first evaluation indexes consisting of the accuracy rate, the precision rate and the recall rate;
and taking the preliminary prediction model as the remote control prediction model under the condition that the model evaluation index corresponding to the preliminary prediction model meets the preset condition.
Optionally, the risk prediction device includes: and the data preprocessing module and the data dividing module.
The data preprocessing module is used for acquiring a historical distribution network scheduling data set before training the isolated forest model through the training sample set to obtain a preliminary prediction model, and preprocessing the historical distribution network scheduling data set to obtain a sample data set, and the risk data and the risk influence factors corresponding to the sample data set;
the data dividing module is used for dividing the sample data set according to a preset proportion to obtain the training sample set and the test sample set.
Optionally, the data preprocessing module includes: an information matching unit and a language processing unit.
The information matching unit is used for obtaining a first sample set by carrying out information matching on the historical distribution network scheduling data set;
the language processing unit is configured to obtain the sample data set and the risk data and the risk influencing factor corresponding to the sample data set by performing natural language processing on the first sample set.
Optionally, the language processing unit is configured to:
obtaining a standardized sample set by carrying out standardized processing on the first sample set;
and carrying out ascending arrangement on the standardized sample sets, extracting key features of the standardized sample sets after ascending arrangement, and encoding the extracted key features to obtain the sample data set, wherein the sample data set comprises corresponding risk data and risk influence factors.
The risk prediction device provided by the embodiment of the invention can execute the risk prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 6 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the risk prediction method.
In some embodiments, the risk prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the risk prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the risk prediction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A risk prediction method, comprising:
acquiring original distribution network scheduling data, preprocessing the original distribution network scheduling data to obtain data to be predicted and risk data corresponding to the data to be predicted in a first time period;
predicting the input data to be predicted through a remote control prediction model which is completed through training to obtain a remote control prediction result, wherein the remote control prediction model is obtained through training an isolated forest model through a training sample set;
Loading terminal online rates corresponding to the data to be predicted, and determining risk prediction grades corresponding to the data to be predicted according to the remote control prediction results corresponding to the data to be predicted, the terminal online rates and the risk data, wherein the terminal online rates are obtained by predicting the input data to be predicted through a online rate prediction model.
2. The method according to claim 1, wherein before predicting the input data to be predicted by a trained remote control prediction model to obtain a remote control prediction result, comprising:
training the isolated forest model through the training sample set to obtain a preliminary prediction model;
and testing the preliminary prediction model through the test sample set, and taking the preliminary prediction model as the remote control prediction model under the condition that the preset condition is met.
3. The method of claim 2, wherein training the isolated forest model through the training sample set to obtain a preliminary prediction model comprises:
predicting the input training sample data through the isolated forest model to obtain a first remote control result;
Acquiring terminal online rate, target remote control result and risk data corresponding to the training sample data in a second time period, wherein the terminal online rate and the target remote control result correspond to the training sample data;
and adjusting the weights of risk influencing factors in the isolated forest model according to the first remote control result, the target remote control result, the terminal online rate and the risk data corresponding to the training sample data to obtain a preliminary prediction model.
4. The method according to claim 2, wherein the testing the preliminary prediction model by the test sample set, in case a preset condition is satisfied, takes the preliminary prediction model as the remote control prediction model, comprises:
testing the preliminary prediction model through the test sample set to obtain model evaluation indexes corresponding to the preliminary prediction model, wherein the model evaluation indexes comprise at least one of accuracy rate, precision rate, recall rate and first evaluation indexes consisting of the accuracy rate, the precision rate and the recall rate;
and taking the preliminary prediction model as the remote control prediction model under the condition that the model evaluation index corresponding to the preliminary prediction model meets the preset condition.
5. The method of claim 2, comprising, prior to training the orphan forest model by the training sample set to obtain a preliminary predictive model:
acquiring a historical distribution network scheduling data set, and preprocessing the historical distribution network scheduling data set to obtain a sample data set, and the risk data and the risk influencing factors corresponding to the sample data set;
dividing the sample data set according to a preset proportion to obtain the training sample set and the test sample set.
6. The method according to claim 5, wherein preprocessing the historical distribution network scheduling data set to obtain a sample data set, and the risk data and the risk influencing factors corresponding to the sample data set, includes:
obtaining a first sample set by carrying out information matching on the historical distribution network scheduling data set;
and performing natural language processing on the first sample set to obtain the sample data set, and the risk data and the risk influencing factors corresponding to the sample data set.
7. The method according to claim 6, wherein the obtaining the sample data set and the risk data and the risk influencing factors corresponding to the sample data set by performing natural language processing on the first sample set includes:
Obtaining a standardized sample set by carrying out standardized processing on the first sample set;
and carrying out ascending arrangement on the standardized sample sets, extracting key features of the standardized sample sets after ascending arrangement, and encoding the extracted key features to obtain the sample data set, wherein the sample data set comprises corresponding risk data and risk influence factors.
8. A risk prediction apparatus, comprising:
the data acquisition module is used for acquiring original distribution network scheduling data, preprocessing the original distribution network scheduling data, and obtaining data to be predicted and risk data corresponding to the data to be predicted in a first time period;
the result prediction module is used for predicting the input data to be predicted through a remote control prediction model which is completed by training to obtain a remote control prediction result, wherein the remote control prediction model is obtained by training an isolated forest model through a training sample set;
the level prediction module is used for loading the terminal online rate corresponding to the data to be predicted, and determining the risk prediction level corresponding to the data to be predicted according to the remote control prediction result, the terminal online rate and the risk data corresponding to the data to be predicted, wherein the terminal online rate is obtained by predicting the input data to be predicted through a online rate prediction model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the risk prediction method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the risk prediction method of any one of claims 1-7.
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