Power outage judgment and processing method based on big data

Abstract

This application relates to a power outage judgment and processing method based on big data. The method includes: using artificial intelligence technology based on deep learning to perform data time series analysis on weather monitoring data, capturing the local time series change characteristics of the weather data, and then through the correlation optimization and time series aggregation of the weather data time series change characteristics in each local time domain, mining the time series dynamic change pattern of the weather data in the global time domain, so as to conduct intelligent power outage early warning. In this way, the accuracy and timeliness of power outage early warning can be effectively improved, which helps to discover potential power outage risks in advance, so as to take corresponding preventive measures.

Description

Technical Field

[0001] This application relates to the technical field of power outage judgment, and specifically, to a method for processing power outage judgment based on big data. Background Art

[0002] Power supply is an indispensable part of modern life. Any unexpected power outage will bring inconvenience or even serious consequences to social economy and personal life. The normal operation of the power system is affected by various factors, among which weather conditions are a very important external factor. Under extreme weather conditions such as thunderstorms, typhoons, and hailstorms, power grid facilities may be damaged to varying degrees, resulting in large-scale power outage accidents. Therefore, accurately predicting the impact of extreme weather on the power grid and making emergency preparations in advance are of great significance for reducing the negative impact of power outages.

[0003] Traditional power outage warning methods mainly rely on weather forecasts and historical data of the power grid, and use expert experience and some simple statistical methods for prediction. Although this method can play a warning role to a certain extent, due to its dependence on manual analysis, it is difficult to predict power outage risks in real time and accurately. Moreover, due to the lack of in-depth mining and analysis of weather data, its warning effect is often not satisfactory.

[0004] Therefore, an intelligent method for processing power outage judgment based on big data is expected. Summary of the Invention

[0005] This Summary of the Invention section is provided to introduce concepts in a brief form, which will be described in detail in the following Detailed Description section. This Summary of the Invention section is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] In a first aspect, this application provides a method for processing power outage judgment based on big data, the method comprising:

[0007] Obtaining a time queue of weather monitoring data, wherein the weather monitoring data includes temperature values, humidity values, wind speed values, and rainfall amounts;

[0008] Performing time series feature extraction based on a local time scale on the time queue of the weather monitoring data to obtain a sequence of local time series correlation feature vectors of the weather data;

[0009] Performing feature correlation optimization on the sequence of local time series correlation feature vectors of the weather data to obtain a sequence of local time series correlation optimized feature vectors of the weather data;

[0010] Based on the feature significance temporal aggregation information of the sequence of the feature vectors optimized by the local temporal correlation of the weather data, determine whether to generate a power outage warning prompt.

[0011] Optionally, perform temporal feature extraction based on a local time scale on the time queue of the weather monitoring data to obtain a sequence of local temporal correlation feature vectors of the weather data, including: using a weather data embedding matrix to perform embedding encoding on each weather monitoring data in the time queue of the weather monitoring data to obtain a time queue of weather monitoring embedding encoding vectors; dividing the time queue of the weather monitoring embedding encoding vectors by a predetermined time scale and inputting it into a weather local temporal feature catcher based on a 1D-CNN model to obtain the sequence of the local temporal correlation feature vectors of the weather data.

[0012] Optionally, perform feature correlation optimization on the sequence of the local temporal correlation feature vectors of the weather data to obtain a sequence of optimized local temporal correlation feature vectors of the weather data, including: inputting the sequence of the local temporal correlation feature vectors of the weather data into an adaptive feature optimization module based on sequence correlation semantics to obtain the sequence of the optimized local temporal correlation feature vectors of the weather data.

[0013] Optionally, inputting the sequence of the local temporal correlation feature vectors of the weather data into an adaptive feature optimization module based on sequence correlation semantics to obtain the sequence of the optimized local temporal correlation feature vectors of the weather data, including: calculating the semantic association score vector between any two local temporal correlation feature vectors of the weather data in the sequence of the local temporal correlation feature vectors of the weather data to obtain a sequence of local temporal semantic association score vectors of the weather data; calculating the mean vector of the sequence of the local temporal semantic association score vectors of the weather data to obtain a globally representative vector of sequence endogenous correlation; based on the globally representative vector of sequence endogenous correlation, calculating the association optimization factor of each local temporal correlation feature vector in the sequence of the local temporal correlation feature vectors of the weather data to obtain a sequence of association optimization factors; inputting the sequence of the association optimization factors into an activation function to obtain a sequence of association optimization weight factors; using each association optimization weight factor in the sequence of the association optimization weight factors as a weight, respectively weighting each local temporal correlation feature vector in the sequence of the local temporal correlation feature vectors of the weather data to obtain the sequence of the optimized local temporal correlation feature vectors of the weather data.

[0014] Optionally, the semantic association score vector between any two local time series association feature vectors of weather data in the sequence of local time series association feature vectors of weather data is calculated to obtain a sequence of local time series semantic association score vectors of weather data, including: cascading any two local time series association feature vectors of weather data in the sequence of local time series association feature vectors of weather data, multiplying them by a weight coefficient matrix, and then dot-adding them with a bias vector to obtain the local time series semantic association score vector of weather data.

[0015] Optionally, based on the global representation vector of the intrinsic correlation of the sequence, the correlation optimization factor of each local time series correlation feature vector of weather data in the sequence of local time series correlation feature vectors of weather data is calculated to obtain a sequence of correlation optimization factors, including: multiplying the local time series correlation feature vector of weather data and the global representation vector of the intrinsic correlation of the sequence by different weight coefficient vectors and then performing an addition operation to obtain a semantic correlation coefficient; adding a bias parameter to the semantic correlation coefficient and then passing it through a sigmoid activation function to obtain the correlation optimization factor.

[0016] Optionally, based on the feature significance time series aggregation information of the sequence of local time series association optimization feature vectors of the weather data, determining whether to generate a power outage warning prompt includes: inputting the sequence of local time series association optimization feature vectors of the weather data into a node time series aggregation response network modulated by feature significance to obtain a weather data time series significant aggregation representation vector; inputting the weather data time series significant aggregation representation vector into a classifier-based judgment result generator to obtain a judgment result, and the judgment result is used to indicate whether to generate the power outage warning prompt.

[0017] Optionally, input the sequence of the locally temporally correlated and optimized feature vectors of the weather data into a node temporal aggregation response network with feature significance modulation to obtain a temporally significant aggregation representation vector of the weather data, including: calculating a feature significance description factor for each of the locally temporally correlated and optimized feature vectors in the sequence of the locally temporally correlated and optimized feature vectors of the weather data; using the last locally temporally correlated and optimized feature vector in the sequence of the locally temporally correlated and optimized feature vectors of the weather data as the current locally temporally correlated and optimized feature vector, and constructing a feature significance attenuation factor for each of the other locally temporally correlated and optimized feature vectors in the sequence based on the distance span between each of the other locally temporally correlated and optimized feature vectors and the current locally temporally correlated and optimized feature vector in the sequence of the locally temporally correlated and optimized feature vectors of the weather data; calculating the product of the feature significance attenuation factor of each of the other locally temporally correlated and optimized feature vectors and its feature significance description factor to obtain a sequence of feature significance attenuation description factors; inputting the sequence of the feature significance attenuation description factors into a gated mask module to obtain a sequence of feature significance attenuation weight factors; and calculating the weighted sum of the sequence of the locally temporally correlated and optimized feature vectors of the weather data based on the sequence of the feature significance attenuation weight factors to obtain the temporally significant aggregation representation vector of the weather data.

[0018] Optionally, calculating a feature significance description factor for each of the locally temporally correlated and optimized feature vectors in the sequence of the locally temporally correlated and optimized feature vectors of the weather data includes: calculating the expected value of the fourth power of the difference between each eigenvalue in the locally temporally correlated and optimized feature vector and its feature mean, and dividing the expected value by the square of the feature variance of the locally temporally correlated and optimized feature vector to obtain the feature significance description factor.

[0019] Optionally, constructing a feature significance attenuation factor for each of the other locally temporally correlated and optimized feature vectors in the sequence based on the distance span between each of the other locally temporally correlated and optimized feature vectors and the current locally temporally correlated and optimized feature vector in the sequence of the locally temporally correlated and optimized feature vectors of the weather data includes: calculating the difference between the maximum eigenvalue of the current locally temporally correlated and optimized feature vector and the maximum eigenvalues of each of the other locally temporally correlated and optimized feature vectors in the sequence of the locally temporally correlated and optimized feature vectors of the weather data, and then dividing the difference by the number of feature vectors separated between them to obtain the feature significance attenuation factor for each of the other locally temporally correlated and optimized feature vectors.

[0020] With the above technical solution, by using artificial intelligence technology based on deep learning to perform data time series analysis on weather monitoring data, the local time series change characteristics of weather data are captured. Then, by correlating and optimizing the time series change characteristics of weather data in each local time domain and performing time series aggregation, the time series dynamic change pattern of weather data in the global time domain is mined, so as to perform intelligent power outage warning. In this way, the accuracy and timeliness of power outage warning can be effectively improved, which helps to discover potential power outage risks in advance, so as to take corresponding preventive measures.

[0021] Other features and advantages of this application will be described in detail in the following specific implementation part. Brief Description of the Drawings

[0022] Combined with the drawings and referring to the following specific implementation manners, the above and other features, advantages and aspects of each embodiment of this application will become more obvious. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic, and the original components and elements are not necessarily drawn to scale. In the drawings:

[0023] Figure 1 is a flowchart of a power outage judgment and processing method based on big data shown according to an exemplary embodiment.

[0024] Figure 2 is a block diagram of a power outage judgment and processing system based on big data shown according to an exemplary embodiment.

[0025] Figure 3 is a logical model diagram of a data analysis method shown according to an exemplary embodiment.

[0026] Figure 4 is a flowchart of a warning release judgment shown according to an exemplary embodiment.

[0027] Figure 5 is a schematic diagram showing the relationship between different weather types and the number of power outage users shown according to an exemplary embodiment.

[0028] Figure 6 is a block diagram of an electronic device shown according to an exemplary embodiment. Detailed Description of the Preferred Embodiments

[0029] Hereinafter, the embodiments of this application will be described in more detail with reference to the drawings. Although some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand this application. It should be understood that the drawings and embodiments of this application are only for exemplary purposes and are not used to limit the protection scope of this application.

[0030] It should be understood that the various steps described in the method embodiments of the present application may be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this regard.

[0031] As used herein, the term "comprising" and its variations are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0032] It should be noted that the concepts such as "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0033] It should be noted that the modifications of "one" and "a plurality" mentioned in the present application are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0034] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0035] Extreme rainstorm disasters have posed severe challenges to power supply and grass-roots disaster response. This disaster has caused widespread power outages. Due to various reasons, power outage information cannot be reported in a timely and accurate manner, which has seriously affected the accurate assessment of the power outage areas and user conditions. As the "eyes" of power marketing, the acquisition system has been severely damaged due to the suspension of operator base stations, flooding of underground distribution rooms, etc. The online rate of terminals has dropped to as low as 63.5% at the lowest, and power outage events in more than one-third of the areas cannot be reported to the system master station in a timely manner, seriously affecting the accuracy of judging power outage areas and users. In the face of the disaster situation, how to reasonably dispatch and repair in bad weather, quickly master the power outage scope, judge the type of power failure, and accurately locate the fault location is a great test.

[0036] Meanwhile, due to the interruption of base station communication, the power outage information cannot be reported in a timely manner, further exacerbating the problems of being unable to accurately lock down the power outage area and the repair progress. The existence of these problems highlights the urgency of the insufficient emergency response ability of customer service. In the face of extreme disaster weather, it is necessary to find out the problems through in-depth analysis and comprehensive review of the incident. Only in this way can relevant technical research be carried out targeted to improve the emergency response ability of marketing customer service, further improve the efficiency of disaster response, and reduce the workload of grass-roots units. In this disaster, the stable power supply and disaster response ability of the power system are particularly crucial. Only through technical research and innovation can power supply personnel better cope with similar disasters and ensure the safety of people's lives and property.

[0037] Therefore, it is urgent to strengthen the research and application of relevant technologies to improve the efficiency and accuracy of disaster emergency response to cope with the possible disaster challenges in the future. This will also provide better support for grass-roots units, enabling them to respond to disasters more effectively and protect the lives and property interests of the people.

[0038] To solve these problems, it is necessary to find out the problem of insufficient emergency response of customer service in extreme disaster weather through the analysis and review of the incident. At present, it is urgent to carry out relevant technical research to improve the emergency response ability of marketing customer service, enhance the efficiency of disaster response, and reduce the workload of grass-roots units. In this disaster, the stable power supply and the improvement of disaster response ability of the power system are of vital importance. Only through technical research and innovation can we better cope with similar disasters and ensure the safety of people's lives and property. Therefore, strengthening the research and application of relevant technologies and improving the efficiency and accuracy of disaster emergency response are urgent problems to be solved. In the research and application of key digital technologies for power restoration in residential communities, a technical route based on "minimized precise collection + digital system calculation and recommendation" is adopted, and efforts are made to build a power outage judgment model and a power outage prediction model to further enhance the ability in customer service emergency response and strengthen the prevention and response to power supply service incidents.

[0039] First, in the research and development of power outage judgment models, various power outage-related data, including power outage scope, duration, and causes, are collected and sorted through the method of minimizing precise collection. With the computing and recommendation capabilities of digital systems, advanced data processing and analysis techniques are used to deeply mine and analyze this data to establish an accurate and reliable power outage judgment model. This model will be able to accurately evaluate the conditions of power outage areas and users based on real-time data and provide support and reference for relevant decisions. Secondly, efforts are made to construct a power outage prediction model. By analyzing historical power outage data and applying prediction algorithms, the power outage situations that may occur in the future are predicted. The prediction model will comprehensively consider various factors, such as weather conditions, equipment conditions, and electricity consumption loads, to achieve effective early warning of power outage risks. At the same time, the setting of risk warning thresholds, the optimization of warning methods, and the setting of warning carriers are studied to improve the accuracy and operability of early warning.

[0040] This application aims to solve various technical problems in the process of emergency repair and power restoration in residential communities under extreme weather conditions. Through in-depth analysis and comprehensive review of events under extreme disaster weather, the deficiencies in customer service emergency response are identified, and relevant technical research is carried out to improve the stability and response speed of the power system in disaster response.

[0041] Specifically, this application will bring the following practical significances and expected outcomes:

[0042] 1. Improve the accuracy of power outage judgment and prediction: By minimizing precise collection and digital system computing and recommendation technologies, accurate and reliable power outage judgment models and power outage prediction models are established, which can evaluate and predict the conditions of power outage areas and users in real time. This will enable power supply companies to quickly identify affected areas during disasters, accurately determine the causes of power outages, and take restoration measures in a timely manner, reducing the duration and scope of power outages.

[0043] 2. Enhance the disaster emergency response ability: Through the technology of collecting and cleaning multi-source system data and combining big data analysis methods, the accuracy of power distribution transformer power outage judgment is improved. By constructing a risk early warning and power outage prediction model, potential risks can be early warned, and the ability of the power system to respond to extreme weather can be improved, ensuring the safety of residents' lives and property.

[0044] 3. Reduce the work burden of grass-roots units: Under extreme disaster weather, the traditional way of reporting power outage information is easily affected by communication interruptions, resulting in untimely information transmission and affecting the efficiency of emergency response. By researching and applying digital power outage judgment technology, power outage data can be automatically collected and processed, reducing the pressure and errors of manual reporting and improving the work efficiency of grass-roots units in disasters.

[0045] 4. Promote technological innovation and application in the power system: This application enhances the intelligence level of the power system by researching and applying advanced data processing, machine learning, and deep learning technologies. The research results can not only be applied to the emergency repair and power restoration in residential communities but also be extended to the disaster response and fault handling in other power systems, promoting the technological progress of the entire industry.

[0046] 5. Improve customer satisfaction and company competitiveness: Through accurate power outage judgment and prediction, the needs of customers can be better met, customer problems and concerns can be responded to in a timely manner, and customer satisfaction can be improved. At the same time, enhance the ability to prevent and respond to power supply service incidents, and consolidate the leading position and competitiveness in the market.

[0047] In summary, the implementation of this application will significantly enhance the emergency response ability and service quality of the power system under extreme disaster weather, providing strong technical support for ensuring the normal life and production order of residents.

[0048] This application establishes a power outage judgment and prediction model to improve the understanding of power outages and the response speed, enabling faster emergency decision-making and deployment, and minimizing the impact of power outages; the prediction model can early warn of possible power outage events, enabling the company to make full preparations and responses in advance, and improving the ability to respond to emergencies such as natural disasters; based on accurate judgment results, the company can quickly allocate the required personnel, equipment, and resources, improve the processing efficiency, minimize the power outage duration, and reduce the impact of power outages on customers; according to accurate judgment and prediction results, the company can better meet the power supply needs and service expectations of customers, conduct targeted emergency responses and resource allocations, and significantly improve the quality of power supply services and customer satisfaction. Excellent emergency service capabilities become one of the core advantages of the company in the market competition; the research results directly support the company's key strategic goals such as enhancing emergency response capabilities and optimizing resource allocation. The improvement of technological innovation and service quality both contribute to enhancing the company's competitiveness in the market and supporting the long-term sustainable development of power enterprises.

[0049] The following details the specific implementation manners of this application in conjunction with the accompanying drawings.

[0050] Figure 1 is a flowchart of a power outage judgment processing method based on big data shown according to an exemplary embodiment, as Figure 1 shown, the method includes:

[0051] Step S101, obtain the time queue of weather monitoring data, where the weather monitoring data includes temperature value, humidity value, wind speed value, and rainfall;

[0052] Step S102: Extract time series features based on local time scales from the time queue of the weather monitoring data to obtain a sequence of local time series correlation feature vectors of the weather data;

[0053] Step S103: Optimize the feature correlation of the sequence of local time series correlation feature vectors of the weather data to obtain a sequence of optimized local time series correlation feature vectors of the weather data;

[0054] Step S104: Determine whether to generate a power outage warning prompt based on the feature significance time series aggregation information of the sequence of optimized local time series correlation feature vectors of the weather data.

[0055] To address the above technical problems, the technical concept of this application is to use artificial intelligence technology based on deep learning to perform data time series analysis on weather monitoring data, capture the local time series change characteristics of the weather data, and then through correlation optimization and time series aggregation of the time series change characteristics of the weather data in each local time domain, excavate the time series dynamic change pattern of the weather data in the global time domain, so as to perform intelligent power outage warning. In this way, the accuracy and timeliness of power outage warning can be effectively improved, which helps to detect potential power outage risks in advance, so as to take corresponding preventive measures.

[0056] Based on this, in the technical solution of this application, first, obtain the time queue of weather monitoring data, where the weather monitoring data includes temperature value, humidity value, wind speed value, and rainfall. It should be understood that the operating conditions of the power system are closely related to weather conditions, and temperature, humidity, wind speed, and rainfall are the main factors affecting the safe operation and performance of power facilities in weather conditions. Therefore, in the technical solution of this application, by monitoring the key meteorological parameters in the historical time period, the possible impact of weather changes on the power system can be understood, so as to realize intelligent power outage warning prompts.

[0057] Next, in order to convert the original weather monitoring data into a data form that is easy for machine learning models to process, this application further uses a weather data embedding matrix to perform embedding encoding on each weather monitoring data in the time queue of the weather monitoring data, so as to map the weather monitoring data into a high-dimensional semantic space, capture the context correlation between different meteorological parameters at the same time point, convert the weather monitoring data at each time point into a vector representation with rich semantic information, and obtain a time queue of weather monitoring embedding encoding vectors, thereby providing a more effective data basis for subsequent power outage warning. In the embodiment of this application, the weather data embedding matrix is trained through a Word2Vec model.

[0058] Secondly, in order to more precisely understand the dynamic change process of weather data, in the technical solution of this application, first, the time queue of the weather monitoring embedded coding vectors is divided into time series at a predetermined time scale, and the entire time series is divided into multiple local time domains. Then, a weather local time series feature catcher based on a 1D-CNN model is used to process the weather monitoring embedded coding vectors in each local time domain, so as to utilize the advantage of the 1D-CNN model in processing one-dimensional time series data. By performing a sliding window convolution operation in the time dimension, the time series change law of the weather data in each local time domain is captured, thereby obtaining a sequence of weather data local time series correlation feature vectors.

[0059] In an embodiment of this application, extracting time series features based on a local time scale from the time queue of the weather monitoring data to obtain a sequence of weather data local time series correlation feature vectors includes: using a weather data embedding matrix to perform embedding coding on each weather monitoring data in the time queue of the weather monitoring data to obtain a time queue of weather monitoring embedded coding vectors; after dividing the time queue of the weather monitoring embedded coding vectors at a predetermined time scale, inputting it into a weather local time series feature catcher based on a 1D-CNN model to obtain the sequence of the weather data local time series correlation feature vectors.

[0060] Furthermore, considering that in the global time domain, the weather data in different local time domains may have different importance. Therefore, in order to further improve the accuracy of feature expression, in the technical solution of this application, an adaptive feature optimization module based on sequence correlation semantics is introduced to process the sequence of the weather data local time series correlation feature vectors. By analyzing the semantic correlation between the weather data features in each local time domain, the feature weights are dynamically adjusted, thereby achieving a more accurate capture of the time series change pattern of the weather data. Specifically, the adaptive feature optimization module first quantifies its similarity or difference by calculating the semantic association between any two weather data local time series correlation feature vectors to understand the semantic structure inside the sequence, and generates a set of semantic association score vectors. Then, using the aggregation information of the set of semantic association score vectors as the overall context information, the association optimization factor of each weather data local time series correlation feature vector is calculated to quantitatively describe the consistency or deviation between the weather data features in each local time domain and the overall semantic structure of the sequence. Furthermore, after normalizing the association optimization factor, it is used as a weight coefficient to perform weighted optimization on the original sequence of the weather data local time series correlation feature vectors, thereby suppressing the features irrelevant to the overall semantics of the sequence and enhancing the expression of key features to obtain a sequence of weather data local time series correlation optimized feature vectors.

[0061] In one embodiment of the present application, optimizing the feature correlation of the sequence of local temporal correlation feature vectors of the weather data to obtain a sequence of optimized local temporal correlation feature vectors of the weather data includes: inputting the sequence of local temporal correlation feature vectors of the weather data into an adaptive feature optimization module based on sequence correlation semantics to obtain the sequence of optimized local temporal correlation feature vectors of the weather data.

[0062] Further, in one embodiment of the present application, inputting the sequence of local temporal correlation feature vectors of the weather data into an adaptive feature optimization module based on sequence correlation semantics to obtain the sequence of optimized local temporal correlation feature vectors of the weather data includes: calculating a semantic association score vector between any two local temporal correlation feature vectors in the sequence of local temporal correlation feature vectors of the weather data to obtain a sequence of local temporal semantic association score vectors; calculating a mean vector of the sequence of local temporal semantic association score vectors to obtain a globally representative vector of sequence endogenous correlation; based on the globally representative vector of sequence endogenous correlation, calculating an association optimization factor for each local temporal correlation feature vector in the sequence of local temporal correlation feature vectors of the weather data to obtain a sequence of association optimization factors; inputting the sequence of association optimization factors into an activation function to obtain a sequence of association optimization weight factors; using each association optimization weight factor in the sequence of association optimization weight factors as a weight to respectively weight each local temporal correlation feature vector in the sequence of local temporal correlation feature vectors of the weather data to obtain the sequence of optimized local temporal correlation feature vectors of the weather data.

[0063] Further, in one embodiment of the present application, calculating a semantic association score vector between any two local temporal correlation feature vectors in the sequence of local temporal correlation feature vectors of the weather data to obtain a sequence of local temporal semantic association score vectors includes: concatenating any two local temporal correlation feature vectors in the sequence of local temporal correlation feature vectors of the weather data, multiplying by a weight coefficient matrix, and then performing a dot product with a bias vector to obtain the local temporal semantic association score vector.

[0064] Further, in an embodiment of the present application, based on the sequence endogenous correlation global representation vector, calculating an association optimization factor for each weather data local temporal association feature vector in the sequence of the weather data local temporal association feature vectors to obtain a sequence of association optimization factors, including: multiplying the weather data local temporal association feature vector and the sequence endogenous correlation global representation vector by different weight coefficient vectors respectively and then performing an addition operation to obtain a semantic correlation coefficient; adding a bias parameter to the semantic correlation coefficient and then passing it through a sigmoid activation function to obtain the association optimization factor.

[0065] Specifically, the following semantic feature association optimization formula is used to process the sequence of the weather data local temporal association feature vectors to obtain a sequence of the weather data local temporal association optimized feature vectors, where the semantic feature association optimization formula is:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Wherein, represents the sequence of the weather data local temporal association feature vectors, , , , and respectively represent the first, second, th, th, and th weather data local temporal association feature vectors in the sequence of the weather data local temporal association feature vectors, is the value of the number of feature vectors in the sequence of the weather data local temporal association feature vectors, represents a concatenation operation, is a weight coefficient matrix, is a bias vector, is the th weather data local temporal semantic correlation score vector in the sequence of the weather data local temporal semantic correlation score vectors, is the value of the number of vectors in the sequence of the weather data local temporal semantic correlation score vectors, is the sequence endogenous correlation global representation vector, and respectively represent different weight coefficient vectors, is a bias parameter, is the sigmoid activation function, is the normalized exponential function, is the th associated optimization factor, represents the th local time-series correlation optimization feature vector of weather data.

[0072] In an embodiment of the present application, based on the feature significance time-series aggregation information of the sequence of the local time-series correlation optimization feature vectors of the weather data, it is determined whether to generate a power outage warning prompt, including: inputting the sequence of the local time-series correlation optimization feature vectors of the weather data into a node time-series aggregation response network modulated by feature significance to obtain a weather data time-series significant aggregation representation vector; inputting the weather data time-series significant aggregation representation vector into a judgment result generator based on a classifier to obtain a judgment result, and the judgment result is used to indicate whether to generate the power outage warning prompt.

[0073] Then, in order to extract the time-series dynamic change pattern of the weather data in the global time domain, further time-series information aggregation processing is performed on the sequence of the local time-series correlation optimization feature vectors of the weather data. Considering that in the analysis process of time-series data, the influence of early weather monitoring data on the current weather state may gradually weaken over time, while recent weather monitoring data has a greater impact on the current weather state. Therefore, in the technical solution of the present application, a node time-series aggregation response network modulated by feature significance is introduced to process the sequence of the local time-series correlation optimization feature vectors of the weather data, and a feature significance description factor is calculated based on the mean and variance of each local time-series correlation optimization feature vector of the weather data to quantitatively represent its feature importance. At the same time, the feature significance is dynamically modulated according to the feature space distance span between each local time-domain feature and the current local time-domain feature, so that the weather information closer to the current local time-domain in the feature space obtains a higher weight, facilitating more accurate time-series information aggregation. Then, an activation function and a gating mechanism are further used to screen the modulated description factors, and the screened description factors are used as weights to perform weighted summation on the sequence of the local time-series correlation optimization feature vectors of the weather data to obtain a weather data time-series significant aggregation representation vector with time correlation. In this way, the time-series dynamic change pattern of the weather data in the global time domain can be effectively extracted, providing strong data support for intelligent power outage warning.

[0074] In one embodiment of the present application, inputting the sequence of the locally temporally correlated and optimized feature vectors of the weather data into the node temporal aggregation response network with feature saliency modulation to obtain the temporally significant aggregation representation vector of the weather data includes: calculating the feature saliency description factors of each of the locally temporally correlated and optimized feature vectors of the weather data in the sequence; using the last locally temporally correlated and optimized feature vector of the weather data in the sequence as the current locally temporally correlated and optimized feature vector of the weather data, and constructing the feature saliency attenuation factors of each of the other locally temporally correlated and optimized feature vectors of the weather data in the sequence based on the distance span between each of the other locally temporally correlated and optimized feature vectors of the weather data in the sequence and the current locally temporally correlated and optimized feature vector of the weather data; calculating the product of the feature saliency attenuation factors of each of the other locally temporally correlated and optimized feature vectors of the weather data in the sequence and their feature saliency description factors to obtain a sequence of feature saliency attenuation description factors; inputting the sequence of the feature saliency attenuation description factors into a gated mask module to obtain a sequence of feature saliency attenuation weight factors; and calculating the weighted sum of the sequence of the locally temporally correlated and optimized feature vectors of the weather data based on the sequence of the feature saliency attenuation weight factors to obtain the temporally significant aggregation representation vector of the weather data.

[0075] Further, in one embodiment of the present application, calculating the feature saliency description factors of each of the locally temporally correlated and optimized feature vectors of the weather data in the sequence includes: calculating the expected value of the fourth power of the difference between each eigenvalue in the locally temporally correlated and optimized feature vector of the weather data and its feature mean value, and dividing the expected value by the square of the feature variance of the locally temporally correlated and optimized feature vector of the weather data to obtain the feature saliency description factor.

[0076] Furthermore, in one embodiment of the present application, constructing the feature saliency attenuation factors of each of the other locally temporally correlated and optimized feature vectors of the weather data based on the distance span between each of the other locally temporally correlated and optimized feature vectors of the weather data in the sequence and the current locally temporally correlated and optimized feature vector of the weather data includes: calculating the difference between the maximum eigenvalue of the current locally temporally correlated and optimized feature vector of the weather data and the maximum eigenvalues of each of the other locally temporally correlated and optimized feature vectors of the weather data in the sequence, and then dividing the difference by the number of feature vectors separated between them to obtain the feature saliency attenuation factors of each of the other locally temporally correlated and optimized feature vectors of the weather data.

[0077] Specifically, the following feature significance attenuation-guided fusion formula is used to process the sequence of the optimized feature vectors of the local time series correlation of the weather data to obtain the time series significant aggregation representation vector of the weather data, where the feature significance attenuation-guided fusion formula is:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Among them, represents the sequence of the optimized feature vectors of the local time series correlation of the weather data, the value of is the number of feature vectors in the sequence of the optimized feature vectors of the local time series correlation of the weather data, 、 、 、 and respectively represent the first, second, th, th, and the current optimized feature vectors of the local time series correlation of the weather data, represents the maximum eigenvalue of the feature vector, represents the th th eigenvalue of the and respectively represent the feature mean and the square of the feature variance of the th optimized feature vector of the local time series correlation of the weather data, represents the fourth-order central moment of the th optimized feature vector of the local time series correlation of the weather data, that is, the expected value of the fourth power of the difference between each eigenvalue in the th optimized feature vector of the local time series correlation of the weather data and its feature mean, , represents the feature significance attenuation factor of the th optimized feature vector of the local time series correlation of the weather data, represents the a feature significance attenuation description factor indicating the th normalized feature significance attenuation description factor indicating the th feature significance attenuation weight factor is a preset threshold indicating masking processing indicating the significantly aggregated representation vector of the weather data time series

[0086] Finally, further input the significantly aggregated representation vector of the weather data time series into a classifier-based judgment result generator to obtain a judgment result, and the judgment result is used to indicate whether a power outage warning prompt is generated

[0087] In a preferred example, inputting the significantly aggregated representation vector of the weather data time series through a classifier-based judgment result generator to obtain a judgment result includes:

[0088] Determine the maximum feature value and the minimum feature value of the significantly aggregated weather data time series of the significantly aggregated representation vector of the weather data time series, and calculate the mean and standard deviation of the significantly aggregated weather data time series of the feature set of the significantly aggregated representation vector of the weather data time series;

[0089] Calculate the quotient of the mean of the significantly aggregated weather data time series and the standard deviation of the significantly aggregated weather data time series to obtain the significantly aggregated statistical standardization value of the weather data time series;

[0090] Calculate the reciprocal of each feature value of the significantly aggregated representation vector of the weather data time series, and after dot-multiplying with the difference between the maximum feature value and the minimum feature value of the significantly aggregated weather data time series, perform dot-subtraction with the significantly aggregated statistical standardization value of the weather data time series to obtain the significantly aggregated distribution approximation vector of the weather data time series;

[0091] Calculate the exponential function with the natural constant as the base and each feature value of the significantly aggregated distribution approximation vector of the weather data time series as the exponent, and perform dot-addition with the significantly aggregated statistical standardization value of the weather data time series to obtain the significantly aggregated probability approximation vector of the weather data time series;

[0092] Calculate the base-two logarithm of the absolute value of each feature value of the significantly aggregated probability approximation vector of the weather data time series to obtain an optimized significantly aggregated representation vector of the weather data time series; and

[0093] Input the optimized significantly aggregated representation vector of the weather data time series through a classifier-based judgment result generator to obtain a judgment result

[0094] Among them, the time-series significant aggregation representation vector of the weather data, denoted as The optimization of is expressed as:

[0095]

[0096] , , and are the feature sets of the time-series significant aggregation representation vector of the weather data The mean, standard deviation, minimum value and maximum value of, and is the logarithm to the base 2, is the time-series significant aggregation representation vector of the weather data, is the reciprocal of each eigenvalue of the time-series significant aggregation representation vector of the weather data, is to calculate the exponential function with the base of the natural constant and each eigenvalue of the vector as the exponent, is the optimized time-series significant aggregation representation vector of the weather data, is addition by position points, is multiplication by position points, is subtraction by position points.

[0097] Here, in the preferred example, considering that the sequences of the optimized feature vectors of the local time-series correlation of the weather data respectively represent the local time-domain context time-series semantic correlation features of the weather monitoring data, when performing node time-series aggregation response based on feature significance modulation, the differences in the time-series node energy distributions within each local time-domain will have different aggregation response weights based on the node time-series aggregation response, so that the time-series significant aggregation representation vector of the weather data will also have a diverse set of aggregation response feature expression distributions. Therefore, it is expected to improve the balance between the regression mapping accuracy and integrity when the time-series significant aggregation representation vector of the weather data is subjected to class regression through a classifier-based judgment result generator, thereby improving the accuracy of the obtained judgment results.

[0098] Therefore, by performing random statistical standardization on the diverse feature set of the time-series significant aggregation representation vector of the weather data, an approximation of the standardized continuous probability density distribution is carried out for the response hypothesis test of the confidence space constructed based on the overall eigenvalue of the time-series significant aggregation representation vector of the weather data with respect to each eigenvalue of the time-series significant aggregation representation vector of the weather data, thereby establishing the target reachability from the discretized feature distribution of the time-series significant aggregation representation vector of the weather data to the unified regression target, so as to achieve the balanced executability between mapping accuracy and mapping integrity in the class regression process based on the discretized feature distribution of the time-series significant aggregation representation vector of the weather data, and improve the accuracy of the judgment result obtained by the judgment result generator based on the classifier for the time-series significant aggregation representation vector of the weather data.

[0099] In summary, adopting the above solution, using the artificial intelligence technology based on deep learning to perform data time-series analysis on weather monitoring data, capturing the local time-series change characteristics of the weather data, and then through the correlation optimization and time-series aggregation of the time-series change characteristics of the weather data in each local time domain, mining the time-series dynamic change pattern of the weather data in the global time domain, so as to perform intelligent power outage warning. In this way, the accuracy and timeliness of the power outage warning can be effectively improved, which helps to discover potential power outage risks in advance, so as to take corresponding preventive measures.

[0100] Figure 2 is a block diagram of a power outage judgment processing system based on big data shown according to an exemplary embodiment. As Figure 2 shown, the system 200 includes:

[0101] A weather monitoring data acquisition module 201, configured to acquire the time queue of weather monitoring data, where the weather monitoring data includes temperature value, humidity value, wind speed value, and rainfall;

[0102] A time-series feature extraction module 202, configured to perform time-series feature extraction based on a local time scale on the time queue of the weather monitoring data to obtain a sequence of local time-series correlation feature vectors of the weather data;

[0103] A feature correlation optimization module 203, configured to perform feature correlation optimization on the sequence of local time-series correlation feature vectors of the weather data to obtain a sequence of local time-series correlation optimized feature vectors of the weather data;

[0104] A power outage warning prompt determination module 204, configured to determine whether to generate a power outage warning prompt based on the feature significance time-series aggregation information of the sequence of local time-series correlation optimized feature vectors of the weather data.

[0105] It should be understood that through big data technology, the efficient prediction of risk warning and system power outage and the accurate analysis of the bearing capacity threshold are realized, such asFigure 3 As shown below. First, the data acquisition module is responsible for collecting various types of data from the system, including real-time operation data, historical fault records, and environmental information. These data are obtained with high efficiency and transmitted to the data processing center in an efficient manner. Subsequently, in the data processing module, the collected raw data undergoes preprocessing, including data cleaning, formatting, and standardization, to ensure data quality and prepare for subsequent analysis. Next, machine learning and data mining techniques are used to analyze the processed data and extract key features. It comprehensively considers various factors, such as fault characteristics, meteorological factors, historical fault data, etc., to determine the specific type and location of the fault. The entire system framework collaborates closely. It is a process of applying various technologies and tools to process, analyze, and mine large-scale datasets. In the power industry, the generation of massive data involves multiple aspects such as power grid monitoring, power production, and energy consumption. In this project, the application of big data analysis methods can effectively process and analyze the massive data in the power industry and play the following key roles.

[0106] Improve decision-making assistance capabilities. Through big data analysis, the data in the power industry can be deeply mined and analyzed from multiple dimensions and perspectives, providing accurate and comprehensive information support for decision-makers to make scientific and wise decisions. And it can monitor the operating status of the power system in real time, detect and predict potential faults or anomalies in a timely manner. By analyzing the real-time data stream, the health status of power equipment can be predicted in advance, and preventive maintenance measures can be taken to reduce the risk of power outages.

[0107] Insight discovery capabilities. Through the application of big data technology, it reveals potential laws, trends, and correlation relationships in the data of the power industry, discovers business problems, management loopholes, and improvement opportunities, and provides insights and innovative ideas. It is a process of exploring and analyzing to find patterns, trends, and correlations in the data. Common data mining techniques include clustering, classification, association rule mining, etc.

[0108] Improve the ability to optimize business processes. By analyzing the data in the power industry, bottlenecks, inefficiencies, and problems in the business processes are discovered, and optimization suggestions and solutions are provided to achieve lean management and the optimization of business processes. The application of big data analysis in the power industry can improve the intelligent level of the power system, improve operating efficiency, reduce energy waste, enhance the stability and security of the system, and promote the power industry to develop towards a more sustainable and intelligent direction.

[0109] Furthermore, as Figure 4As shown, the flowchart shows how to determine whether to issue a warning or identify a power outage by checking the node fault information layer by layer when the power failure warning or power failure judgment condition is triggered. The process starts when the power failure warning or power failure judgment condition is triggered. First, the system checks whether there is fault information of the bottom-level (customer) node. If it exists, it continues to call the fault information vector P^' of the bottom-level child node; if it does not exist, the process turns to the failure warning of the lower-level child node fault. Next, the system will obtain the fault information P of this node. Then check whether the fault information is zero. If P is zero, the system will obtain the fault information vector P of the child node of this layer corresponding to the parent node, and calculate the fault information P^' of the parent node. Then check whether the parent node fault information P^' is zero. If P^' is zero, the parent node is judged to be faulty; if P^' is not zero, the node is judged to be faulty. If the fault information P of this node is not zero, the node is directly judged to be faulty. Finally, the system checks whether there is root node (trunk) fault information. If it exists, the fault analysis result is output; if it does not exist, the process ends. In summary, the flowchart details the decision-making process of determining whether to issue a warning or identify a power outage by examining node fault information layer by layer, from the underlying customer nodes to the root node.

[0110] In this application, monitoring and early warning are carried out for six aspects, including power outage user changes, work order changes, call traffic changes, power outage event changes, terminal offline changes and weather changes. By monitoring indicator data and setting change thresholds, early warning reminders are issued when indicator data reaches or exceeds the threshold, and the number of power outage users is predicted, and corresponding early warning signals are issued when the early warning threshold is reached.

[0111] Monitoring and early warning of six aspects, including power outage user changes, work order changes, call traffic changes, power outage event changes, terminal offline changes and weather changes, can be achieved by using monitoring indicator data and set change thresholds. The following is an expanded description of the monitoring and early warning methods for each aspect:

[0112] 1. Monitoring and early warning of abnormal changes in power outage users: Monitoring indicator data: Monitor the number, proportion or change trend of power outage users. Abnormal change threshold setting: Set the threshold of the number or proportion of power outage users. If the threshold is exceeded, it is considered that there is an abnormal change in power outage users. Early warning reminder: When the number or proportion of power outage users reaches or exceeds the set threshold, the system can send an early warning reminder to notify relevant personnel to handle and investigate.

[0113] 2. Work order movement monitoring and warning: Monitoring indicator data: Monitor the number of work orders, processing time, or abnormal situations. Movement threshold setting: Set the threshold for the number of work orders or processing time. If the threshold is exceeded or an abnormal situation occurs, it is considered that there is a work order movement. Warning reminder: When the number of work orders exceeds the set threshold, the processing time is delayed, or an abnormal situation occurs, the system can send a warning reminder to notify relevant personnel to process and follow up on the work orders in a timely manner.

[0114] 3. Call traffic movement monitoring and warning: Monitoring indicator data: Monitor the call volume, call quality, or abnormal situations. Movement threshold setting: Set the threshold for the call volume or call quality. If the threshold is exceeded or an abnormal situation occurs, it is considered that there is a call traffic movement. Warning reminder: When the call volume exceeds the set threshold, the call quality deteriorates, or an abnormal situation occurs, the system can send a warning reminder to notify relevant personnel to conduct investigations and handle them.

[0115] 4. Power outage event movement monitoring and warning: Monitoring indicator data: Monitor the number of power outage events, duration, or abnormal situations. Movement threshold setting: Set the threshold for the number of power outage events or duration. If the threshold is exceeded or an abnormal situation occurs, it is considered that there is a power outage event movement. Warning reminder: When the number of power outage events exceeds the set threshold, the duration is extended, or an abnormal situation occurs, the system can send a warning reminder to notify relevant personnel to take measures in a timely manner and conduct fault troubleshooting.

[0116] 5. Terminal offline movement monitoring and warning: Monitoring indicator data: Monitor the online status of terminal devices, communication abnormalities, or changes in the number of terminals. Movement threshold setting: Set the threshold for terminal offline or communication abnormalities. If the threshold is exceeded or an abnormal situation occurs, it is considered that there is a terminal offline movement. Warning reminder: When the number of offline terminal devices exceeds the set threshold, communication abnormalities, or other abnormal situations occur, the system can send a warning reminder to notify relevant personnel to conduct repairs and maintenance.

[0117] 6. Weather movement monitoring and warning: Monitoring indicator data: Monitor weather data such as temperature, wind speed, rainfall, etc. Movement threshold setting: Set the corresponding weather abnormality threshold according to the impact of the weather on the power supply system. Warning reminder: When the weather data reaches or exceeds the set abnormality threshold, the system can send a warning reminder to notify relevant personnel to pay attention to weather abnormalities that may affect the power supply system.

[0118] Figure 5 shows the relationship between different weather types (represented by weather codes) and the number of power outage users. The chart type is a box plot, which is used to display the distribution and statistical characteristics of the data. For the relationship between different weather types and the number of power outage users, the X-axis of the chart represents the weather code, ranging from 0 to 12, and different values represent different weather types. The Y-axis represents the number of power outage users, ranging from 0 to 800,000, reflecting the distribution of the number of power outage users under different weather types.

[0119] Each weather code corresponds to a box plot. The upper and lower edges of the box represent the first quartile (Q1) and the third quartile (Q3) of the data respectively. The median line inside the box represents the median (Median) of the data. The whiskers above and below the box extend to the minimum and maximum values of the data (excluding outliers). The dots in the figure represent outliers, which are points outside the whisker range.

[0120] It can be seen from the chart that the distribution of the number of power outage users under different weather types is different. For example, when the weather codes are 4 and 7, the median of the number of power outage users is higher and the distribution range is wider, indicating that these weather types have a greater impact on power outages. When the weather codes are 11 and 12, the number of power outage users is less and the distribution is more concentrated, indicating that these weather types have a smaller impact on power outages.

[0121] Generally speaking, this chart clearly shows the impact of different weather conditions on the number of power outage users, which helps to analyze and predict power outages under different weather conditions, and then formulate corresponding countermeasures.

[0122] The following refers to Figure 6 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present application. The terminal devices in the embodiments of the present application may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0123] As Figure 6As shown, the electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0124] Generally, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 6 the electronic device 600 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had.

[0125] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart may be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above functions defined in the method of the embodiment of the present application are executed.

[0126] It should be noted that the above-mentioned computer-readable medium in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In this application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0127] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0128] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device.

[0129] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0130] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the module itself in some cases. For example, the test parameter acquisition module can also be described as "the module for acquiring the device test parameters corresponding to the target device".

[0132] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on a chip (SOC), complex programmable logic devices (CPLD), and so on.

[0133] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, 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.

[0134] In the application scenario of the power outage judgment and processing method based on big data, first, obtain the time queue of weather monitoring data; then, input the obtained time queue of weather monitoring data into a server deployed with a power outage judgment and processing algorithm based on big data, where the server can process the time queue of weather monitoring data based on the power outage judgment and processing algorithm based on big data to determine whether to generate a power outage warning prompt.

[0135] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the present application.

[0136] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present application. Certain features described in the context of separate embodiments can also be implemented combinatorially in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0137] Although the present subject matter has been described in language specific to structural features and / or methodological logical acts, it is to be understood that the subject matter defined is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms of implementation. Regarding the apparatus in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated herein.

Claims

1. A power outage analysis and processing method based on big data, characterized in that: include: Obtaining a time queue of weather monitoring data, wherein the weather monitoring data includes temperature value, humidity value, wind speed value and rainfall; Extracting time series features based on a local time scale from the time queue of the weather monitoring data to obtain a sequence of local time series correlation feature vectors of the weather data; The sequence of the local time series correlation feature vectors of the weather data is subjected to feature correlation optimization to obtain a sequence of the local time series correlation optimized feature vectors of the weather data, wherein the sequence of the local time series correlation feature vectors of the weather data is input into an adaptive feature optimization module based on sequence correlation semantics to obtain the sequence of the local time series correlation optimized feature vectors of the weather data, specifically comprising: Calculating a semantic association score vector between any two weather data local time series association feature vectors in the sequence of weather data local time series association feature vectors to obtain a sequence of weather data local time series semantic association score vectors; Calculating the mean vector of the sequence of local temporal semantic association score vectors of the weather data to obtain a global representation vector of the sequence endogenous correlation; Based on the global representation vector of the endogenous correlation of the sequence, the correlation optimization factor of each local time series correlation feature vector of weather data in the sequence of the local time series correlation feature vector of weather data is calculated to obtain a sequence of correlation optimization factors, wherein the local time series correlation feature vector of weather data and the global representation vector of the endogenous correlation of the sequence are multiplied by different weight coefficient vectors respectively and then added to obtain a semantic correlation coefficient, and the semantic correlation coefficient is added with a bias parameter and then activated by a sigmoid function to obtain the correlation optimization factor; The sequence of the associated optimization factors is input into Activation function to obtain a sequence of associated optimization weight factors; Using each association optimization weight factor in the sequence of association optimization weight factors as a weight, weighting each weather data local time series association feature vector in the sequence of weather data local time series association feature vectors respectively to obtain the sequence of weather data local time series association optimization feature vectors; Determining whether to generate a power outage warning prompt based on the characteristic significance time series aggregation information of the sequence of the local time series association optimization feature vector of the weather data specifically includes: The sequence of the local time series association optimization feature vectors of weather data is input into a node time series aggregation response network modulated by feature significance to obtain a weather data time series significant aggregation representation vector, specifically including: calculating the feature significance description factor of each local time series association optimization feature vector of weather data in the sequence of the local time series association optimization feature vector of weather data, wherein the expected value of the fourth power of the difference between each eigenvalue in the local time series association optimization feature vector of weather data and its eigenmean is calculated, and the expected value is divided by the square of the eigenvariance of the local time series association optimization feature vector of weather data to obtain the feature significance description factor; the last local time series association optimization feature vector of weather data in the sequence of the local time series association optimization feature vector of weather data is used as the current local time series association optimization feature vector of weather data, and based on the distance span between each other local time series association optimization feature vector of weather data in the sequence of the local time series association optimization feature vector of weather data and the current local time series association optimization feature vector of weather data, Constructing the characteristic significance attenuation factors of the other local time series association optimization feature vectors of weather data, wherein the difference between the maximum eigenvalue of the local time series association optimization feature vector of the current weather data and the maximum eigenvalue of the local time series association optimization feature vector of other weather data in the sequence of the local time series association optimization feature vector of weather data is calculated, and then the difference is divided by the number of eigenvectors separated therebetween to obtain the characteristic significance attenuation factors of the other local time series association optimization feature vectors of weather data; calculating the product between the characteristic significance attenuation factors of the other local time series association optimization feature vectors of weather data and their characteristic significance description factors to obtain a sequence of characteristic significance attenuation description factors; inputting the sequence of characteristic significance attenuation description factors into a gated mask module to obtain a sequence of characteristic significance attenuation weight factors; based on the sequence of characteristic significance attenuation weight factors, calculating the weighted sum of the sequence of the local time series association optimization feature vectors of weather data to obtain the time series significant aggregation representation vector of weather data; The weather data time series significant aggregation representation vector is input into a classifier-based analysis and judgment result generator to obtain an analysis and judgment result, and the analysis and judgment result is used to indicate whether the power outage warning prompt is generated.

2. The method for analyzing and processing power outages based on big data according to claim 1 is characterized in that: Extracting time series features based on a local time scale from the time queue of the weather monitoring data to obtain a sequence of local time series correlation feature vectors of the weather data includes: Using a weather data embedding matrix, each weather monitoring data in the time queue of the weather monitoring data is embedded and encoded to obtain a time queue of weather monitoring embedded coding vectors; The time queue of the weather monitoring embedded coding vector is divided into a predetermined time scale and then input into a weather local time series feature capturer based on a 1D-CNN model to obtain a sequence of local time series associated feature vectors of the weather data.

3. The method for analyzing and processing power outages based on big data according to claim 2 is characterized in that: Calculating the semantic association score vector between any two weather data local time series association feature vectors in the sequence of weather data local time series association feature vectors to obtain a sequence of weather data local time series semantic association score vectors, including: Any two local temporal series associated feature vectors of weather data in the sequence of local temporal series associated feature vectors of weather data are cascaded and multiplied by a weight coefficient matrix, and then dotted with a bias vector to obtain the local temporal series semantic association score vector of weather data.

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