Power distribution network structure dynamic deduction method and system based on time sequence dynamic analysis

Abstract

The invention relates to the technical field of power systems, and provides a power distribution network structure dynamic deduction method and system based on time sequence dynamic analysis, and the method comprises the steps: carrying out the embedded coding and principal component time sequence aggregation of time queues of power generation side data, power grid side data and user side data through employing a data processing algorithm based on deep learning; and intelligently obtaining a short-term prediction value of carbon emission according to the time-space significant joint representation among the power generation side data feature principal component time sequence aggregation representation, the power grid side data feature principal component time sequence aggregation representation and the user side data feature principal component time sequence aggregation representation. Therefore, by integrating the multi-source data of the power generation side, the power grid side and the user side, the complex time sequence dynamic relationship in the power distribution network can be captured more accurately, so that the prediction of the carbon emission is more accurate, and the real-time performance, the comprehensiveness and the intelligent degree of the carbon emission prediction are remarkably improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and more specifically, to a method and system for dynamically simulating a distribution network structure based on time series dynamic analysis. Background Art

[0002] The distribution network plays a vital role in the power system. It transmits high-voltage electricity generated by power plants to low-voltage power grids and finally distributes it to households and commercial users. In the power industry, the management of carbon emissions is crucial to optimize the allocation of power generation resources and reduce environmental impact. Therefore, by accurately predicting carbon emissions, power generation resources can be planned more effectively, especially increasing the proportion of renewable energy, thereby promoting the sustainable use of green energy.

[0003] Traditional predictions of carbon emissions from distribution networks often only consider data from a single source, such as power generation or electricity consumption. However, modern power systems involve data from multiple levels, including the power generation side, the grid side, and the user side. There are complex interactions between these data. For example, the output of renewable energy on the power generation side is affected by weather conditions, while electricity consumption on the user side fluctuates over time and seasons. Traditional prediction methods fail to fully utilize the comprehensive information of these multi-source data, resulting in incomplete and inaccurate prediction results. In addition, traditional methods are usually based on static analysis of historical data and cannot reflect current dynamic changes in real time. The supply and demand relationship and operating status of the distribution network are changing all the time, and traditional prediction methods cannot capture these time series changes in a timely manner due to the lack of a real-time data update mechanism, resulting in delayed and inaccurate prediction results.

[0004] Therefore, a dynamic deduction scheme for distribution network structure based on time series dynamic analysis is expected. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present application provides a method and system for dynamically simulating the structure of a distribution network based on time series dynamic analysis.

[0006] According to one aspect of the present application, a method for dynamic deduction of distribution network structure based on time series dynamic analysis is provided, which includes: Obtaining a time queue for power generation side data, a time queue for grid side data, and a time queue for user side data; Embedding and encoding the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data respectively to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector; Respectively perform dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: extracting the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector, and then perform principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; perform graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector; Based on the spatiotemporal significant joint representation of multi-source factors of the power grid among the principal component time series aggregation representation vector of the power generation side data features, the principal component time series aggregation representation vector of the power grid side data features and the principal component time series aggregation representation vector of the user side data features, a short-term prediction value of carbon emissions is obtained.

[0007] In the above-mentioned method for dynamic deduction of distribution network structure based on dynamic analysis of time series, a time queue of power generation side data, a time queue of power grid side data and a time queue of user side data are obtained, including: obtaining the time queue of the power generation side data, the power generation side data includes the power generation of renewable energy and the power generation of traditional energy; obtaining the time queue of the power grid side data, the power grid side data includes line load, substation operating status and voltage level; obtaining the time queue of the user side data, wherein the user side data includes electricity demand, electricity load and electricity consumption behavior.

[0008] In the above-mentioned method for dynamic deduction of distribution network structure based on dynamic analysis of time series, the time queue of the generation side data, the time queue of the grid side data and the time queue of the user side data are respectively embedded and encoded to obtain the time queue of the generation side data embedded coding vector, the time queue of the grid side data embedded coding vector and the time queue of the user side data embedded coding vector, including: using the generation side data embedding encoder to embed coding each generation side data in the time queue of the generation side data to obtain the time queue of the generation side data embedded coding vector; using the grid side data embedding encoder to embed coding each grid side data in the time queue of the grid side data to obtain the time queue of the grid side data embedded coding vector; using the user side data embedding encoder to embed coding each user side data in the time queue of the user side data to obtain the time queue of the user side data embedded coding vector.

[0009] In the above-mentioned method for dynamic deduction of distribution network structure based on dynamic analysis of time series, the sequence of current generation side data embedded coding vector and historical generation side data embedded coding vector is extracted from the time queue of the generation side data embedded coding vector, and then principal component extraction is performed to obtain the sequence of current generation side data principal component representation vector and historical generation side data principal component representation vector, including: extracting the current generation side data embedded coding vector from the time queue of the generation side data embedded coding vector, and performing principal component extraction based on eigenvalues ​​on the current generation side data embedded coding vector to obtain the current generation side data principal component representation vector; defining other generation side data embedded coding vectors in the time queue of the generation side data embedded coding vector as historical generation side data embedded coding vectors to obtain the sequence of historical generation side data embedded coding vectors, and performing principal component extraction based on eigenvalues ​​on each historical generation side data embedded coding vector in the sequence of historical generation side data embedded coding vectors to obtain the sequence of historical generation side data principal component representation vectors.

[0010] In the above-mentioned distribution network structure dynamic deduction method based on time series dynamic analysis, the principal component propagation aggregation of the current power generation side data principal component representation vector and the sequence of the historical power generation side data principal component representation vector is performed to obtain the power generation side data feature principal component time series aggregation representation vector, including: calculating the time series propagation attenuation entropy absolute factor of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector to obtain the sequence of the power generation side data time series propagation attenuation entropy absolute factor; calculating the time series propagation attenuation entropy absolute factor of the power generation side data based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector; calculating the time series propagation attenuation entropy absolute factor of the power generation side data principal component representation vector based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector. Each absolute factor of the time series propagation attenuation entropy of the power generation side data in the sequence of factors is modulated in the time dimension to obtain a sequence of the time series span modulation propagation attenuation entropy factors of the power generation side data; the sequence of the time series span modulation propagation attenuation entropy factors of the power generation side data is input into the information transmission screening module based on the gating function to obtain a sequence of the time series span modulation propagation attenuation weights of the power generation side data; based on the sequence of the time series span modulation propagation attenuation weights of the power generation side data, the weighted sum of the sequence of the principal component representation vectors of the historical power generation side data is calculated to obtain the principal component significant transfer aggregation representation vector of the historical power generation side data; the positional sum of the principal component significant transfer aggregation representation vector of the historical power generation side data and the principal component representation vector of the current power generation side data is calculated to obtain the principal component time series aggregation representation vector of the feature power side data.

[0011] In the above-mentioned method for dynamic deduction of distribution network structure based on time series dynamic analysis, the absolute factors of time series propagation attenuation entropy of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector are calculated to obtain a sequence of absolute factors of time series propagation attenuation entropy of power generation side data, including: dividing the eigenvalues ​​of the corresponding positions of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector to obtain the power generation side data time series characteristic principal component attenuation vector; calculating the logarithmic function value with base two of the absolute value of each eigenvalue of the power generation side data time series characteristic principal component attenuation vector to obtain the power generation side data time series characteristic principal component attenuation logarithmic vector; calculating the positional point product of the historical power generation side data principal component representation vector and the power generation side data time series characteristic principal component attenuation logarithmic vector, and performing positional multiplication on the obtained point product vector to obtain the power generation side data time series propagation attenuation value; calculating the exponential function of the power generation side data time series propagation attenuation value with base natural constant e as the exponent to obtain the power generation side data time series propagation attenuation absolute factor of entropy.

[0012] In the above-mentioned method for dynamic deduction of distribution network structure based on dynamic analysis of time series, each absolute factor of the time series propagation attenuation entropy of the power generation side data in the sequence of absolute factors of the time series propagation attenuation entropy of the power generation side data is modulated in the time dimension based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector to obtain a sequence of propagation attenuation entropy factors of the time series span modulation of the power generation side data, including: subtracting the timestamp of the current power generation side data principal component representation vector from the timestamp of the historical power generation side data principal component representation vector and rounding down to obtain the time span value of the power generation side data; calculating an exponential function with the natural constant e as the base and the time span value of the power generation side data as the exponent to obtain the time span modulation value of the power generation side data; dividing the absolute factor of the time series propagation attenuation entropy of the power generation side data corresponding to the historical power generation side data principal component representation vector by the time span modulation value of the power generation side data to obtain the time span modulation propagation attenuation entropy factor of the power generation side data.

[0013] In the above-mentioned method for dynamic deduction of distribution network structure based on dynamic timing analysis, the sequence of the generation side data timing span modulation propagation attenuation entropy factors is input into the information transmission screening module based on the gating function to obtain the sequence of the generation side data timing span modulation propagation attenuation weights, including: comparing each generation side data timing span modulation propagation attenuation entropy factor in the sequence of the generation side data timing span modulation propagation attenuation entropy factors with a predetermined threshold to obtain the sequence of the generation side data timing span modulation propagation attenuation weights; wherein, in response to the generation side data timing span modulation propagation attenuation entropy factor being greater than the predetermined threshold, the generation side data timing span modulation propagation attenuation entropy factor greater than the predetermined threshold is input into the sigmoid function; in response to the generation side data timing span modulation propagation attenuation entropy factor being less than or equal to the predetermined threshold, the generation side data timing span modulation propagation attenuation entropy factor less than or equal to the predetermined threshold is set to zero.

[0014] In the above-mentioned dynamic deduction method of distribution network structure based on time series dynamic analysis, the short-term prediction value of carbon emissions is obtained based on the spatiotemporal significant joint representation of multi-source factors of the power grid between the time series aggregation representation vector of the main component of the power generation side data features, the time series aggregation representation vector of the main component of the power grid side data features and the time series aggregation representation vector of the main component of the user side data features, including: inputting the time series aggregation representation vector of the main component of the power generation side data features, the time series aggregation representation vector of the main component of the power grid side data features and the time series aggregation representation vector of the main component of the user side data features into a spatial element joint deep analyzer based on a multi-layer perceptron model to obtain the spatiotemporal significant joint representation vector of multi-source factors of the power grid as the spatiotemporal significant joint representation of multi-source factors of the power grid; and obtaining the short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation of multi-source factors of the power grid.

[0015] In the above-mentioned dynamic deduction method of distribution network structure based on time series dynamic analysis, the short-term prediction value of carbon emissions is obtained based on the spatiotemporal significant joint representation of multi-source factors of the power grid, including: inputting the spatiotemporal significant joint representation vector of multi-source factors of the power grid into a dynamic evolution module based on a decoder to obtain the short-term prediction value of carbon emissions.

[0016] According to one aspect of the present application, a distribution network structure dynamic deduction system based on time series dynamic analysis is provided, comprising: A distribution network data time queue acquisition module is used to acquire the time queue of power generation side data, the time queue of grid side data and the time queue of user side data; A distribution network data time queue embedding coding module, used to embed the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data respectively to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector; The distribution network data dynamic principal component time series aggregation module is used to perform dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector respectively to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: a principal component extraction unit, which is used to extract the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector and then perform principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; a graph principal component propagation aggregation unit, which is used to perform graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector; The carbon emission short-term prediction module is used to obtain the short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation of the multi-source factors of the power grid between the principal component time series aggregation representation vector of the power generation side data characteristics, the principal component time series aggregation representation vector of the power grid side data characteristics and the principal component time series aggregation representation vector of the user side data characteristics.

[0017] This application has significant technical effects: The present application provides a method and system for dynamic deduction of distribution network structure based on time series dynamic analysis, which adopts a data processing algorithm based on deep learning to embed coding and principal component time series aggregation of the time queues of power generation side data, power grid side data and user side data, respectively, so as to intelligently obtain the short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation between the principal component time series aggregation representation of power generation side data features, the principal component time series aggregation representation of power grid side data features and the principal component time series aggregation representation of user side data features. In this way, by integrating multi-source data from the power generation side, power grid side and user side, and analyzing and correlating their time series characteristics, the complex time series dynamic relationship in the distribution network can be captured more accurately, providing more complete information, making the prediction of carbon emissions more accurate, thereby significantly improving the real-time, comprehensive and intelligent nature of carbon emission prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 It is a flowchart of a method for dynamic deduction of distribution network structure based on time series dynamic analysis according to an embodiment of the present application.

[0020] Figure 2 Schematic diagram of data flow of a method for dynamic deduction of distribution network structure based on time series dynamic analysis according to an embodiment of the present application.

[0021] Figure 3 This is a flowchart of a multi-side data feature principal component time series aggregation representation vector according to an embodiment of the present application.

[0022] Figure 4 The present invention is a flowchart for generating a short-term prediction value of carbon emissions according to an embodiment of the present application.

[0023] Figure 5 It is a block diagram of a distribution network structure dynamic deduction system based on time series dynamic analysis according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0025] In the power system, the distribution network is indispensable. It is responsible for transmitting the high-voltage electricity generated by power plants to the low-voltage power grid and finally to home and commercial end users. In the power industry, carbon emission management is the key to optimizing the allocation of power resources and reducing environmental impact. Therefore, accurate carbon emission forecasting is crucial for planning power generation resources, especially increasing the proportion of renewable energy utilization, which helps promote the sustainable development of green energy.

[0026] However, traditional distribution network carbon emission forecasting methods often rely on a single data source, such as power generation or power consumption data. This approach ignores the complex interactions between data at all levels in the power system. For example, renewable energy output on the power generation side is affected by weather, while power consumption on the user side varies over time and seasons. These traditional methods fail to fully utilize the integrated information of multi-source data, resulting in limited comprehensiveness and accuracy of forecast results. In addition, traditional methods are mostly based on static analysis of historical data and cannot capture current dynamic changes in real time. Due to the continuous changes in the supply and demand conditions and operating status of the distribution network, and the lack of real-time data updates in traditional forecasting methods, the forecast results are often delayed and inaccurate.

[0027] In response to the above technical problems, the technical concept of the present application is to obtain the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data, and adopt the data mining and analysis algorithm based on deep learning to embed the power generation side data, the power grid side data and the user side data for the main component time series aggregation, so as to intelligently obtain the short-term prediction value of carbon emissions according to the spatiotemporal significant joint representation between the main component time series aggregation representation of the power generation side data characteristics, the main component time series aggregation representation of the power grid side data characteristics and the main component time series aggregation representation of the user side data characteristics. In this way, by integrating multi-source data from the power generation side, the power grid side and the user side, and analyzing and correlating the time series characteristics, the complex time series dynamic relationship in the distribution network can be captured more accurately, providing more complete information, making the prediction of carbon emissions more accurate, thereby significantly improving the real-time, comprehensive and intelligent nature of carbon emission prediction.

[0028] Figure 1 It is a flowchart of a method for dynamic deduction of distribution network structure based on time series dynamic analysis according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of a method for dynamic deduction of distribution network structure based on time series dynamic analysis according to an embodiment of the present application. Figure 1 and Figure 2 As shown, according to an embodiment of the present application, a method for dynamic deduction of distribution network structure based on time series dynamic analysis includes: S110, obtaining a time queue of data on the power generation side, a time queue of data on the power grid side, and a time queue of data on the user side; S120, respectively embedding and encoding the time queue of the power generation side data, the time queue of the power grid side data, and the time queue of the user side data to obtain a time queue of power generation side data embedded coding vectors, a time queue of power grid side data embedded coding vectors, and a time queue of user side data embedded coding vectors; S130, respectively performing dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector, and the time queue of the user side data embedded coding vector to obtain a power generation side data feature principal component time series aggregation representation vector, a power grid side data feature principal component time series aggregation representation vector, and a user side data feature principal component time series aggregation representation vector; S140, based on the spatiotemporal significant joint representation of multi-source factors of the power grid among the principal component time series aggregation representation vector of the power generation side data characteristics, the principal component time series aggregation representation vector of the power grid side data characteristics and the principal component time series aggregation representation vector of the user side data characteristics, a short-term prediction value of carbon emissions is obtained.

[0029] In step S110, a time queue of power generation side data, a time queue of grid side data, and a time queue of user side data are obtained. Specifically, in an embodiment of the present application, obtaining a time queue of power generation side data, a time queue of grid side data, and a time queue of user side data includes: obtaining a time queue of the power generation side data, the power generation side data includes the power generation of renewable energy and the power generation of traditional energy; obtaining a time queue of the grid side data, the grid side data includes line load, substation operation status, and voltage level; obtaining a time queue of the user side data, wherein the user side data includes power demand, power load, and power behavior.

[0030] It should be understood that the time queue of data on the power generation side specifically includes the real-time and historical power generation data of renewable energy such as solar energy, wind energy, and hydropower, as well as the real-time and historical power generation data of traditional energy such as coal, oil, and natural gas. The time queue of data on the power grid side specifically includes the real-time and historical load conditions of each line in the power grid, usually expressed in the form of current, power, etc., as well as the working status of the substation (such as normal, fault, maintenance, etc.) and the real-time and historical voltage values ​​of each node in the power grid. The time queue on the user side specifically includes the real-time and historical power demand data of the user, usually expressed in terms of power, and also includes the real-time and historical power load data of the user, usually expressed in terms of the average power consumption of the user in a certain period of time, as well as power consumption behavior data, specifically the peak and valley periods of the user's power consumption. The carbon emission prediction model based on these three time queue data can take into account the comprehensive influence of multiple factors, including the volatility of renewable energy power generation, changes in power grid load, changes in user power consumption behavior, etc. This comprehensive consideration helps to improve the accuracy of the prediction. In general, by comprehensively analyzing the data of these three time queues, the dynamic factors affecting carbon emissions can be captured more accurately, thereby providing more comprehensive basic data for short-term prediction of carbon emissions. This multi-dimensional data integration can help the model understand complex relationships, thereby improving the accuracy and real-time nature of predictions.

[0031] In step S120, the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data are respectively embedded and encoded to obtain the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector. Specifically, in an embodiment of the present application, the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data are respectively embedded and encoded to obtain the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector, including: using the power generation side data embedding encoder to embed the respective power generation side data in the time queue of the power generation side data to obtain the time queue of the power generation side data embedded coding vector; using the power grid side data embedding encoder to embed the respective power grid side data in the time queue of the power grid side data to obtain the time queue of the power grid side data embedded coding vector; using the user side data embedding encoder to embed the respective user side data in the time queue of the user side data to obtain the time queue of the user side data embedded coding vector.

[0032] It should be understood that each power generation side data in the time queue of the power generation side data usually contains a large number of features and variables, and there may be complex interactions between these features, which express important time series information of the power generation data, and this information is crucial to understanding the time series dynamic changes of electricity. Based on this, in order to map the high-dimensional features in the original data to a lower-dimensional space, so that the important characteristics of the data can be better captured in this space, and redundant information can be removed at the same time, in the technical solution of the present application, a power generation side data embedding encoder is used to embed the encoding of each power generation side data in the time queue of the power generation side data to extract and mine the core features in the data, and obtain the time queue of the power generation side data embedded encoding vector.

[0033] Accordingly, considering that the grid-side data usually contains multiple features (such as line load, substation status, voltage level), these features may be high-dimensional, and directly using the original data for analysis may lead to high computational complexity and difficulty in model training. In order to convert the high-dimensional data and make the data easier to process and analyze, the technical solution of the present application uses a grid-side data embedding encoder to embed the individual grid-side data in the time queue of the grid-side data to obtain a time queue of the grid-side data embedded coding vector. By embedding and coding the grid-side data, the high-dimensional data can be compressed into a low-dimensional embedding vector, which can significantly reduce the computational complexity of the model, thereby making the training and reasoning process more efficient and more suitable for short-term prediction scenarios of carbon emissions.

[0034] Similarly, considering that user-side data usually includes multiple types of features, such as electricity demand, electricity consumption behavior, etc. These features may have different scales and distributions. Directly using the original data for analysis may make it difficult for the model to learn effective patterns. In order to convert these diverse data into a unified vector representation for subsequent processing, the technical solution of the present application uses a user-side data embedding encoder to embed and encode each user-side data in the time queue of the user-side data to obtain a time queue of user-side data embedded coding vectors. By embedding and coding the user-side data, the patterns and structures in the user-side data can be better understood, while the computational complexity of the model can be reduced, thereby improving the computing performance of the model.

[0035] In step S130, dynamic principal component time series aggregation is performed on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector, and the time queue of the user side data embedded coding vector to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector, and the user side data feature principal component time series aggregation representation vector. Specifically, Figure 3 In the method for dynamic deduction of distribution network structure based on dynamic time series analysis according to the embodiment of the present application, the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector are respectively subjected to dynamic principal component time series aggregation to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector. Figure 3 As shown, the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector are respectively subjected to dynamic principal component time series aggregation to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: S131, extracting the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector, and then performing principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; S132, performing graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector.

[0036] It should be understood that the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector are all mutually correlated and dependent in the time dimension, and the features at different time points have different importance and influence in the entire time range. Therefore, in order to integrate the data features at different times and form a continuous time series representation, so as to better understand the trend and pattern of the historical and current status of the power generation side, the power grid side and the user side over time, in the technical solution of the present application, the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector are respectively subjected to dynamic principal component time series aggregation to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector. In particular, the time queue of the power generation side data embedded coding vector is taken as an example for detailed description.

[0037] In detail, first, the sequence of the current power generation side data embedding code vector and the historical power generation side data embedding code vector is extracted from the time queue of the power generation side data embedding code vector, and then the principal component extraction is performed to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector. It can be understood that principal component extraction analysis (PCA) is a common dimensionality reduction technique that can reveal and identify key features. In other words, PCA can determine the direction that best reflects the overall change trend in the data set, that is, the principal component. These principal components capture the key information in the data and help remove redundant information. By extracting the principal components of the current and historical features, historical information on the changes of features over time can be provided, which provides a basis for subsequent time series analysis and processing.

[0038] Next, the sequence of the principal component representation vector of the current power generation side data and the principal component representation vector of the historical power generation side data is subjected to graph principal component propagation aggregation to obtain the principal component time series aggregation representation vector of the power generation side data feature. That is, graph principal component propagation aggregation can capture the dynamic relationship of the power generation side data in time and space. By propagating the current and historical data on the graph, we can better understand the change pattern of the power generation side data over time and their mutual influence. Specifically, first, by calculating the absolute factor of the time series propagation attenuation entropy of the principal component representation vector of each historical power generation side data, the attenuation degree and information loss of the feature over time are quantified. This is very useful for understanding the importance of historical data at the current time point, thereby obtaining a sequence of absolute factors of the time series propagation attenuation entropy of the power generation side data. Then, in order to further capture and extract the understanding ability of information transmission between features in long time intervals, the absolute factors of the time-series propagation attenuation entropy of each power generation side data are modulated in time dimension based on the time span between the principal component representation vector of the current power generation side data and the principal component representation vector of each historical power generation side data to ensure that even significant changes between nodes that are far apart in time are regarded as part of normal fluctuations, so that the model can more appropriately handle the feature correlation between different time distance spans, and obtain the sequence of the time-series span modulation propagation attenuation entropy factors of the power generation side data. Subsequently, a gating function-based information transmission screening module is used to feature screen and adaptively calculate the weights of the sequence of the time-series span modulation propagation attenuation entropy factors of the power generation side data to identify the most influential propagation features and assign corresponding weights accordingly, thereby obtaining the sequence of the time-series span modulation propagation attenuation weights of the power generation side data. Finally, the sequence of the principal component representation vectors of the historical power generation side data is weighted and calculated based on the sequence of propagation attenuation weights to fully integrate the short-term and long-term feature trends and changes to obtain the historical power generation side data principal component significant transmission aggregation representation vector. Finally, the historical aggregation representation vector and the principal component representation vector of the current power generation side data are added by position to fully integrate the long-term trends in the historical data and the immediate changes in the current data to generate the principal component time series aggregation representation vector of the power generation side data features.

[0039] Specifically, in an embodiment of the present application, after extracting a sequence of a current generation side data embedded coding vector and a historical generation side data embedded coding vector from a time queue of the generation side data embedded coding vector, principal component extraction is performed to obtain a sequence of a current generation side data principal component representation vector and a historical generation side data principal component representation vector, including: extracting the current generation side data embedded coding vector from the time queue of the generation side data embedded coding vector, and performing eigenvalue-based principal component extraction on the current generation side data embedded coding vector to obtain the current generation side data principal component representation vector; defining other generation side data embedded coding vectors in the time queue of the generation side data embedded coding vector as historical generation side data embedded coding vectors to obtain a sequence of historical generation side data embedded coding vectors, and performing eigenvalue-based principal component extraction on each historical generation side data embedded coding vector in the sequence of historical generation side data embedded coding vectors to obtain a sequence of historical generation side data principal component representation vectors.

[0040] Specifically, in an embodiment of the present application, the graph principal component propagation aggregation is performed on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector, including: calculating the time series propagation attenuation entropy absolute factor of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector to obtain a sequence of the power generation side data time series propagation attenuation entropy absolute factors; calculating the sequence of the power generation side data time series propagation attenuation entropy absolute factors based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector. The absolute factors of the time-series propagation attenuation entropy of each power generation side data in the data are modulated in the time dimension to obtain a sequence of the time-series span modulation propagation attenuation entropy factors of the power generation side data; the sequence of the time-series span modulation propagation attenuation entropy factors of the power generation side data is input into the information transmission screening module based on the gating function to obtain a sequence of the time-series span modulation propagation attenuation weights of the power generation side data; based on the sequence of the time-series span modulation propagation attenuation weights of the power generation side data, the weighted sum of the sequence of the principal component representation vectors of the historical power generation side data is calculated to obtain the principal component significant transfer aggregation representation vector of the historical power generation side data; the position-by-position sum of the principal component significant transfer aggregation representation vector of the historical power generation side data and the principal component representation vector of the current power generation side data is calculated to obtain the principal component time-series aggregation representation vector of the feature power side data.

[0041] More specifically, in an embodiment of the present application, the absolute factors of the time series propagation attenuation entropy of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector are calculated to obtain a sequence of absolute factors of the time series propagation attenuation entropy of the power generation side data, including: dividing the eigenvalues ​​of the corresponding positions of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector to obtain the power generation side data time series characteristic principal component attenuation vector; calculating the logarithmic function value with base two of the absolute value of each eigenvalue of the power generation side data time series characteristic principal component attenuation vector to obtain the power generation side data time series characteristic principal component attenuation logarithmic vector; calculating the positional point product of the historical power generation side data principal component representation vector and the power generation side data time series characteristic principal component attenuation logarithmic vector, and performing positional multiplication on the obtained point product vector to obtain the power generation side data time series propagation attenuation value; calculating an exponential function with the natural constant e as the base, the power generation side data time series propagation attenuation value as the exponent to obtain the power generation side data time series propagation attenuation entropy absolute factors.

[0042] More specifically, in an embodiment of the present application, based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector, each power generation side data time series propagation attenuation entropy absolute factor in the sequence of the power generation side data principal component representation vector is modulated in the time dimension to obtain a sequence of power generation side data time series span modulation propagation attenuation entropy factors, including: subtracting the timestamp of the current power generation side data principal component representation vector from the timestamp of the historical power generation side data principal component representation vector and rounding down to obtain the power generation side data time span value; calculating an exponential function with the natural constant e as the base and the power generation side data time span value as the exponent to obtain the power generation side data time span modulation value; dividing the power generation side data time series propagation attenuation entropy absolute factor corresponding to the historical power generation side data principal component representation vector by the power generation side data time span modulation value to obtain the power generation side data time series span modulation propagation attenuation entropy factor.

[0043] More specifically, in an embodiment of the present application, the sequence of the generation side data timing span modulation propagation attenuation entropy factors is input into an information transmission screening module based on a gating function to obtain a sequence of the generation side data timing span modulation propagation attenuation weights, including: comparing each generation side data timing span modulation propagation attenuation entropy factor in the sequence of the generation side data timing span modulation propagation attenuation entropy factors with a predetermined threshold to obtain a sequence of the generation side data timing span modulation propagation attenuation weights; wherein, in response to the generation side data timing span modulation propagation attenuation entropy factor being greater than the predetermined threshold, the generation side data timing span modulation propagation attenuation entropy factor greater than the predetermined threshold is input into a sigmoid function; in response to the generation side data timing span modulation propagation attenuation entropy factor being less than or equal to the predetermined threshold, the generation side data timing span modulation propagation attenuation entropy factor less than or equal to the predetermined threshold is set to zero.

[0044] In the embodiment of the present application, specifically, the time queue of the power generation side data embedded coding vector is dynamically principal component time series aggregated to obtain the power generation side data feature principal component time series aggregated representation vector, which can be expressed by the formula: , , , , , , , , in, A time queue for embedding the encoding vector for the power generation side data, are respectively the first, second, ..., first in the time queue of the power generation side data embedded coding vector ,..., The data on the power generation side is embedded in the coding vector, Yes Perform principal component extraction based on eigenvalues. for The corresponding principal component representation vector of the power generation side data is: for The transposed vector of is the length of the power generation side data embedding coding vector, yes The corresponding covariance matrix of the power generation side data is: yes The corresponding principal component orthogonal matrix of the power generation side data is: for The transposed matrix of is the principal component vector of each power generation side data in the sequence of the principal component vector of the power generation side data, for The corresponding diagonal matrix of power generation side data is: The diagonal elements of the matrix are The diagonal matrix of power generation side data, are the weight values ​​of the principal component vectors of each power generation side data, To return the maximum value value, is the maximum approximate matching value, is a sequence of principal component representation vectors of the power generation side data, are the first, second, ..., first ,..., The principal component representation vector of the power generation side data, for The eigenvalues ​​at each position in , for The eigenvalues ​​at each position in for The number of eigenvalues ​​in , represents the logarithmic function value with base 2, represents an exponential function with the natural constant e as the base, for and The absolute factor of the attenuation entropy of the data time series propagation at the power generation side, and Respectively represent and The timestamp of the principal component representation vector of the power generation side data, To round down, yes and The data timing span modulation propagation attenuation entropy factor between the power generation side, For masking, yes function, is a predetermined threshold, is the propagation attenuation weight of each generation side data timing span modulation in the sequence of the generation side data timing span modulation propagation attenuation weight, is the number of vectors in the sequence of the principal component representation vectors of the power generation side data, is the principal component time series aggregation representation vector of the power generation side data feature. Similarly, the processing method of the time queue of the grid side data embedded coding vector and the time queue of the user side data embedded coding vector is also as shown in the above formula.

[0045] In step S140, based on the spatiotemporal significant joint representation of the power grid multi-source factors between the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector, and the user side data feature principal component time series aggregation representation vector, a short-term prediction value of carbon emissions is obtained. Specifically, Figure 4 A flow chart of obtaining a short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation of the power grid multi-source factors among the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector, and the user side data feature principal component time series aggregation representation vector in a distribution network structure dynamic deduction method based on time series dynamic analysis according to an embodiment of the present application. Figure 4 As shown, based on the spatiotemporal significant joint representation of multi-source factors of the power grid among the time series aggregation representation vector of the power generation side data features, the time series aggregation representation vector of the power grid side data features and the time series aggregation representation vector of the user side data features, a short-term prediction value of carbon emissions is obtained, including: S141, inputting the time series aggregation representation vector of the power generation side data features, the time series aggregation representation vector of the power grid side data features and the time series aggregation representation vector of the user side data features into a spatial element joint deep analyzer based on a multi-layer perceptron model to obtain a spatiotemporal significant joint representation vector of multi-source factors of the power grid as the spatiotemporal significant joint representation of multi-source factors of the power grid; S142, based on the spatiotemporal significant joint representation of multi-source factors of the power grid, a short-term prediction value of carbon emissions is obtained.

[0046] In step S141, the time series aggregation representation vector of the principal component of the data feature of the power generation side, the time series aggregation representation vector of the principal component of the data feature of the power grid side, and the time series aggregation representation vector of the principal component of the data feature of the user side are input into the spatial element joint depth analyzer based on the multi-layer perceptron model to obtain the spatiotemporal significant joint representation vector of the multi-source factors of the power grid as the spatiotemporal significant joint representation of the multi-source factors of the power grid. It should be understood that in order to integrate and analyze data from different aspects, so as to obtain a comprehensive and detailed representation of the power grid state, so as to improve the accuracy of the subsequent short-term prediction of carbon emissions, in the technical solution of the present application, the time series aggregation representation vector of the principal component of the data feature of the power generation side, the time series aggregation representation vector of the principal component of the data feature of the power grid side, and the time series aggregation representation vector of the principal component of the data feature of the user side are input into the spatial element joint depth analyzer based on the multi-layer perceptron model to obtain the spatiotemporal significant joint representation vector of the multi-source factors of the power grid. It is worth mentioning that the multi-layer perceptron model can capture the nonlinear relationship in the data and the interaction between different features in space. Therefore, through the multi-layer perceptron, the complex spatiotemporal interactions between the data on the power generation side, the grid side, and the user side can be explored, so as to better understand how they jointly affect the overall performance of the power grid and improve the prediction accuracy of the power grid status.

[0047] In step S142, based on the spatiotemporal significant joint representation of the multi-source factors of the power grid, the short-term prediction value of the carbon emission is obtained. Specifically, in an embodiment of the present application, based on the spatiotemporal significant joint representation of the multi-source factors of the power grid, the short-term prediction value of the carbon emission is obtained, including: inputting the spatiotemporal significant joint representation vector of the multi-source factors of the power grid into a dynamic evolution module based on a decoder to obtain the short-term prediction value of the carbon emission. That is, the spatiotemporal significant joint representation of the multi-source factors of the power grid obtained by spatiotemporal significant joint representation of the main component time series aggregation representation vector of the power generation side data feature, the main component time series aggregation representation vector of the power grid side data feature and the main component time series aggregation representation vector of the user side data feature is used for decoding processing, so as to intelligently obtain the short-term prediction value of carbon emission. In this way, by integrating the multi-source data of the power generation side, the power grid side and the user side, and performing time series feature analysis and mutual correlation on them, the complex time series dynamic relationship in the distribution network can be more accurately captured, and more complete information can be provided, so that the prediction of carbon emissions is more accurate, thereby significantly improving the real-time, comprehensive and intelligent degree of carbon emission prediction.

[0048] In particular, in the technical solution of the present application, the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector respectively represent the time series propagation aggregation characteristics of the power generation side data, power grid measurement data and user side data. In this way, when the principal component time series aggregation representation vector of the power generation side data features, the principal component time series aggregation representation vector of the power grid side data features and the principal component time series aggregation representation vector of the user side data features are input into the spatial element joint deep analyzer based on the multi-layer perceptron model, considering the differences in time series pattern distribution and source data modality between the principal component time series aggregation representation vector of the power generation side data features, the principal component time series aggregation representation vector of the power grid side data features and the principal component time series aggregation representation vector of the user side data features, this will cause the spatial element joint deep analysis based on the multi-layer perceptron model to have an imbalance in the element joint weights, so that the characteristic manifold of the obtained spatiotemporal significant joint representation vector of the multi-source factors of the power grid has expression sparsity and distribution imbalance in the high-dimensional feature space, which in turn affects the accuracy of the short-term prediction value of carbon emissions obtained through the dynamic evolution module based on the decoder.

[0049] Based on this, in a preferred embodiment, the spatiotemporal significant joint representation vector of the multi-source factors of the power grid is input into a decoder-based dynamic evolution module to obtain a short-term prediction value of carbon emissions, including: performing L random sampling on the spatiotemporal significant joint representation vector of the multi-source factors of the power grid to obtain L random disturbance eigenvalues ​​of the power grid state; arranging the L random disturbance eigenvalues ​​of the power grid state in an ordered manner from large to small to obtain a random disturbance ordered vector of the power grid state; calculating the self-inner product of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid to obtain a power grid state energy modulation eigenvalue; and performing L random sampling on the random disturbance ordered vector of the power grid state based on the power grid state energy modulation eigenvalue. Perform semantic intensity scaling to obtain a semantically enhanced ordered vector of random disturbance of power grid state; calculate the product between the semantically enhanced ordered vector of random disturbance of power grid state and the transposed vector of the power grid multi-source factor spatiotemporal significant joint representation vector to obtain the power grid state best sample prompt orthogonal modulation matrix; calculate the matrix product between the power grid state best sample prompt orthogonal modulation matrix and the power grid multi-source factor spatiotemporal significant joint representation vector to obtain an optimized power grid multi-source factor spatiotemporal significant joint representation vector; input the optimized power grid multi-source factor spatiotemporal significant joint representation vector into the decoder-based dynamic evolution module to obtain the short-term prediction value of the carbon emission.

[0050] The above optimization process is expressed as follows: , , , , , , , , in, is the spatiotemporal significant joint representation vector of the multi-source factors of the power grid, It means to calculate the mean of the vector. is the mean of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid, is the spatiotemporal significant joint representation vector of the multi-source factors of the power grid. The eigenvalues ​​at the positions, is the length of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid, is the variance of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid, is the Gaussian density probability function, express Random sampling, and It represents the spatiotemporal significant joint representation vector of the multi-source factors of the power grid. The random sampling The random disturbance characteristic value of the power grid state, Express Arrange them in order from large to small. is the ordered vector of random disturbance of the power grid state, represents the square of the vector's two-norm, is the energy modulation characteristic value of the power grid state, It means point multiplication by position. represents matrix multiplication, for The transposed vector of Prompt an orthogonal modulation matrix for the best example of the grid state, A spatiotemporal significant joint representation vector of multi-source factors in the optimized power grid.

[0051] Accordingly, the spatiotemporal significant joint representation vector of the multi-source factors of the power grid is Random sampling is performed to obtain a set of new feature values ​​based on the original features but with slight changes, so as to introduce randomness to explore different aspects of the feature space, and by simulating the noise or variation in the actual data, it helps the model to better generalize to unseen data. Then, these grid state random perturbation feature values ​​are arranged in order from large to small to form a grid state random perturbation ordered vector to identify which perturbation directions may be the most important for the current problem. Such an ordering not only helps the algorithm focus on the feature differences that can best distinguish different categories, but also helps to reveal hidden data structures or patterns. Subsequently, the inner product of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid is calculated to obtain the power grid state energy modulation eigenvalue, which reflects the overall strength or "energy" of the original feature vector and provides a benchmark for measuring the importance of the feature. Using this power grid state energy modulation eigenvalue as a reference point, the previously formed power grid state random perturbation ordered vector is semantically scaled to construct a semantically enhanced power grid state random perturbation ordered vector. In this way, those disturbances that appear to be more important in the original feature space are amplified, thereby enhancing the model's sensitivity to changes in key features, while also retaining relatively small but potentially useful disturbance information.

[0052] Then, the product of the ordered vector of random disturbance of semantically enhanced power grid state and the transpose of the vector of spatiotemporal significant joint representation of multi-source factors of power grid is calculated to obtain the orthogonal modulation matrix of the best sample prompt of power grid state. The purpose is to find the interaction mode between features under two different perspectives. The formed matrix can be regarded as a conversion tool for capturing and representing the complex linear relationship between features. Finally, the final feature structure modulation is completed by applying the orthogonal modulation matrix of the best sample prompt of power grid state constructed above to the original spatiotemporal significant joint representation vector of multi-source factors of power grid, so as to make the features more in line with the requirements of the objective function, reduce redundant information, and maximize the retention of useful information, so as to improve learning efficiency and prediction performance.

[0053] In summary, the feature manifold optimization process of the spatiotemporal significant joint representation vector of the multi-source factors of the power grid is explained, which extracts the best prompt examples based on the fine-grained target retrieval of random perturbations, and then guides the adversarial robustness learning based on the best prompt examples to complete the structure of the high-dimensional manifold of the feature vector in the feature space, so as to improve the detail and structure of the feature expression, thereby improving the accuracy of the short-term prediction value of carbon emissions obtained by the dynamic evolution module based on the decoder. In this way, by integrating multi-source data from the power generation side, the power grid side and the user side, and analyzing and correlating the time series characteristics, the complex time series dynamic relationship in the distribution network can be captured more accurately, providing more complete information, making the prediction of carbon emissions more accurate, thereby significantly improving the real-time, comprehensive and intelligent nature of carbon emission prediction.

[0054] In summary, a method for dynamic deduction of distribution network structure based on time series dynamic analysis based on the embodiment of the present application is explained, which adopts a data processing algorithm based on deep learning to embed coding and principal component time series aggregation of the time queues of power generation side data, power grid side data and user side data, respectively, so as to intelligently obtain the short-term prediction value of carbon emissions according to the spatiotemporal significant joint representation between the principal component time series aggregation representation of power generation side data features, the principal component time series aggregation representation of power grid side data features and the principal component time series aggregation representation of user side data features. In this way, by integrating multi-source data from the power generation side, power grid side and user side, and performing time series feature analysis and mutual correlation on them, the complex time series dynamic relationship in the distribution network can be captured more accurately, providing more complete information, making the prediction of carbon emissions more accurate, thereby significantly improving the real-time, comprehensiveness and intelligence of carbon emission prediction.

[0055] Figure 5 FIG. 1 is a block diagram of a distribution network structure dynamic deduction system based on time series dynamic analysis according to an embodiment of the present application. Figure 5 As shown, a distribution network structure dynamic deduction system 100 based on time series dynamic analysis includes: The distribution network data time queue acquisition module 110 is used to acquire the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data; A distribution network data time queue embedding coding module 120 is used to embed the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector respectively; The distribution network data dynamic principal component time series aggregation module 130 is used to perform dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector respectively to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: a principal component extraction unit, which is used to extract the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector and then perform principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; a graph principal component propagation aggregation unit, which is used to perform graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector; The carbon emission short-term prediction module 140 is used to obtain the short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation of the multi-source factors of the power grid between the main component time series aggregation representation vector of the power generation side data characteristics, the main component time series aggregation representation vector of the power grid side data characteristics and the main component time series aggregation representation vector of the user side data characteristics.

[0056] Here, those skilled in the art can understand that the specific operations of the various modules and units in the above-mentioned distribution network structure dynamic deduction system based on time series dynamic analysis have been referred to above. Figures 1 to 4 It has been introduced in detail in the description of the distribution network structure dynamic deduction method based on time series dynamic analysis, and therefore, its repeated description will be omitted.

Claims

1. A distribution network structure dynamic deduction method based on time series dynamic analysis, characterized in that: include: Obtaining a time queue for power generation side data, a time queue for grid side data, and a time queue for user side data; Embedding and encoding the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data respectively to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector; Respectively perform dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: extracting the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector, and then perform principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; perform graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector; Based on the spatiotemporal significant joint representation of multi-source factors of the power grid among the principal component time series aggregation representation vector of the power generation side data features, the principal component time series aggregation representation vector of the power grid side data features and the principal component time series aggregation representation vector of the user side data features, a short-term prediction value of carbon emissions is obtained.

2. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 1 is characterized in that: Obtain the time queue of the power generation side data, the time queue of the grid side data and the time queue of the user side data, including: Acquire a time queue of the power generation side data, wherein the power generation side data includes power generation of renewable energy and power generation of traditional energy; Acquire a time queue of the grid-side data, the grid-side data including line load, substation operation status and voltage level; A time queue of the user-side data is obtained, wherein the user-side data includes electricity demand, electricity load and electricity consumption behavior.

3. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 2 is characterized in that: The time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data are respectively embedded and encoded to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector, including: Using a power generation side data embedding encoder to embed the respective power generation side data in the time queue of the power generation side data to obtain the time queue of the power generation side data embedding coding vector; Using a grid-side data embedding encoder to embed the individual grid-side data in the time queue of the grid-side data to obtain the time queue of the grid-side data embedding coding vector; A user-side data embedding encoder is used to embed the respective user-side data in the time queue of the user-side data to obtain the time queue of the user-side data embedding coding vector.

4. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 3 is characterized in that: The method further comprises: extracting a sequence of a current power generation side data embedding coding vector and a sequence of a historical power generation side data embedding coding vector from a time queue of the power generation side data embedding coding vector, and then performing principal component extraction to obtain a sequence of a current power generation side data principal component representation vector and a sequence of a historical power generation side data principal component representation vector, including: Extracting the current power generation side data embedding coding vector from the time queue of the power generation side data embedding coding vector, and performing principal component extraction based on eigenvalues ​​on the current power generation side data embedding coding vector to obtain the principal component representation vector of the current power generation side data; The other power generation side data embedded coding vectors in the time queue of the power generation side data embedded coding vector are defined as historical power generation side data embedded coding vectors to obtain a sequence of the historical power generation side data embedded coding vectors, and the principal component extraction based on the eigenvalue is performed on each historical power generation side data embedded coding vector in the sequence of the historical power generation side data embedded coding vector to obtain a sequence of the principal component representation vectors of the historical power generation side data.

5. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 4 is characterized in that: Performing graph principal component propagation aggregation on the sequence of the principal component representation vector of the current power generation side data and the principal component representation vector of the historical power generation side data to obtain the principal component time series aggregation representation vector of the power generation side data feature, including: Calculate the absolute factor of the time series propagation attenuation entropy of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector to obtain a sequence of absolute factors of the time series propagation attenuation entropy of the power generation side data; Based on the time span between each historical power generation data principal component representation vector in the sequence of historical power generation data principal component representation vectors and the current power generation data principal component representation vector, each power generation data time series propagation attenuation entropy absolute factor in the sequence of power generation data time series propagation attenuation entropy absolute factors is modulated in time dimension to obtain a sequence of power generation data time series span modulated propagation attenuation entropy factors; Inputting the sequence of the generation side data timing span modulation propagation attenuation entropy factors into the information transmission screening module based on the gating function to obtain the sequence of the generation side data timing span modulation propagation attenuation weights; Based on the sequence of the time series span modulation propagation attenuation weights of the power generation side data, a weighted sum of the sequence of the principal component representation vectors of the historical power generation side data is calculated to obtain a principal component significant transfer aggregation representation vector of the historical power generation side data; Calculate the positional sum of the historical power generation side data principal component significant transfer aggregation representation vector and the current power generation side data principal component representation vector to obtain the power generation side data characteristic principal component time series aggregation representation vector; The sequence of the generation side data timing span modulation propagation attenuation entropy factors is input into the information transmission screening module based on the gating function to obtain the sequence of the generation side data timing span modulation propagation attenuation weights, including: Comparing each generation side data timing span modulation propagation attenuation entropy factor in the sequence of the generation side data timing span modulation propagation attenuation entropy factors with a predetermined threshold value to obtain a sequence of the generation side data timing span modulation propagation attenuation weights; Among them, in response to the fact that the propagation attenuation entropy factor of the timing span modulation of the data on the power generation side is greater than the predetermined threshold, the propagation attenuation entropy factor of the timing span modulation of the data on the power generation side that is greater than the predetermined threshold is input into the sigmoid function; in response to the fact that the propagation attenuation entropy factor of the timing span modulation of the data on the power generation side is less than or equal to the predetermined threshold, the propagation attenuation entropy factor of the timing span modulation of the data on the power generation side that is less than or equal to the predetermined threshold is set to zero.

6. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 5 is characterized in that: Calculating the time series propagation attenuation entropy absolute factor of each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector relative to the current power generation side data principal component representation vector to obtain a sequence of the time series propagation attenuation entropy absolute factors of the power generation side data, including: Dividing the eigenvalues ​​of the corresponding positions of the principal component representation vector of the historical power generation side data and the principal component representation vector of the current power generation side data to obtain a principal component attenuation vector of the time series characteristics of the power generation side data; Calculate the base-two logarithmic function value of the absolute value of each eigenvalue of the principal component attenuation vector of the power generation side data time series feature to obtain the principal component attenuation logarithmic vector of the power generation side data time series feature; Calculate the positional point product of the principal component representation vector of the historical power generation side data and the principal component attenuation logarithm vector of the power generation side data time series feature, and perform point-by-point multiplication on the obtained point product vector to obtain the power generation side data time series propagation attenuation value; An exponential function of the power generation side data time series propagation attenuation value with the natural constant e as the base is calculated to obtain the power generation side data time series propagation attenuation entropy absolute factor.

7. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 6 is characterized in that: Based on the time span between each historical power generation side data principal component representation vector in the sequence of the historical power generation side data principal component representation vector and the current power generation side data principal component representation vector, each power generation side data time series propagation attenuation entropy absolute factor in the sequence of the power generation side data time series propagation attenuation entropy absolute factor is modulated in the time dimension to obtain a sequence of power generation side data time series span modulated propagation attenuation entropy factors, including: Subtract the timestamp of the principal component representation vector of the current power generation side data from the timestamp of the principal component representation vector of the historical power generation side data and round down to obtain a time span value of the power generation side data; Calculate an exponential function with the natural constant e as the base and the time span value of the power generation side data as the exponent to obtain a time span modulation value of the power generation side data; The absolute factor of the generation side data time series propagation attenuation entropy corresponding to the principal component representation vector of the historical generation side data is divided by the time span modulation value of the generation side data to obtain the time series span modulation propagation attenuation entropy factor of the generation side data.

8. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 7 is characterized in that: Based on the spatiotemporal significant joint representation of the power grid multi-source factors among the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector, and the user side data feature principal component time series aggregation representation vector, a short-term prediction value of carbon emissions is obtained, including: Input the time series aggregation representation vector of the principal component of the data feature on the power generation side, the time series aggregation representation vector of the principal component of the data feature on the power grid side, and the time series aggregation representation vector of the principal component of the data feature on the user side into a spatial element joint deep analyzer based on a multi-layer perceptron model to obtain a spatiotemporal significant joint representation vector of multi-source factors of the power grid as the spatiotemporal significant joint representation of multi-source factors of the power grid; Based on the spatiotemporal significant joint representation of the multi-source factors of the power grid, the short-term prediction value of the carbon emission is obtained.

9. The method for dynamic deduction of distribution network structure based on time series dynamic analysis according to claim 8, characterized in that: Based on the spatiotemporal significant joint representation of the multi-source factors of the power grid, a short-term prediction value of carbon emissions is obtained, including: inputting the spatiotemporal significant joint representation vector of the multi-source factors of the power grid into a decoder-based dynamic evolution module to obtain the short-term prediction value of carbon emissions.

10. A distribution network structure dynamic deduction system based on time series dynamic analysis is characterized by: include: A distribution network data time queue acquisition module is used to acquire the time queue of power generation side data, the time queue of grid side data and the time queue of user side data; A distribution network data time queue embedding coding module, used to embed the time queue of the power generation side data, the time queue of the power grid side data and the time queue of the user side data respectively to obtain a time queue of the power generation side data embedded coding vector, a time queue of the power grid side data embedded coding vector and a time queue of the user side data embedded coding vector; The distribution network data dynamic principal component time series aggregation module is used to perform dynamic principal component time series aggregation on the time queue of the power generation side data embedded coding vector, the time queue of the power grid side data embedded coding vector and the time queue of the user side data embedded coding vector respectively to obtain the power generation side data feature principal component time series aggregation representation vector, the power grid side data feature principal component time series aggregation representation vector and the user side data feature principal component time series aggregation representation vector, including: a principal component extraction unit, which is used to extract the sequence of the current power generation side data embedded coding vector and the historical power generation side data embedded coding vector from the time queue of the power generation side data embedded coding vector and then perform principal component extraction to obtain the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector; a graph principal component propagation aggregation unit, which is used to perform graph principal component propagation aggregation on the sequence of the current power generation side data principal component representation vector and the historical power generation side data principal component representation vector to obtain the power generation side data feature principal component time series aggregation representation vector; The carbon emission short-term prediction module is used to obtain the short-term prediction value of carbon emissions based on the spatiotemporal significant joint representation of the multi-source factors of the power grid between the principal component time series aggregation representation vector of the power generation side data characteristics, the principal component time series aggregation representation vector of the power grid side data characteristics and the principal component time series aggregation representation vector of the user side data characteristics.

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