An emergency cooperation auxiliary analysis system based on intelligent algorithm

Abstract

The application provides an emergency cooperation auxiliary analysis system based on an intelligent algorithm, which comprises a detection device, a processing unit, an intelligent algorithm module, a stability coefficient calculation unit and a pre-judgment unit. The detection device acquires various state data of a power system and captures fault chain reaction data, and the processing unit receives and pre-processes data in real time to ensure data consistency. The intelligent algorithm module dynamically adjusts the algorithm mode based on feature extraction and simplified calculation strategy to improve the analysis speed under high load conditions. The stability coefficient calculation unit calculates the stability coefficient of the power system through a multi-level algorithm and adjusts the calculation strategy under high load. The pre-judgment unit performs risk assessment based on the stability coefficient, divides the system state according to the risk level and generates an alarm signal, thereby realizing real-time monitoring and early warning of potential problems of the power system.

Claims

1. An emergency collaborative auxiliary analysis system based on intelligent algorithms, characterized in that: The auxiliary analysis system includes: The detection device is used to acquire emergency processing data of multiple states of the power system within a specific time period. The emergency processing data includes multiple emergency processing procedures related to the fault state of each component in the power system, and each emergency processing procedure contains emergency data of chain reactions. The processing unit is used to acquire real-time condition prediction information related to electrical quantities in the power system; The intelligent algorithm module is configured to extract a feature matrix from the received situation inference information based on emergency processing data, wherein the feature matrix corresponds to one or more real-time variables of the power system; the intelligent algorithm module dynamically adjusts the algorithm mode according to the real-time load of the power system. The stability coefficient calculation unit, connected to the intelligent algorithm module, is configured to determine the stability coefficient of the received condition prediction information based on the extracted feature matrix; the stability coefficient calculation unit automatically adjusts the calculation strategy under different load conditions; The prediction unit is used to predict potential problems in the power system based on the determined stability coefficient.

2. The system as described in claim 1, characterized in that: The detection device includes an emergency response process capture unit for recording and tracking chain reaction data caused by component failure. The emergency response process capture unit generates emergency data of the fault propagation path by analyzing the logical relationship between multiple data streams, and analyzes the detected fault state based on Boolean logic rules to identify potential chain reactions.

3. The system as described in claim 1, characterized in that: The processing unit includes a data receiving module and a preliminary data parsing module. The data receiving module is used to receive various status data from the detection device and has a buffer management and data verification mechanism. The preliminary data parsing module is used to perform data cleaning, anomaly detection and data structuring processing on the received data.

4. The system as described in claim 1, characterized in that: The intelligent algorithm module includes: The feature extraction unit is used to analyze the situation prediction information and extract feature variables related to the power system state. The feature extraction unit identifies and extracts key variables based on a pre-trained algorithm model. The feature extraction unit realizes feature extraction through an autoencoder structure, wherein the autoencoder contains an encoder and a decoder, which maps high-dimensional data to a low-dimensional feature space. The mapping function of the autoencoder is expressed as h = f(Wx + b), where W is the weight matrix, b is the bias term, f is the activation function, x is the input data, and h is the extracted feature vector. The emergency response optimization unit includes: a load monitor, which is used to monitor the system load in real time and trigger an emergency response when the load level exceeds a preset threshold; The switching controller receives signals from the load monitor and activates a simplified computing mode, in which a predefined fast path algorithm is run to maintain system analysis speed and processing quality of core feature data under high load conditions.

5. The system as described in claim 1, characterized in that: The stability coefficient calculation unit includes: The core calculation module is used to determine the current stability coefficient of the power system by applying a multi-level calculation algorithm based on the input feature matrix. The multi-level calculation algorithm includes multivariate regression analysis and hierarchical calculation method, which can process each variable layer by layer and obtain the stability coefficient through basic power parameters and load fluctuations. The dynamic computing regulator is used to adjust the computing strategy under high load conditions. It triggers a simplified computing mode based on the current load by monitoring the real-time usage of system resources. In the simplified computation model, the feature selection algorithm is adjusted to process features in stages. The regulator decomposes the computation task into k stages, with each stage calculating a subset of features. The staged calculation formula is as follows: SI stage This indicates the stability coefficient result for a certain stage. p represents the number of features processed in each stage; p is less than the total number of features n. w j It is the weight of the j-th feature; x j It is the value of the j-th feature.

6. The system as described in claim 4 or 5, characterized in that: The simplified calculation mode calculates the importance of each feature based on a recursive feature elimination algorithm and arranges them in descending order, retaining the first m features to form a simplified feature set S′; the specific steps of the recursive feature elimination algorithm are as follows: Initialize the feature set S = {f1, f2, ..., f i ,…,f n The feature set S contains all features f i Where 1≤i≤n; Calculate feature importance I(f) i ), each feature f i Importance I(f i ) is calculated; Sort and retain key features according to their importance I(f) i The features are sorted in descending order, and the first m features are retained as the simplified feature set S′. The simplified formula for the feature set S′ is: S′={f i ∣I(f i )>T} Where T is a preset importance threshold; only importance I(f) is retained. i Features that exceed the threshold T.

7. The system as described in claim 1, characterized in that: The prediction unit includes: The risk assessment module receives the current stability coefficient and historical stability coefficient datasets, and calculates the deviation R of the current stability coefficient relative to the historical mean. The formula for calculating the deviation is as follows: Where SI(t) is the current stability coefficient, μ H σ is the historical data mean. H The standard deviation of historical data is used; the risk assessment module determines the risk level of the system based on the degree of deviation R, and classifies the system status into low-risk, medium-risk, and high-risk levels; The alarm output module is used to generate and issue real-time alarm signals when the risk level reaches medium or high risk, and to transmit the stability coefficient calculation results, deviation analysis and recommended operation suggestions to the operators.

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