Power grid regulation and control data center service characteristic fault positioning method and system based on time sequence and fault tree analysis
A fault tree analysis, data center technology, applied in digital transmission systems, transmission systems, data processing applications, etc., can solve the problems of performance dependence, control data center damage, etc., to achieve simple use, eliminate negative effects, and high prediction accuracy. Effect
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Embodiment 1
[0044] figure 1 It is a schematic flow diagram of the method for locating faults in service characteristics of power grid control data centers based on time series and fault tree analysis provided by Embodiment 1 of the present invention, as shown in figure 1 As shown, the time series and fault tree analysis-based fault location method for business characteristics of power grid regulation data centers provided in this embodiment may include:
[0045] Establish a fault tree model; obtain a set of variable data corresponding to each device in the power control data center, and normalize the variable data with maximum and minimum values; use the MARA adaptive prediction model to predict the future state of the obtained variable data, And store the predicted data in the database; compare the predicted data with the threshold value, and output the predicted data after binary conversion when the predicted data meets the threshold value; receive the binary predicted data, and conduct...
Embodiment 2
[0050] On the basis of Embodiment 1, this Embodiment 2 provides a method for quickly locating faults of power grid regulation data center business characteristics based on time series and fault tree analysis, and the future state prediction of the acquired variable data is performed through the MARA adaptive prediction model. , and store the predicted data in the database, which may specifically include:
[0051] Create a MARA model; create a multi-step MARA model and train it in an adaptive manner to obtain a MARA adaptive prediction model based on variable data; predict the future state of the acquired variable data, when the MARA adaptive prediction model obtains the future moment The predicted data value of , and store the predicted data value in the database.
[0052] image 3 It is a schematic diagram of the circuit structure of the grid control data center business characteristic fault location system based on time series and fault tree analysis provided by Embodiment ...
Embodiment 3
[0062] On the basis of Embodiment 1, this Embodiment 3 provides a method for quickly locating faults in power grid control data center business characteristics based on time series and fault tree analysis. The establishment of a fault tree model may specifically include:
[0063] Create the top business to represent the system state, and split the top business to obtain the lower terminal business, and split the lower terminal business step by step, and end the split when reaching the maximum value of the explainable resolution;
[0064] Use the Boolean logic gate index to connect the lower terminal business with the top business, establish a Boolean expression corresponding to each sub-business, and a Boolean expression corresponding to the top business;
[0065] The receiving binary prediction data and performing availability analysis through the fault tree model may specifically include:
[0066] The fault tree model decomposes the binary forecast data and simplifies it int...
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