A method and system for constructing a safety emergency response evaluation system
A technology of emergency response and construction method, which is applied in the field of neural network algorithm, can solve the problems of the influence of subjective factors in the safety emergency response system, and achieve the effect of solving dynamic problems and overcoming the influence of subjective factors
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Embodiment 1
[0061] This embodiment provides a method for constructing a safety emergency response evaluation system, such as figure 1 shown, including steps:
[0062] S11: Establish a one-click emergency evaluation index system and collect training samples and test samples;
[0063] S12: Determine the network weights of the training samples through the BP neural network model;
[0064] S13: Input the test samples into the BP neural network model and calculate the accuracy of the output results to evaluate the effect of one-key emergency response.
[0065] In this embodiment, step S11 is to establish a one-key emergency evaluation index system and collect training samples and test samples.
[0066] Specifically, through comprehensive analysis, there are four first-level indicators, namely: web page tampering emergency U1, domain name hijacking emergency U2, intrusion attack emergency U3, and different program emergency U4.
[0067] The first-level indicators are subdivided into second-l...
Embodiment 2
[0101] This embodiment provides a method for constructing a safety emergency response evaluation system, such as image 3 shown, including steps:
[0102] S31: Establish a one-click emergency evaluation index system and collect training samples and test samples;
[0103] S32: Determine the network weights of the training samples through the BP neural network model;
[0104] S33: Sorting the importance of the indicators of the training samples according to the network weights;
[0105] S34: Input the test sample into the BP neural network model and calculate the accuracy of the output result to evaluate the effect of one-key emergency response;
[0106] S35: Input the index data of the samples with unknown results into the trained BP neural network model, and predict and evaluate the prediction effect.
[0107] The difference between this embodiment and the first embodiment is that it further includes step S33 and step S35.
[0108] Step S33 is to sort the indicators of the...
Embodiment 3
[0123] This embodiment provides a method for constructing a safety emergency response evaluation system, such as Figure 5 shown, including steps:
[0124] S51: Establish a one-click emergency evaluation index system and collect training samples and test samples;
[0125] S52: Establish a BP neural network model;
[0126] S53: Initialize each parameter of the BP neural network model;
[0127] S54: Input the training sample into the BP neural network model to obtain the error value between the output value and the actual value;
[0128] S55: Determine whether the error value is greater than a preset threshold, if so, correct the network weight; otherwise, initialize each parameter of the BP neural network model;
[0129] S56: Sorting the importance of the indicators of the training samples according to the network weights;
[0130] S57: Input the test sample into the BP neural network model and calculate the accuracy of the output result to evaluate the effect of one-key em...
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