Reading the Wavelet transform plot
Imagine for one second, that you just plotted your daubechies-4 wavelet, as you can 
see here in red. 



Now imagine that you take this waveform in red, and simply do a cross-correlation with your signal. You plot that result. This will be the first row of your plot. This is scale-1. Next, you dilate your Daubechies-4 wavelet, (that is, you simply make it 'stretch' it in time, by some factor). Then, you again do a cross-correlation of this new waveform with your signal. You then get row two of your plot. This is scale-2. You keep doing this for all scales, which means you keep taking your original 'mother' wavelet, and you keep dilating, then cross-correlating, dilating, and cross-correlating, etc, and you just plot the results one on top of the others. 

This is what the CWT plot is showing you. The results of performing a cross-correlation of your signal with a wavelet at different scales, that is, at different dilation (stretch) factors. 

So let us interpret your image. In the first row, you can see that you have weak amplitudes in your cross-correlation. That means that it is telling you, almost nothing in your signal correlates, (or 'matches') your wavelet, when it is at scale-1, (when it is at the smallest scale). You keep stretching your wavelet and correlating, and still it is not matching anything in your signal, until you reach say, scale-31. So when you stretch your wavelet 31 times, and performed a cross-correlation, you start to see some bright spots, which means you are getting good cross-correlation scores between your stretched wavelet, and your signal. 

If you look however at the very top, we have the brightest spots. So for scale-46, you made that row by stretching your original wavelet 46 times, and then cross-correlated it with your signal, and then that is your row-46. So you see a lot of nice bright spots. You can see that at positions (x-axis) ~25, ~190, and ~610, I have bright spots. So that is telling you, you have some feature in your signal, that very closely matches your wavelet that is stretched 46 times. So you have 'something' at those locations that closely match your wavelet at this scale. 

(Of course, in your case, you have used noise, so the positions that I have talked about are random - that is, there is nothing really 'interesting' going on. Do a CWT with a sine pulse and what I am saying can be made clearer to you.)

In summary, the CWT is simply showing you all possible correlation scores between your template/matched filter (in this case daub-4 wavelet), at different positions, (x-axis) , also at different stretched factors, (y-axis).

Hope this helped.