You'll want to use the dependency parse for this. You can see a visualisation of your example sentence using the displaCy visualiser.

You could implement the rules you need a few different ways — much like how there are always multiple ways to write an XPath query, DOM selector, etc.

Something like this should work:

nlp = spacy.load('en')
docs = [nlp(t) for t in text]
for i, doc in enumerate(docs):
    for j, sent in enumerate(doc.sents):
        subjects = [w for w in sent if w.dep_ == 'nsubj']
        for subject in subjects:
            numbers = [w for w in subject.lefts if w.dep_ == 'nummod']
            if len(numbers) == 1:
                print('document.sentence: {}.{}, subject: {}, action: {}, numbers: {}'.format(i, j, subject.text, subject.head.text, numbers[0].text))

For your examples in text you should get:

document.sentence: 0.0, subject: men, action: ran, numbers: 2
document.sentence: 0.0, subject: child, action: ran, numbers: 1
document.sentence: 0.1, subject: people, action: walking, numbers: 3
document.sentence: 1.0, subject: person, action: walking, numbers: One