我使用“
paramiko”将PC连接到开发板并执行脚本。然后，我将该脚本的结果保存在一个列表中（输出）。我想提取列表的一些值并将其插入到Elasticsearch中。我已经手动完成了列表的第一个结果。但是如何使其余的值自动化？我需要“正则表达式”吗？请给我一些线索。

谢谢

这是连接到开发板，执行脚本并检索列表=输出的代码的一部分

def main():
    ssh = initialize_ssh()
    stdin, stdout, stderr = ssh.exec_command('cd coral/tflite/python/examples/classification/Auto_benchmark\n python3 auto_benchmark.py')
    output = stdout.readlines()
    type(output)
    #print(type(output))
    print('\n'.join(output))
    ssh.close()

清单看起来像这样：

labels: imagenet_labels.txt

Model: efficientnet-edgetpu-S_quant_edgetpu.tflite

Image: img0000.jpg


----INFERENCE TIME----

Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.

Time: 6.2ms

Results: wall clock

Score: 0.25781

#####################################

labels: imagenet_labels.txt

Model: mobilenet_v1_1.0_224_quant_edgetpu.tflite

Image: img0000.jpg


----INFERENCE TIME----

Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.

Time: 2.8ms

Results: umbrella

Score: 0.22266

##################################### 
Temperature: 35C

这是将数据索引到ELASTICSSARCH中所需的映射

def initialize_mapping_classification(es):
    """
    Initialise les mappings
    """
    mapping_classification = {
        'properties': {
            '@timestamp': {'type': 'date'},
            'type': 'coralito',
            'Model': {'type': 'string'},
            'Time': {'type': 'float'},
            'Results': {'type': 'string'},
            'Score': {'type': 'float'},
            'Temperature': {'type': 'float'}
        }
    }

    if not es.indices.exists(CORAL):
        es.indices.create(CORAL)
        es.indices.put_mapping(body=mapping_classification, doc_type=DOC_TYPE, index=CORAL)

这是我的尝试。我已经根据清单的第一项结果进行了手动操作。我想自动化

if CLASSIFY == 1:

        doc = {
            '@timestamp':  str(datetime.datetime.utcnow().strftime("%Y-%m-%d"'T'"%H:%M:%S")),
            'type': 'coralito',
            'Model': "efficientnet-edgetpu-S_quant_edgetpu.tflite",
            'Time': "6.2 ms",
            'Results': "wall clock",
            'Score': "0.25781",
            'Temperature': "35 C"
        }

        response = send_data_elasticsearch(CORAL, DOC_TYPE, doc, es)

        print(doc)

------------------------------编辑2 ------------------ ---------------------

所以这是使用正则表达式提取感兴趣的值后我的数据的样子

这是我被索引的内容：

这是我的代码：

import elasticsearch  
from elasticsearch import Elasticsearch, helpers
import datetime
import re

data = ['labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-S_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 23.1\n', 'Time(ms): 5.7\n', '\n', '\n', 'Inference: corkscrew, bottle screw\n', 'Score: 0.03125 \n', '\n', 'TPU_temp(°C): 57.05\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-M_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 29.3\n', 'Time(ms): 10.8\n', '\n', '\n', "Inference: dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\n", 'Score: 0.09375 \n', '\n', 'TPU_temp(°C): 56.8\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: efficientnet-edgetpu-L_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 45.6\n', 'Time(ms): 31.0\n', '\n', '\n', 'Inference: pick, plectrum, plectron\n', 'Score: 0.09766 \n', '\n', 'TPU_temp(°C): 57.55\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v3_299_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 68.8\n', 'Time(ms): 51.3\n', '\n', '\n', 'Inference: ringlet, ringlet butterfly\n', 'Score: 0.48047 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v4_299_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 121.8\n', 'Time(ms): 101.2\n', '\n', '\n', 'Inference: admiral\n', 'Score: 0.59375 \n', '\n', 'TPU_temp(°C): 57.05\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v2_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 34.3\n', 'Time(ms): 16.6\n', '\n', '\n', 'Inference: lycaenid, lycaenid butterfly\n', 'Score: 0.41406 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: mobilenet_v2_1.0_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 14.4\n', 'Time(ms): 3.3\n', '\n', '\n', 'Inference: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea\n', 'Score: 0.36328 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: mobilenet_v1_1.0_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 14.5\n', 'Time(ms): 3.0\n', '\n', '\n', 'Inference: bow tie, bow-tie, bowtie\n', 'Score: 0.33984 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n', 'labels: imagenet_labels.txt \n', '\n', 'Model: inception_v1_224_quant_edgetpu.tflite \n', '\n', 'Image: insect.jpg \n', '\n', '*The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory*\n', 'Time(ms): 21.2\n', 'Time(ms): 3.6\n', '\n', '\n', 'Inference: pick, plectrum, plectron\n', 'Score: 0.17578 \n', '\n', 'TPU_temp(°C): 57.3\n', '##################################### \n', '\n']


# declare a client instance of the Python Elasticsearch library
client = Elasticsearch("http://localhost:9200")

#using regex 
regex = re.compile(r'(\w+)\((.+)\):\s(.*)|(\w+:)\s(.*)')
match_regex = list(filter(regex.match, data))
match = [line.rstrip('\n') for line in match_regex]


#using "bulk"
def yield_docs():
    """
    Initialise les mappings
    """

    doc_source = {
        "data": match

        }

    # use a yield generator so that the doc data isn't loaded into memory
    yield {
        "_index": "coralito",
        "_type": "coralote",
        "_source": doc_source
        }

try:
    # make the bulk call using 'actions' and get a response
    resp = helpers.bulk(
        client,
        yield_docs()
    )
    print ("\nhelpers.bulk() RESPONSE:", resp)
    print ("RESPONSE TYPE:", type(resp))
except Exception as err:
    print("\nhelpers.bulk() ERROR:", err)

-----------------------------编辑3 ------------------- -