Household heating water heater running cloud monitoring method and system thereof
A heating and hot water, cloud monitoring technology, applied in heating systems, signal transmission systems, heating methods, etc., can solve the problem of not being able to know the operation and safety of heating and water heaters in time, excessive CO, and user familiarity with product performance. lower problem
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Embodiment 1
[0024] Calculation and Analysis of Heat Load and Thermal Efficiency of Embodiment 1
[0025] ① The measured converted heat load is calculated according to the following formula:
[0026]
[0027] In the formula Φ——15℃, atmospheric pressure 101.3kPa, and the actual measured converted heat load under the gas dry state, kW;
[0028] Q l ——15℃, atmospheric pressure 101.3kPa reference gas low calorific value, MJ / Nm 3 ;
[0029] V——measured gas flow meter flow rate, m 3 / h;
[0030] P a — Atmospheric pressure during the test, in kPa;
[0031] P m ——The measured pressure of the gas passing through the gas flowmeter, in kPa;
[0032] P g ——The measured gas pressure in front of the water heater, kPa;
[0033] t g ——the temperature of the gas passing through the gas flowmeter at the time of measurement, °C;
[0034] d - the relative density of the dry test gas;
[0035] d r ——The relative density of the reference gas.
[0036] ②The thermal efficiency is calculated accord...
Embodiment 2
[0045] Example 2 Calculation of flue gas CO content and excess air coefficient α
[0046] ① The carbon monoxide content in the flue gas is calculated according to the following formula:
[0047]
[0048] where CO α=1 ——When the excess air coefficient is equal to 1, the carbon monoxide content in the dry flue gas, %;
[0049] CO' - carbon monoxide content in the flue gas sample, %;
[0050] CO" - carbon monoxide content in the indoor air, %;
[0051] o 2 ’——Oxygen content in the flue gas sample, %.
[0052] ②Excess air coefficient α is calculated according to the following formula:
[0053]
[0054] In the formula, α——excess air coefficient;
Embodiment 3
[0056] Embodiment 3 flame recognition
[0057] Based on Tenforflow and Faster-rcnn algorithm, the flame image obtained from real-time monitoring is recognized. Under the Windows system, build the program environment required for flame recognition: based on Python and Anaconda, based on the functions and databases provided by Tensorflow and the general algorithms for image processing and computer vision provided by opencv, to realize the programming of deep learning algorithms, and here On the basis, the Faster-rcnn algorithm is used to train, test and verify the flame images in the VOC data set (including training set and test set).
[0058] Flame recognition training method: For all models of heating water heaters integrated in the cloud server, 7 groups of videos were taken under different flame sizes, different distances, and different angles; each group of videos was decomposed to obtain 7000 single-frame pictures; all single-frame Screen the pictures, remove the pictures...
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