CN-121995902-A - Temperature controller running state monitoring method based on edge data acquisition

CN121995902ACN 121995902 ACN121995902 ACN 121995902ACN-121995902-A

Abstract

The invention relates to the field of intelligent equipment monitoring, and discloses a temperature controller running state monitoring method based on edge data acquisition, which is used for solving the problem that equipment monitoring technology generally only depends on a single environmental parameter to judge equipment states, has certain limitations, comprises calculating comprehensive environmental coefficients through temperature, humidity and air quality, comparing with an environmental health risk threshold value to judge whether to carry out secondary judgment, the method comprises the steps of acquiring equipment state data through current, power, vibration and pressure sensors, calculating an abnormal coefficient of the comprehensive equipment, judging whether to give an alarm according to the abnormal coefficient of the comprehensive equipment, and when the temperature controller is judged to be abnormal, automatically adjusting working parameters of the temperature controller by using regression analysis and a prediction model to recover the equipment to a normal state.

Inventors

CHENG WENFENG
MEI JIANXIN
LI BIN

Assignees

南京精灵智控科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260211

Claims (7)

1. The temperature controller running state monitoring method based on edge data acquisition is characterized by comprising the following steps of; step one, acquiring temperature data, humidity data and air quality data through a temperature sensor, a humidity sensor and an air quality sensor, calculating the temperature data, the humidity data and the air quality data to obtain a comprehensive environment coefficient, and carrying out first abnormality judgment according to the comprehensive environment coefficient; if the first abnormality judgment result is abnormal, performing secondary judgment on the comprehensive environment data marked as abnormal by using the comprehensive equipment abnormality coefficient; Obtaining current data, power data, equipment vibration condition data and pressure data through a current sensor, a power sensor, an equipment vibration sensor and a pressure sensor, calculating the current load index, the power load index, the equipment vibration abnormality index and the pressure risk index through the current data, the power data, the equipment vibration condition data and the pressure data, calculating the current load index, the power load index, the equipment vibration abnormality index and the pressure risk index to obtain comprehensive equipment abnormality coefficients, and judging whether to alarm according to the comprehensive equipment abnormality coefficients; And step four, if the alarm is judged to be needed, using a regression analysis and prediction model automatic adjustment mechanism, and recovering the equipment to a normal working state by adjusting the temperature setting of the temperature controller and the power consumption of the equipment.
2. The method for monitoring the operation state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of acquiring the first abnormality determination according to the comprehensive environment coefficient is as follows: and comparing the comprehensive environmental index with an environmental health threshold, marking the environmental data as abnormal when the comprehensive environmental index is greater than or equal to the environmental health risk threshold, and marking the environmental data as normal when the comprehensive environmental index is less than the environmental health risk threshold.
3. The method for monitoring the operation state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of obtaining the abnormal coefficient of the comprehensive equipment is as follows: acquiring current data, wherein the current data comprises current intensity, current waveform, current frequency, current duration and current change rate, and evaluating according to the current data to obtain a current load index; acquiring power data, wherein the power data comprises instantaneous power, active power, reactive power, apparent power, power fluctuation and power energy consumption, and evaluating according to the power data to obtain a power load index; Acquiring equipment vibration condition data, wherein the equipment vibration condition data comprises vibration displacement, vibration frequency, vibration energy and duration time of vibration, and evaluating according to the equipment vibration condition data to obtain equipment vibration abnormality indexes; Acquiring pressure data, wherein the pressure data comprises absolute pressure, relative pressure, gas pressure and vacuum pressure, and evaluating according to the pressure data to obtain a pressure risk index; and carrying out normalization processing on the current load index, the power load index, the equipment vibration abnormality index and the pressure risk index, and calculating the normalized current load index, the normalized power load index, the normalized equipment vibration abnormality index and the normalized pressure risk index to obtain an integrated equipment abnormality coefficient.
4. The method for monitoring the running state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of obtaining the current load index is that the current intensity and the rated current value in the current data are obtained, and the ratio of the current intensity and the rated current value in the current data is calculated to obtain the current load ratio; acquiring a current waveform and a standard current wave in the current data, calculating a difference value between the current waveform and the standard current wave in the current data, and calculating a ratio between the current waveform and the standard current wave to obtain a waveform deviation coefficient; Acquiring the frequency of the current in the current data and the normal working frequency range of the current, calculating the difference value between the current frequency in the current data and the normal working frequency range of the current, and then calculating the ratio to obtain a current frequency deviation coefficient; Obtaining the allowable deviation of the current duration, the historical contemporaneous current duration and the historical contemporaneous current duration in the current data, and calculating the allowable deviation of the current duration, the historical contemporaneous current duration and the historical contemporaneous current duration in the current data to obtain a current duration anomaly coefficient, wherein the method comprises the following specific steps of: ; in the formula, As a coefficient of anomaly in the duration of the current, For the duration of the present current, For a historical contemporaneous current duration, Tolerance for historical contemporaneous current duration; obtaining the change rate and standard change rate of the current in the current data, calculating the difference value between the current change rate and the standard change rate in the current data, and calculating the ratio of the calculated difference value to the standard change rate to obtain a change rate deviation coefficient; And carrying out normalization processing on the current load ratio, the waveform deviation coefficient, the current frequency deviation coefficient, the current duration abnormal coefficient and the change rate deviation coefficient, and calculating the normalized current load ratio, the waveform deviation coefficient, the current frequency deviation coefficient, the current duration abnormal coefficient and the change rate deviation coefficient to obtain a current load index.
5. The method for monitoring the operation state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of obtaining the power load index is as follows: Acquiring instantaneous power and active power in the power data, and calculating the ratio of the instantaneous power to the active power in the power data to obtain a power efficiency coefficient; obtaining instantaneous power and reactive power in the power data, and calculating the instantaneous power and the reactive power in the power data to obtain a phase offset coefficient; acquiring apparent power and a standard apparent power value in power data, calculating a ratio after calculating a difference value between the apparent power and the standard apparent power value in the power data to obtain an apparent power deviation coefficient; acquiring power fluctuation and standard power fluctuation range in the power data, and calculating the power fluctuation and standard power fluctuation range in the power data to obtain a power fluctuation abnormal coefficient; Acquiring power energy consumption and total power energy consumption in the power data, and calculating the power energy consumption and the total power energy consumption in the power data to obtain abnormal power energy consumption coefficients; And carrying out normalization processing on the power efficiency coefficient, the phase offset coefficient, the power deviation coefficient, the power fluctuation abnormal coefficient and the power energy consumption abnormal coefficient, and calculating the normalized power efficiency coefficient, the phase offset coefficient, the power deviation coefficient, the power fluctuation abnormal coefficient and the power energy consumption abnormal coefficient to obtain a power load index.
6. The method for monitoring the operation state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of obtaining the equipment vibration abnormality index is as follows: Obtaining the vibration displacement distance in the equipment vibration condition data and the maximum tolerance vibration displacement distance under the normal operation condition, and calculating the ratio of the vibration displacement distance in the equipment vibration condition data to the maximum tolerance vibration displacement distance under the normal operation condition to obtain a vibration position ratio coefficient; obtaining the vibration frequency and the normal working frequency of the equipment in the equipment vibration condition data, and calculating the ratio of the vibration frequency and the normal working frequency of the equipment in the equipment vibration condition data to obtain a vibration frequency ratio coefficient; obtaining vibration energy in the equipment vibration condition data and a vibration energy standard value in normal operation, and calculating the ratio of the vibration energy in the equipment vibration condition data to the vibration energy standard value in normal operation to obtain a vibration energy ratio coefficient; Acquiring the duration time of vibration in the equipment vibration condition data and the allowed maximum vibration duration time, and calculating the ratio of the duration time of vibration in the equipment vibration condition data to the allowed maximum vibration duration time to obtain a vibration duration ratio coefficient; And normalizing the vibration position ratio coefficient, the vibration frequency ratio coefficient, the vibration energy ratio coefficient and the vibration duration ratio coefficient, and calculating the vibration position ratio coefficient, the vibration frequency ratio coefficient, the vibration energy ratio coefficient and the vibration duration ratio coefficient subjected to normalization treatment to obtain the equipment vibration abnormality index.
7. The method for monitoring the operation state of the temperature controller based on the edge data acquisition of claim 1, wherein the step of obtaining the pressure risk index is as follows: Acquiring absolute pressure in pressure data and normal working absolute pressure of equipment, and calculating the ratio of the absolute pressure in the pressure data to the normal working absolute pressure of the equipment to obtain an absolute pressure ratio coefficient; acquiring relative pressure in the pressure data and relative pressure for normal operation of the equipment, and calculating the ratio of the relative pressure in the pressure data to the relative pressure for normal operation of the equipment to obtain a relative pressure ratio coefficient; Acquiring gas pressure and maximum working gas pressure in pressure data, and calculating the ratio of the gas pressure in the pressure data to the maximum working gas pressure to obtain a gas pressure ratio coefficient; Obtaining the vacuum pressure and the maximum allowable vacuum pressure in the pressure data, and calculating the ratio of the vacuum pressure in the pressure data to the maximum allowable vacuum pressure to obtain a vacuum pressure ratio coefficient; And carrying out normalization processing on the pressure ratio coefficient, the relative pressure ratio coefficient, the gas pressure ratio coefficient and the vacuum pressure ratio coefficient, and calculating the normalized pressure ratio coefficient, the relative pressure ratio coefficient, the gas pressure ratio coefficient and the vacuum pressure ratio coefficient to obtain a pressure risk index.

Description

Temperature controller running state monitoring method based on edge data acquisition Technical Field The invention relates to the field of temperature controller adjustment, in particular to a temperature controller running state monitoring method based on edge data acquisition. Background With the wide application of industrial automation and intelligent equipment, the monitoring of the running state of the equipment and fault early warning gradually become important means for guaranteeing the normal running of the equipment, improving the efficiency and prolonging the service life of the equipment. In the prior art, equipment monitoring systems typically rely on monitoring environmental factors, particularly temperature, humidity, and real-time acquisition and analysis of air quality parameters. By means of these environmental parameter variations, the state of the art is able to determine and control to a certain extent the operating state of the equipment, for example, in air conditioning and hvac systems, temperature and humidity sensors are widely used to detect the comfort of the indoor environment, maintaining a set environmental condition by adjusting the equipment operation. Air quality sensors are used to monitor contaminants in air, such as carbon dioxide, volatile organic compounds, and particulates, which are commonly used to evaluate whether air quality meets health standards and to regulate ventilation and air cleaning equipment via automated systems. However, the existing equipment monitoring technology generally depends on only the single environmental parameter to determine the state of the equipment, and the method has certain limitation, especially when facing a complex equipment operation environment, the single environmental parameter is difficult to comprehensively reflect the actual operation condition of the equipment, a temperature control system, an air conditioning equipment and the like can operate under normal environmental conditions, the equipment is possibly damaged due to misjudgment caused by faults, uneven loads and the like, and the problems are often difficult to discover in time only by the change of temperature, humidity and air quality, so that the stability and long-term operation of the equipment are affected. However, the above technology has at least the following technical problems: The existing equipment monitoring technology generally only depends on the single environmental parameter to judge the equipment state, and the method has certain limitation, and can greatly enhance the intelligence of abnormal detection of the temperature controller by integrating various sensor data and combining multi-dimensional information from different sensors, so that the temperature controller can analyze and judge from multiple aspects, thereby improving the judgment accuracy and reducing the probability of false alarm and false alarm. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides a temperature controller running state monitoring method based on edge data acquisition, which aims to solve the problems in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: A temperature controller operation state monitoring method based on edge data acquisition comprises the steps of firstly obtaining temperature data, humidity data and air quality data through a temperature sensor, a humidity sensor and an air quality sensor, calculating the temperature data, the humidity data and the air quality data to obtain comprehensive environment coefficients, carrying out first abnormality judgment according to the comprehensive environment coefficients, secondly judging whether the comprehensive environment data marked as abnormal are subjected to secondary judgment by using the comprehensive equipment abnormality coefficients if the first abnormality judgment result is abnormal, thirdly obtaining the current data, the power data, the equipment vibration condition data and the pressure data through the current sensor, the power sensor, the equipment vibration condition data and the pressure sensor, calculating the current load index, the power load index, the equipment vibration abnormality index and the pressure risk index through the current data, the power data, the equipment vibration condition data and the pressure data, calculating the comprehensive equipment abnormality index, judging whether the comprehensive equipment abnormality coefficients are subjected to alarm or not according to the comprehensive equipment abnormality coefficients, and thirdly, automatically returning the temperature controller to a normal operation state through a prediction and a power consumption regulation model if the alarm is judged to be required, and setting the temperature controller to be in normal operation state. Preferably, the first abnormality determination is performed accordin