Temperature controller operation state monitoring method based on edge data acquisition

Abstract

The invention relates to the field of intelligent equipment monitoring, and discloses a temperature controller operation state monitoring method based on edge data acquisition, which is used for solving the problem that the equipment monitoring technology generally depends on single environmental parameters to judge the equipment state, and the method has certain limitations including temperature, humidity and air quality. Calculating a comprehensive environment coefficient, comparing the comprehensive environment coefficient with an environment health risk threshold value, judging whether secondary judgment is carried out or not, acquiring equipment state data through a current sensor, a power sensor, a vibration sensor and a pressure sensor, calculating a comprehensive equipment abnormal coefficient, and judging whether to give an alarm or not according to the comprehensive equipment abnormal coefficient; and the working parameters of the temperature controller are automatically adjusted by using the regression analysis and prediction model, the equipment is recovered to a normal state, and the method can improve the monitoring precision of the temperature controller, reduce false alarm and missing alarm and ensure stable operation of the equipment.

Description

Technical Field

[0001] This invention relates to the field of thermostat regulation, and more specifically to a method for monitoring the operating status of thermostats based on edge data acquisition. Background Technology

[0002] With the widespread application of industrial automation and intelligent equipment, monitoring equipment operating status and fault early warning have gradually become important means to ensure normal equipment operation, improve efficiency, and extend equipment life.

[0003] In existing technologies, equipment monitoring systems typically rely on the monitoring of environmental factors, particularly the real-time acquisition and analysis of parameters such as temperature, humidity, and air quality. By observing changes in these environmental parameters, existing technologies can assess and control the operating status of equipment to a certain extent. For example, in air conditioning and HVAC systems, temperature and humidity sensors are widely used to detect indoor environmental comfort levels and adjust equipment operation to maintain set environmental conditions. Air quality sensors are used to monitor air pollutants such as carbon dioxide, volatile organic compounds, and particulate matter. These indicators are commonly used to assess whether air quality meets health standards and to regulate ventilation and air purification equipment through automated systems.

[0004] However, existing equipment monitoring technologies typically rely solely on the aforementioned single environmental parameter to determine equipment status. This approach has certain limitations, especially when facing complex equipment operating environments. A single environmental parameter is insufficient to fully reflect the actual working condition of the equipment. Temperature control systems, air conditioning equipment, and other systems may malfunction and lead to misjudgments even when operating under normal environmental conditions. Equipment damage can occur due to electrical faults, uneven loads, and other reasons. Relying solely on changes in temperature, humidity, and air quality often fails to detect these problems in a timely manner, thereby affecting the stability and long-term operation of the equipment.

[0005] However, the above-mentioned technologies have at least the following technical problems: Existing equipment monitoring technologies typically rely solely on the aforementioned single environmental parameter to determine equipment status. This approach has certain limitations. By integrating data from multiple sensors and combining multi-dimensional information from different sensors, the intelligence of temperature controller anomaly detection can be greatly enhanced, enabling it to analyze and judge from multiple perspectives, thereby improving the accuracy of judgment and reducing the probability of false alarms and missed alarms. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for monitoring the operating status of a temperature controller based on edge data acquisition, so as to solve the problems existing in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for monitoring the operating status of a thermostat based on edge data acquisition includes the following steps: Step 1: Acquire temperature data, humidity data, and air quality data through temperature sensors, humidity sensors, and air quality sensors; calculate a comprehensive environmental coefficient based on the temperature data, humidity data, and air quality data; perform a first anomaly judgment based on the comprehensive environmental coefficient; Step 2: If the first anomaly judgment result is abnormal, perform a second judgment using a comprehensive equipment anomaly coefficient on the comprehensive environmental data marked as abnormal; Step 3: Acquire current data, power data, equipment vibration data, and pressure data through current sensors, power sensors, equipment vibration sensors, and pressure sensors; calculate a current load index, a power load index, an equipment vibration anomaly index, and a pressure risk index based on the current load index, power load index, equipment vibration anomaly index, and pressure risk index; calculate a comprehensive equipment anomaly coefficient based on the current load index, power load index, equipment vibration anomaly index, and pressure risk index; determine whether to issue an alarm based on the comprehensive equipment anomaly coefficient; Step 4: If an alarm is determined to be necessary, use a regression analysis and prediction model automatic adjustment mechanism to restore the equipment to normal operating status by adjusting the temperature setting of the thermostat and the power consumption of the equipment.

[0008] Preferably, the steps for obtaining the first anomaly determination based on the comprehensive environmental coefficient are as follows: compare the comprehensive environmental index with the environmental health threshold; when the comprehensive environmental index is greater than or equal to the environmental health risk threshold, mark the environmental data as abnormal; when the comprehensive environmental index is less than the environmental health risk threshold, mark the environmental data as normal.

[0009] Preferably, the steps for obtaining the comprehensive equipment anomaly coefficient are as follows: acquiring current data, including current intensity, current waveform, current frequency, current duration, and current rate of change, and evaluating the current load index based on the current data; acquiring power data, including instantaneous power, active power, reactive power, apparent power, power fluctuation, and power consumption, and evaluating the power load index based on the power data; acquiring equipment vibration data, including vibration displacement, vibration frequency, vibration energy, and vibration duration, and evaluating the equipment vibration anomaly index based on the equipment vibration data; acquiring pressure data, including absolute pressure, relative pressure, gas pressure, and vacuum pressure, and evaluating the pressure risk index based on the pressure data; normalizing the current load index, power load index, equipment vibration anomaly index, and pressure risk index, and calculating the comprehensive equipment anomaly coefficient based on the normalized current load index, power load index, equipment vibration anomaly index, and pressure risk index.

[0010] Preferably, the steps for obtaining the current load index are as follows: Obtain the current intensity and rated current value from the current data; calculate the current load ratio by comparing the current intensity and rated current value in the current data; obtain the current waveform and standard current waveform from the current data; calculate the difference between the current waveform and the standard current waveform and then compare it with the standard current waveform to obtain the waveform deviation coefficient; obtain the current frequency and normal operating frequency range from the current data; calculate the current frequency and then compare it with the normal operating frequency range in the current data to obtain the current frequency deviation coefficient; obtain the current duration, historical current duration, and allowable deviation of the current duration from the current data; calculate the current duration anomaly coefficient by comparing the current duration, historical current duration, and allowable deviation of the current duration from the current data. The specific steps are as follows: In the formula, This is the current duration anomaly coefficient. For the duration of the current, For the duration of current during the same historical period, The allowable deviation of current duration during the same historical period is determined. The rate of change of current in the current data and the standard rate of change are obtained. The difference between the rate of change of current in the current data and the standard rate of change is calculated, and the ratio of the difference to the standard rate of change is calculated to obtain the rate of change deviation coefficient. The current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient are normalized. The current load index is calculated from the normalized current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient.

[0011] Preferably, the steps for obtaining the power load index are as follows: obtaining instantaneous power and active power from the power data, and calculating the ratio of instantaneous power to active power to obtain a power efficiency coefficient; obtaining instantaneous power and reactive power from the power data, and calculating the phase offset coefficient; obtaining apparent power and standard apparent power values ​​from the power data, and calculating the ratio of the difference between apparent power and standard apparent power values ​​to obtain an apparent power deviation coefficient; obtaining power fluctuation and standard power fluctuation range from the power data, and calculating a power fluctuation anomaly coefficient; obtaining power energy consumption and total power energy consumption from the power data, and calculating a power energy consumption anomaly coefficient; normalizing the power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient, and calculating the power load index from the normalized power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient.

[0012] Preferably, the steps for obtaining the equipment vibration anomaly index are as follows: obtaining the vibration displacement distance and the maximum tolerable vibration displacement distance under normal operating conditions from the equipment vibration data, and calculating the vibration position ratio coefficient by comparing the vibration displacement distance and the maximum tolerable vibration displacement distance under normal operating conditions; obtaining the vibration frequency and the normal operating frequency from the equipment vibration data, and calculating the vibration frequency ratio coefficient by comparing the vibration frequency and the normal operating frequency; obtaining the vibration energy and the standard value of vibration energy under normal operating conditions from the equipment vibration data, and calculating the vibration energy ratio coefficient by comparing the vibration energy and the standard value of vibration energy under normal operating conditions; obtaining the vibration duration and the maximum allowable vibration duration from the equipment vibration data, and calculating the vibration duration ratio coefficient by comparing the vibration duration and the maximum allowable vibration duration; normalizing the vibration position ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient, and calculating the equipment vibration anomaly index by normalizing the normalized vibration position ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient.

[0013] Preferably, the steps for obtaining the pressure risk index are as follows: obtaining the absolute pressure in the pressure data and the normal operating absolute pressure of the equipment, and calculating the absolute pressure ratio coefficient by comparing the absolute pressure in the pressure data and the normal operating absolute pressure of the equipment; obtaining the relative pressure in the pressure data and the normal operating relative pressure of the equipment, and calculating the relative pressure ratio coefficient by comparing the relative pressure in the pressure data and the normal operating relative pressure of the equipment; obtaining the gas pressure in the pressure data and the maximum working gas pressure, and calculating the gas pressure ratio coefficient by comparing the gas pressure in the pressure data and the maximum working gas pressure; obtaining the vacuum pressure in the pressure data and the maximum allowable vacuum pressure, and calculating the vacuum pressure ratio coefficient by comparing the vacuum pressure in the pressure data and the maximum allowable vacuum pressure; normalizing the pressure ratio coefficient, relative pressure ratio coefficient, gas pressure ratio coefficient, and vacuum pressure ratio coefficient, and calculating the pressure risk index using the normalized pressure ratio coefficient, relative pressure ratio coefficient, gas pressure ratio coefficient, and vacuum pressure ratio coefficient.

[0014] The technical effects and advantages of this invention are as follows: By calculating a comprehensive environmental coefficient based on temperature, humidity, and air quality, and comparing it with an environmental health risk threshold, a secondary assessment is determined. Equipment status data is acquired using current, power, vibration, and pressure sensors, and a comprehensive equipment anomaly coefficient is calculated. Based on this coefficient, an alarm is issued. When a temperature controller malfunction is detected, regression analysis and a predictive model are used to automatically adjust the controller's operating parameters, restoring the equipment to normal operation. This method improves the monitoring accuracy of the temperature controller, reduces false alarms and missed alarms, and ensures stable equipment operation. Attached Figure Description

[0015] Figure 1 A flowchart illustrating a method for intelligent temperature control based on multi-source environmental perception, provided in this application embodiment. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The method for monitoring the operating status of a temperature controller based on edge data acquisition involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] This invention provides a method for monitoring the operating status of a temperature controller based on edge data acquisition, such as... Figure 1 As shown, it includes the following steps: Step 1: Acquire temperature, humidity, and air quality data using temperature, humidity, and air quality sensors. Calculate the comprehensive environmental coefficient based on the temperature, humidity, and air quality data, and then make the first anomaly determination based on the comprehensive environmental coefficient.

[0018] In this embodiment, it should be noted that the specific steps for obtaining the comprehensive environmental coefficient are as follows: The actual ambient temperature and the suitable temperature set value are obtained from the temperature data. The temperature deviation coefficient is obtained by calculating the difference between the suitable temperature value and the actual temperature value in the temperature data and then calculating the ratio with the suitable temperature value. Obtain the actual ambient humidity and suitable humidity values ​​from the humidity data. Calculate the difference between the suitable humidity value and the actual ambient humidity value, and then calculate the ratio of this difference to the suitable humidity value to obtain the humidity deviation coefficient. The air circulation index and suitable air quality value are obtained from the air quality data. The difference between the air circulation index and the suitable air quality value is calculated and then the ratio of the difference to the suitable air quality value is calculated to obtain the air quality deviation coefficient. The temperature deviation coefficient, humidity deviation coefficient, and air quality deviation coefficient are normalized. The normalized temperature deviation coefficient, humidity deviation coefficient, and air quality deviation coefficient are then used to calculate the comprehensive environmental coefficient. The specific steps are as follows: ; In the formula, It is a comprehensive environmental data index. This is the temperature deviation coefficient. This is the temperature deviation coefficient. The air quality deviation coefficient is calculated by multiplication and square root operations to balance the contributions of the three indicators, avoid the dominance of a single indicator in the results, and quantify the overall reliability of the data.

[0019] In this embodiment, it should be noted that the first anomaly determination is made based on the comprehensive environmental coefficient, and the specific steps are as follows: The comprehensive environmental index is compared with the environmental health threshold. When the comprehensive environmental index is greater than or equal to the environmental health risk threshold, the environmental data is marked as abnormal. When the comprehensive environmental index is less than the environmental health risk threshold, the environmental data is marked as normal. It should be noted that the environmental health risk threshold is a critical value that may have a significant negative impact on human health when the concentration of pollutants, temperature, and humidity reach or exceed a certain value under specific environmental conditions.

[0020] Step 2: If the first anomaly determination result is anomaly, the comprehensive environmental data marked as anomaly will be used for a second determination using the comprehensive equipment anomaly coefficient.

[0021] Step 3: Acquire current data, power data, equipment vibration data, and pressure data using current sensors, power sensors, equipment vibration sensors, and pressure sensors. Calculate the current load index, power load index, equipment vibration anomaly index, and pressure risk index using these data. Then, calculate the comprehensive equipment anomaly coefficient based on the comprehensive equipment anomaly coefficient to determine whether to trigger an alarm.

[0022] In this embodiment, it should be noted that the specific steps for obtaining the comprehensive equipment anomaly coefficient are as follows: Acquire current data, including current intensity, current waveform, current frequency, current duration, and current rate of change. Evaluate the current load index based on the current data. Acquire power data, including instantaneous power, active power, reactive power, apparent power, power fluctuation, and power consumption, and evaluate the power load index based on the power data; Acquire equipment vibration data, including vibration displacement, vibration frequency, vibration energy, and vibration duration. Evaluate the equipment vibration anomaly index based on the equipment vibration data. Acquire pressure data, including absolute pressure, relative pressure, gas pressure, and vacuum pressure, and assess the pressure risk index based on the pressure data; The current load index, power load index, equipment vibration anomaly index, and pressure risk index are normalized. The normalized current load index, power load index, equipment vibration anomaly index, and pressure risk index are then used to calculate the comprehensive equipment anomaly coefficient. The specific steps are as follows: ; In the formula, The comprehensive equipment anomaly coefficient, For current load index, This is the power load index. The abnormal vibration index of the equipment. The pressure risk index is calculated using the geometric mean formula. The normalized current load index, power load index, equipment vibration anomaly index, and pressure risk index are multiplied together, and the fourth root is taken to obtain a comprehensive equipment anomaly index. This can avoid the excessive influence of a single anomaly value on the final result, because all indices are equally important and the comprehensive index will not fluctuate drastically due to the extreme value of a single index.

[0023] In this embodiment, it should be noted that the specific steps for obtaining the current load index are as follows: Obtain the current intensity and rated current value from the current data, and calculate the current load ratio by comparing the current intensity and rated current value in the current data. Obtain the current waveform and standard current waveform from the current data, calculate the difference between the current waveform and the standard current waveform, and then calculate the ratio of the difference to the standard current waveform to obtain the waveform deviation coefficient. Obtain the frequency of the current and the normal operating frequency range of the current from the current data. Calculate the difference between the current frequency and the normal operating frequency range of the current data and then calculate the ratio to obtain the current frequency deviation coefficient. Obtain the current duration, historical current duration for the same period, and allowable deviation of historical current duration from the current data. Calculate the current duration anomaly coefficient from the current duration, historical current duration for the same period, and allowable deviation of historical current duration from the current data. The specific steps are as follows: ; In the formula, This is the current duration anomaly coefficient. For the duration of the current, For the duration of current during the same historical period, This represents the allowable deviation in the duration of current during the same historical period; Obtain the rate of change of current in the current data and the standard rate of change. Calculate the difference between the rate of change of current in the current data and the standard rate of change, and then calculate the ratio of the difference to the standard rate of change to obtain the rate of change deviation coefficient. The current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient are normalized. The current load index is then calculated from the normalized current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient. The specific steps are as follows: ; In the formula, For current load index, This is the current load ratio. The waveform deviation coefficient is... This is the current frequency deviation coefficient. This is the current duration anomaly coefficient. The rate of change deviation coefficient is calculated using a square root formula. By multiplying the coefficients and taking the cube root, the influence of the five types of coefficients on the result is balanced. The matching degree of the current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient is considered. The number of indicators is used to evaluate the total number of the five core indicators: current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient.

[0024] In this embodiment, it should be noted that the specific steps for obtaining the power load index are as follows: Obtain the instantaneous power and active power from the power data, and calculate the power efficiency coefficient by comparing the instantaneous power and active power in the power data. Obtain the instantaneous power and reactive power from the power data, and calculate the phase offset coefficient from the instantaneous power and reactive power in the power data; Obtain the apparent power and standard apparent power values ​​from the power data, calculate the difference between the apparent power and standard apparent power values ​​from the power data, and then calculate the ratio to obtain the apparent power deviation coefficient. Obtain the power fluctuation range and standard power fluctuation range from the power data, and calculate the power fluctuation anomaly coefficient from the power fluctuation range and standard power fluctuation range. Obtain the power energy consumption and total power energy consumption from the power data, and calculate the power energy consumption anomaly coefficient from the power energy consumption and total power energy consumption in the power data. The power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient are normalized. The power load index is then calculated from the normalized power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient. The specific steps are as follows: ; In the formula, This is the power load index. The power efficiency coefficient, This is the phase offset coefficient. This is the power deviation coefficient. The power fluctuation anomaly coefficient, The energy consumption anomaly coefficient is calculated using the root mean square formula, which amplifies the contribution of significantly abnormal indicators through squaring, highlighting the main problems and making it more sensitive to coefficients that significantly deviate from the normal range. The power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient are calculated uniformly.

[0025] In this embodiment, it should be noted that the specific steps for obtaining the equipment vibration anomaly index are as follows: The vibration displacement distance in the equipment vibration data is compared with the maximum tolerable vibration displacement distance under normal operating conditions. The ratio of the vibration displacement distance in the equipment vibration data to the maximum tolerable vibration displacement distance under normal operating conditions is calculated to obtain the vibration position ratio coefficient. Obtain the vibration frequency and normal operating frequency of the equipment from the vibration data, and calculate the vibration frequency ratio coefficient by comparing the vibration frequency in the equipment vibration data with the normal operating frequency of the equipment. Obtain the vibration energy from the equipment vibration data and the standard value of vibration energy during normal operation. Calculate the vibration energy ratio coefficient by comparing the vibration energy from the equipment vibration data with the standard value of vibration energy during normal operation. Obtain the duration of vibration and the maximum allowable duration of vibration from the equipment vibration data, and calculate the vibration duration ratio coefficient by comparing the duration of vibration in the equipment vibration data with the maximum allowable duration of vibration. The vibration location ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient are normalized. The normalized vibration location ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient are then used to calculate the equipment vibration anomaly index. The steps for obtaining this index are as follows: ; In the formula, The abnormal vibration index of the equipment. This is the vibration position ratio coefficient. It is the ratio coefficient of vibration frequency. This is the vibration energy ratio coefficient. The vibration duration ratio coefficient is used to balance the contributions of the four indicators through multiplication and square root operations, avoiding the dominance of a single indicator in the results and quantifying the overall reliability of the data.

[0026] In this embodiment, it should be noted that the specific steps for obtaining the stress risk index are as follows: Obtain the absolute pressure from the pressure data and the normal operating absolute pressure of the equipment. Calculate the absolute pressure ratio coefficient by comparing the absolute pressure from the pressure data with the normal operating absolute pressure of the equipment. Obtain the relative pressure from the pressure data and the relative pressure during normal operation of the equipment. Calculate the ratio of the relative pressure from the pressure data to the relative pressure during normal operation of the equipment to obtain the relative pressure ratio coefficient. Obtain the gas pressure and maximum working gas pressure from the pressure data, and calculate the gas pressure ratio coefficient by comparing the gas pressure in the pressure data with the maximum working gas pressure. Obtain the vacuum pressure and the maximum allowable vacuum pressure from the pressure data, and calculate the vacuum pressure ratio coefficient by comparing the vacuum pressure and the maximum allowable vacuum pressure in the pressure data. The pressure ratio coefficient, relative pressure ratio coefficient, gas pressure ratio coefficient, and vacuum pressure ratio coefficient are normalized. The pressure risk index is then calculated from these normalized coefficients. The specific steps are as follows: ; In the formula, The normalized stress risk index This is the normalized pressure ratio coefficient. This is the normalized relative pressure ratio coefficient. This is the normalized gas pressure ratio coefficient. This is the normalized vacuum pressure ratio coefficient. , , , These are the weighting coefficients for the normalized pressure ratio coefficient, the normalized relative pressure ratio coefficient, the normalized gas pressure ratio coefficient, and the normalized vacuum pressure ratio coefficient. It should be noted that... , , , It is calculated using the weighted summation method, a simple and direct method that assigns a weight to each factor, with the weight value set based on judgment or historical data.

[0027] In this embodiment, it should be noted that the specific steps for determining whether to issue an alarm based on the comprehensive equipment anomaly coefficient are as follows: The system compares the overall equipment anomaly coefficient with the thermostat status threshold. When the overall equipment anomaly coefficient is greater than or equal to the thermostat status threshold, the thermostat is determined to be in an abnormal operating state, and an alarm is immediately triggered. When the overall equipment anomaly coefficient is less than the thermostat status threshold, the thermostat is determined to be in a normal operating state, and no alarm is triggered. It should be noted that the thermostat status threshold is set with operating parameters and safety limits based on the expected usage environment, equipment specifications, and performance requirements. These parameters are typically used to determine the operating status threshold.

[0028] Step four: If an alarm is deemed necessary, an automatic adjustment mechanism using regression analysis and a predictive model is employed to restore the equipment to normal operating condition by adjusting the temperature setting of the thermostat and the power consumption of the equipment.

[0029] In this embodiment, it should be noted that the specific steps of using regression analysis and the automatic adjustment mechanism of the prediction model are as follows: Before executing the regression analysis and prediction model, historical data is collected from the temperature sensor, humidity sensor, air quality sensor, current sensor, power sensor, vibration sensor, and pressure sensor of the thermostat. This data helps the regression analysis and prediction model understand the normal operating mode of the thermostat and the impact of various environmental factors on the equipment performance. It is used to analyze the causes of problems and take appropriate measures. The core purpose of the regression analysis and prediction model is to describe the relationship between independent and dependent variables by establishing a mathematical model. The most common regression analysis is linear regression, which assumes that there is a linear relationship between independent and dependent variables. Through methods such as least squares, the regression analysis calculates the regression coefficients, which can then predict the value of the dependent variable. It should be noted that the regression analysis and prediction model is existing technology and will not be explained in detail in this embodiment.

[0030] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0031] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the operating status of a temperature controller based on edge data acquisition, characterized in that, Includes the following steps; Step 1: Acquire temperature data, humidity data, and air quality data through temperature sensors, humidity sensors, and air quality sensors. Calculate the comprehensive environmental coefficient based on the temperature data, humidity data, and air quality data, and make the first anomaly judgment based on the comprehensive environmental coefficient. Step 2: If the first anomaly determination result is anomaly, the comprehensive environmental data marked as anomaly will be used for a second determination using the comprehensive equipment anomaly coefficient. Step 3: Acquire current data, power data, equipment vibration data, and pressure data through current sensors, power sensors, equipment vibration sensors, and pressure sensors. Calculate the current load index, power load index, equipment vibration anomaly index, and pressure risk index based on the current data, power data, equipment vibration anomaly index, and pressure risk index. Calculate the comprehensive equipment anomaly coefficient from the current load index, power load index, equipment vibration anomaly index, and pressure risk index. Determine whether to issue an alarm based on the comprehensive equipment anomaly coefficient. Step four: If an alarm is deemed necessary, an automatic adjustment mechanism using regression analysis and a predictive model is employed to restore the equipment to normal operating condition by adjusting the temperature setting of the thermostat and the power consumption of the equipment.

2. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the first anomaly determination based on the comprehensive environmental coefficient are as follows: The comprehensive environmental index is compared with the environmental health threshold. When the comprehensive environmental index is greater than or equal to the environmental health risk threshold, the environmental data is marked as abnormal. When the comprehensive environmental index is less than the environmental health risk threshold, the environmental data is marked as normal.

3. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the integrated equipment anomaly coefficient are as follows: Acquire current data, including current intensity, current waveform, current frequency, current duration, and current rate of change. Evaluate the current load index based on the current data. Acquire power data, including instantaneous power, active power, reactive power, apparent power, power fluctuation, and power consumption, and evaluate the power load index based on the power data; Acquire equipment vibration data, including vibration displacement, vibration frequency, vibration energy, and vibration duration. Evaluate the equipment vibration anomaly index based on the equipment vibration data. Acquire pressure data, including absolute pressure, relative pressure, gas pressure, and vacuum pressure, and assess the pressure risk index based on the pressure data; The current load index, power load index, equipment vibration anomaly index, and pressure risk index are normalized, and the comprehensive equipment anomaly coefficient is calculated from the normalized current load index, power load index, equipment vibration anomaly index, and pressure risk index.

4. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the current load index are as follows: obtain the current intensity and rated current value from the current data, and calculate the current load ratio by comparing the current intensity and rated current value in the current data. Obtain the current waveform and standard current waveform from the current data, calculate the difference between the current waveform and the standard current waveform, and then calculate the ratio of the difference to the standard current waveform to obtain the waveform deviation coefficient. Obtain the frequency of the current and the normal operating frequency range of the current from the current data. Calculate the difference between the current frequency and the normal operating frequency range of the current data and then calculate the ratio to obtain the current frequency deviation coefficient. Obtain the current duration, historical current duration for the same period, and allowable deviation of historical current duration from the current data. Calculate the current duration anomaly coefficient from the current duration, historical current duration for the same period, and allowable deviation of historical current duration from the current data. The specific steps are as follows: ； In the formula, This is the current duration anomaly coefficient. For the duration of the current, For the duration of current during the same historical period, This represents the allowable deviation in the duration of current during the same historical period; Obtain the rate of change of current in the current data and the standard rate of change. Calculate the difference between the rate of change of current in the current data and the standard rate of change, and then calculate the ratio of the difference to the standard rate of change to obtain the rate of change deviation coefficient. The current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient are normalized. The current load index is then calculated from the normalized current load ratio, waveform deviation coefficient, current frequency deviation coefficient, current duration anomaly coefficient, and rate of change deviation coefficient.

5. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the power load index are as follows: Obtain the instantaneous power and active power from the power data, and calculate the power efficiency coefficient by comparing the instantaneous power and active power in the power data. Obtain the instantaneous power and reactive power from the power data, and calculate the phase offset coefficient from the instantaneous power and reactive power in the power data; Obtain the apparent power and standard apparent power values ​​from the power data, calculate the difference between the apparent power and standard apparent power values ​​from the power data, and then calculate the ratio to obtain the apparent power deviation coefficient. Obtain the power fluctuation range and standard power fluctuation range from the power data, and calculate the power fluctuation anomaly coefficient from the power fluctuation range and standard power fluctuation range. Obtain the power energy consumption and total power energy consumption from the power data, and calculate the power energy consumption anomaly coefficient from the power energy consumption and total power energy consumption in the power data. The power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient are normalized. The power load index is then calculated from the normalized power efficiency coefficient, phase offset coefficient, power deviation coefficient, power fluctuation anomaly coefficient, and power energy consumption anomaly coefficient.

6. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the abnormal vibration index of the equipment are as follows: The vibration displacement distance in the equipment vibration data is compared with the maximum tolerable vibration displacement distance under normal operating conditions. The ratio of the vibration displacement distance in the equipment vibration data to the maximum tolerable vibration displacement distance under normal operating conditions is calculated to obtain the vibration position ratio coefficient. Obtain the vibration frequency and normal operating frequency of the equipment from the vibration data, and calculate the vibration frequency ratio coefficient by comparing the vibration frequency in the equipment vibration data with the normal operating frequency of the equipment. Obtain the vibration energy from the equipment vibration data and the standard value of vibration energy during normal operation. Calculate the vibration energy ratio coefficient by comparing the vibration energy from the equipment vibration data with the standard value of vibration energy during normal operation. Obtain the duration of vibration and the maximum allowable duration of vibration from the equipment vibration data, and calculate the vibration duration ratio coefficient by comparing the duration of vibration in the equipment vibration data with the maximum allowable duration of vibration. The vibration location ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient are normalized. The normalized vibration location ratio coefficient, vibration frequency ratio coefficient, vibration energy ratio coefficient, and vibration duration ratio coefficient are then used to calculate the equipment vibration anomaly index.

7. The method for monitoring the operating status of a temperature controller based on edge data acquisition according to claim 1, characterized in that: The steps for obtaining the stress risk index are as follows: Obtain the absolute pressure from the pressure data and the normal operating absolute pressure of the equipment. Calculate the absolute pressure ratio coefficient by comparing the absolute pressure from the pressure data with the normal operating absolute pressure of the equipment. Obtain the relative pressure from the pressure data and the relative pressure during normal operation of the equipment. Calculate the ratio of the relative pressure from the pressure data to the relative pressure during normal operation of the equipment to obtain the relative pressure ratio coefficient. Obtain the gas pressure and maximum working gas pressure from the pressure data, and calculate the gas pressure ratio coefficient by comparing the gas pressure in the pressure data with the maximum working gas pressure. Obtain the vacuum pressure and the maximum allowable vacuum pressure from the pressure data, and calculate the vacuum pressure ratio coefficient by comparing the vacuum pressure and the maximum allowable vacuum pressure in the pressure data. The pressure ratio coefficient, relative pressure ratio coefficient, gas pressure ratio coefficient, and vacuum pressure ratio coefficient are normalized, and the pressure risk index is calculated from the normalized pressure ratio coefficient, relative pressure ratio coefficient, gas pressure ratio coefficient, and vacuum pressure ratio coefficient.

Images