Analysis and evaluation algorithm for temperature regulation effect of variable air volume air conditioner terminal device

Abstract

The invention discloses an analysis and evaluation algorithm for the temperature regulation effect of a variable air volume air conditioner terminal device, and relates to the technical field of air conditioner regulation. Generating a temperature evaluation parameter and an air valve evaluation parameter based on the operation data; based on the temperature evaluation parameters and the air valve evaluation parameters, establishing a joint judgment model; according to the method, the energy waste working condition of counteracting the regional cold and hot demand can be found in time, the passive situation that treatment is carried out after the temperature is seriously deviated is avoided through early recognition and root diagnosis of the abnormal track, and the method is suitable for large-scale popularization and application. And the indoor comfort is improved.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning regulation technology, specifically an algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device. Background Technology

[0002] Variable air volume (VAV) air conditioning systems regulate the indoor environment through terminal devices. The core principle is that the terminal devices collect the measured indoor temperature, compare it with the temperature setpoint, calculate the required air volume, and control the measured air volume by adjusting the opening of the air valve to make it close to the required air volume, thereby achieving temperature control. However, in actual operation, when the air valve is fully open but the air supply still cannot meet the demand, or when the air valve is limited to its minimum opening but the temperature still deviates excessively, the terminal device itself is unable to make effective adjustments, and the system is actually in a state of adjustment failure. Existing technology lacks an automatic analysis and evaluation mechanism for such adjustment effects, relying entirely on maintenance personnel to manually observe data to discover and locate problems. This method is inefficient and highly dependent on personnel experience and a sense of responsibility, making it difficult to quickly and comprehensively identify all abnormal terminals in complex air conditioning systems, leading to delayed maintenance response and impacting environmental control quality and energy efficiency. Therefore, there is an urgent need in this field for a technical solution that can automatically and accurately analyze and evaluate the temperature regulation effect of variable air volume terminal devices, in order to overcome the various drawbacks caused by relying on manual judgment. Summary of the Invention

[0003] The purpose of this invention is to provide an algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device, so as to solve the problems mentioned in the background art.

[0004] An algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device, comprising: Collect operational data from variable air volume (VAV) air conditioning terminal units; Based on the aforementioned operational data, temperature evaluation parameters and damper evaluation parameters are generated. Based on the temperature evaluation parameters and the air valve evaluation parameters, a joint judgment model is established; Extract the coordinated change characteristics of the temperature evaluation parameters and the damper evaluation parameters over time series to generate adjustment trajectory feature parameters; When the adjusted trajectory feature parameters identify a preset abnormal trajectory pattern, the enhanced diagnostic state of the joint judgment model is activated; In the enhanced diagnostic state, an optimized assessment conclusion on the temperature regulation effect is output.

[0005] Furthermore, the step of extracting cooperative variation features to generate adjustment trajectory feature parameters includes: Establish a dynamic behavior phase diagram with temperature evaluation parameters as the vertical axis and damper evaluation parameters as the horizontal axis; In the dynamic behavior phase diagram, the region where the system's operating trajectory is located is identified; Among them, the quadrants corresponding to the operating states where the trajectory is continuously distributed in the same direction as the operating states with high temperature difference and high valve opening, or high temperature difference and low valve opening, are defined as abnormal trajectory modes.

[0006] Furthermore, after triggering the enhanced diagnostic state, the process further includes an attribution analysis step for the abnormal trajectory pattern: Obtain operational data from other terminal units served by the same air handling unit; Comparative analysis is conducted: if other terminal devices simultaneously exhibit similar abnormal trajectory patterns, the abnormality is attributed to pressure imbalance in the central air conditioning unit or air system; if only this terminal device exhibits an abnormality, it is attributed to a malfunction in the terminal device itself or an abnormal load in the room.

[0007] Furthermore, the step of outputting the optimized evaluation conclusion specifically includes: Based on the duration and deviation rate of the abnormal trajectory pattern, the urgency index of regulation failure is calculated. Based on the urgency index, the output format and push strategy of the final evaluation conclusion are dynamically adjusted. Specifically, when the urgency index exceeds the first threshold, the conclusion is pushed to the central monitoring console in the form of a pop-up alert; when it exceeds a higher second threshold, the conclusion is attached to the system maintenance work order system and a processing priority is specified.

[0008] Furthermore, in the enhanced diagnostic state, the model performs the following operations: Temporarily release the airflow setting limit of the terminal device and instruct its air valve to perform a full-open to full-close limit test within a short period of time; Monitor and record the range of changes in measured air volume during extreme testing and its ultimate impact on room temperature; Based on the test data, the intrinsic relationship model between the damper adjustment capability and temperature control efficiency of the terminal device in the joint judgment model is calibrated, and the calibrated model is used for optimization evaluation.

[0009] Furthermore, a long-term degradation assessment model for the regulation performance of the terminal device is established: During stable system operation, the average time required for the terminal device to adjust the room temperature to the set value and the corresponding average air valve opening are recorded periodically. The average adjustment time and the average damper opening are used to form a performance vector, which is then stored in the historical database in chronological order. The evolution path of the performance vector is analyzed by applying a trend fitting algorithm; when the fitting curve shows that the opening of the damper required to achieve the same adjustment effect continues to increase and the adjustment time is extended synchronously, an early warning is generated that the mechanical performance of the damper actuator of the terminal device is deteriorating or the air duct resistance is increasing. This warning is intended to prompt that mechanical inspections and duct cleaning of the terminal unit be prioritized in the next planned maintenance cycle.

[0010] Furthermore, implement control logic conflict detection for end-group collaborative states: When the joint judgment model identifies that a certain terminal device has entered the enhanced diagnostic state, it simultaneously detects the operating modes of other terminal devices located in the same building area at the same time. If it is found that more than one terminal device is operating in conflicting modes, including one terminal having its air valve fully open because it needs to cool, while the adjacent terminal has its air valve fully open because it needs to heat, then it is determined that there is an energy waste condition caused by the offsetting of regional cooling and heating demands. At this point, instead of directly adjusting the terminal air valve, a high-level alarm is sent to the central control system, indicating that there may be a large area of ​​open windows, extremely uneven distribution of internal heat sources, or conflicting thermostat settings in the area, and manual verification and intervention are recommended.

[0011] Furthermore, a cross-validation and adaptive fault-tolerance mechanism for latent faults in temperature sensors is introduced: This mechanism is activated when the joint judgment model determines that the adjustment effect is abnormal, but the air valve evaluation parameters show that the adjustment mechanism is operating normally; Obtain the energy consumption data of the lighting circuits in the building area where the room is located, and use it as an independent proxy variable for the instantaneous heat source in that area; Establish an empirical baseline for the hysteretic correlation between temperature changes and lighting energy consumption changes in the area under normal sensor conditions; The current monitored temperature change sequence is compared with the temperature change sequence predicted based on real-time lighting energy consumption data. If the two deviate from the empirical baseline, the reliability of the temperature sensor reading is determined to be low. Subsequently, in the subsequent joint judgment model, the system temporarily discarded the suspicious measured temperature value and instead used the estimated temperature calculated based on the damper opening, air volume and load prediction model to replace it, until the sensor was calibrated or replaced.

[0012] Furthermore, the current fluctuation spectrum of the damper actuator motor is monitored and compared with the health reference spectrum of the damper bearing and the actual operating stroke of the damper. When an abnormal harmonic that matches the characteristic frequency of mechanical wear is identified in the current spectrum, and the intensity of the harmonic increases as the valve travels further, it is determined that the valve bearing has early wear. Based on this determination, dynamic friction compensation parameters for the damper are automatically introduced into the control logic to counteract the operating resistance caused by increased wear and maintain the linear accuracy of airflow control.

[0013] Furthermore, during the system's nighttime rest period, the natural temperature recovery curve of the room was recorded, and this curve was fitted with the expected recovery model based on the building envelope design parameters and the indoor-outdoor temperature difference. If the measured recovery rate is consistently higher than the model prediction, it indicates that the actual thermal insulation performance of the room is lower than the design expectation. Based on this, a personalized preheating / precooling strategy is generated for the room. Before the start of the next work cycle, a longer advance start time and a gentler temperature rise rate than the standard strategy are automatically calculated to achieve a rebalancing of comfort and energy efficiency.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention generates dual-dimensional evaluation parameters for temperature and dampers by collecting full-dimensional operational data (including motor current and nighttime temperature data), and establishes a joint judgment model, a long-term attenuation evaluation model, a temperature sensor fault tolerance mechanism, a damper bearing wear diagnosis and compensation mechanism, and a nighttime heat preservation monitoring strategy optimization mechanism.

[0015] Meanwhile, this invention can promptly detect energy waste caused by imbalances in regional heating and cooling demands. By identifying and diagnosing abnormal trajectories in advance, it avoids the reactive approach of addressing issues only after significant temperature deviations, thus improving indoor comfort. Furthermore, it provides maintenance personnel with clear directions for improvement, priority maintenance recommendations, and suggestions for manual intervention, reducing ineffective maintenance costs and energy waste. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method framework structure of the present invention.

[0017] Figure 2 This is a flowchart of the variable air volume terminal control of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figures 1-2 This application provides an algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device, including: Collect operational data from the variable air volume (VAV) air conditioning terminal unit. In this embodiment, operational data refers to key parameters that reflect the adjustment status of the terminal unit and environmental feedback, specifically including environmental data such as real-time indoor temperature, indoor temperature fluctuation value, and indoor-outdoor temperature difference. Equipment operation data such as damper opening, air volume, and fan speed; Control data such as temperature setpoint, thermostat output signal, and air valve control signal.

[0020] Specifically, data acquisition is achieved through a network of temperature sensors, air volume sensors, angle sensors, and PLC controllers integrated into the terminal device. The PLC controller must support the Modbus-RTU communication protocol to adapt to the data transmission of various sensors.

[0021] Temperature data is sampled at different intervals than data on dampers and air volume. The sampling interval is determined based on the rate of parameter change. The sensor accuracy meets the engineering measurement requirements and ensures that dynamic changes during the adjustment process are captured.

[0022] The data acquisition scheme of this invention covers all dimensions of environmental, equipment, and control data, avoiding evaluation biases caused by single data dimensions and providing a comprehensive basis for subsequent parameter generation. Compared with traditional schemes that only collect temperature data, it can more accurately trace the source of adjustment anomalies and reduce maintenance and troubleshooting time.

[0023] Based on operational data, temperature evaluation parameters and damper evaluation parameters are generated. Specifically, temperature evaluation parameters refer to indicators that quantify the accuracy and stability of temperature regulation. In this embodiment, these parameters specifically include two indicators: temperature deviation coefficient and temperature fluctuation frequency.

[0024] The temperature deviation coefficient is expressed by the formula: The calculation shows that the real-time indoor temperature is the actual room temperature (in °C) collected by the sensor, and the temperature setpoint is the system's preset target temperature (in °C). The smaller the absolute value of the coefficient, the higher the adjustment accuracy. For example, when the temperature setpoint is 25 °C and the real-time temperature is 25.5 °C, the coefficient is (25.5-25) / 25×100%=2%. This parameter can intuitively reflect the core effect of temperature control, avoiding the problem of traditional methods that only use temperature difference values ​​to determine the temperature setpoint and ignore the differences.

[0025] It should be understood that temperature fluctuation frequency refers to the number of times the temperature deviation coefficient exceeds the preset range per unit time. The preset range is derived from the allowable temperature fluctuation range (±5%) in GB50736 Indoor Comfort Standard. The lower the frequency, the better the temperature stability, and the more likely it is to capture small fluctuations that are not noticeable to the user.

[0026] The evaluation parameters of a damper refer to the indicators that quantify the responsiveness and rationality of damper regulation, specifically including two indicators: damper response delay time and damper opening degree matching degree.

[0027] The response delay time of the air valve is the time difference between the output adjustment signal of the temperature controller and the actual start of the air valve action. It is calculated by recording the timestamps of signal transmission and sensor feedback action by the PLC controller. The delay time is considered normal if it is within the preset acceptable range. The preset acceptable range is determined with reference to industry standard (e.g., air valve response time <3s) and the rated response speed of the actuator. This parameter can directly locate mechanical faults such as jamming of the air valve actuator.

[0028] The matching degree of the damper opening is determined by the formula. Calculations show that the actual damper opening is the damper opening angle (in %) collected by the sensor in real time, and the theoretical optimal opening is the damper opening that meets the current air supply volume requirement (in %), which is determined by fitting the air supply volume requirement with the damper flow characteristic curve (obtained by fitting the flow-opening data calibrated at the factory). The matching degree is normal if it is within the preset reasonable range (±10%, based on the allowable error setting of the damper adjustment accuracy), which can avoid energy waste caused by excessive damper adjustment.

[0029] The two types of parameters approach the problem from two dimensions: the temperature regulation result and the damper regulation process. This lays the foundation for subsequent joint evaluation and is more comprehensive than evaluation based on a single temperature parameter. It can detect potential problems with the damper in advance and prevent regulation failure.

[0030] A joint judgment model is established based on temperature assessment parameters and damper assessment parameters. Specifically, the joint judgment model refers to a mathematical model that integrates temperature and damper parameters to achieve a comprehensive evaluation of the regulation effect. In this embodiment, a construction method combining weighted scoring and rule-based judgment is adopted.

[0031] First, weights were assigned to the temperature deviation coefficient, temperature fluctuation frequency, damper response delay time, and damper opening degree matching degree. The weights were determined using the analytic hierarchy process (AHP) combined with multiple sets of measured data from different scenarios. When constructing the judgment matrix, a 1-9 scale was followed, i.e., 1 for equal importance, 3 for slightly important, and 5 for significantly important. After passing the consistency check, the final weights were determined to ensure that the weight allocation accurately reflects the actual degree of adjustment impact.

[0032] Then, based on the scoring criteria corresponding to the values ​​of each parameter, individual scores are obtained, and weighted summation is used to obtain a comprehensive score. The comprehensive score is divided into three levels: excellent, qualified, and unqualified according to a preset range. The preset range is derived with reference to the temperature control accuracy requirements in the GB50736 Indoor Thermal Environment Quality Standard.

[0033] Simultaneously, rule-based judgment logic is embedded. For example, when the absolute value of the temperature deviation coefficient exceeds the severely abnormal range and the damper response delay time exceeds the acceptable range, it is directly judged as unacceptable, quickly locating severely abnormalities. This model achieves organic integration of parameters, avoiding the limitations of single-parameter scoring. For instance, hidden problems such as acceptable temperature but damper delay can be identified, improving the reliability of evaluation results and providing more accurate optimization directions for operation and maintenance.

[0034] This is achieved by introducing cross-validation and an adaptive fault-tolerance mechanism for latent faults in temperature sensors. Specifically, this mechanism addresses the evaluation bias caused by latent faults in temperature sensors (such as drift and decreased sensitivity). The triggering condition is that the joint judgment model determines the adjustment effect is abnormal (comprehensive score < 60 points or temperature deviation coefficient exceeds the severely abnormal range), but the damper evaluation parameters show that the adjustment mechanism operates normally (damper response delay time is within the acceptable range and opening degree matching meets requirements).

[0035] The first step in the specific process is to obtain the energy consumption data of the lighting circuits in the room area where the terminal device is located through the building energy management system. Since the lighting energy consumption is highly correlated with the activities of people and the operation of equipment in the area, it can be used as an independent proxy variable for the instantaneous heat source in the area, and the data collection frequency is consistent with the temperature data.

[0036] The second step in the specific process is to establish an empirical baseline for the lag correlation between temperature changes and lighting energy consumption changes in the area under normal sensor conditions. Specifically, this empirical baseline is the statistical law of the lag correlation between temperature changes and lighting energy consumption changes in the area under normal sensor conditions. It is obtained by collecting temperature change sequences and corresponding lighting energy consumption change sequences within 30 days after sensor calibration, and using a linear regression algorithm to fit the lag relationship between the two (the lag time is 5-10 minutes to adapt to the heat transfer time lag).

[0037] The third step in the specific process is to synchronously compare the currently monitored temperature change sequence with the temperature change sequence predicted by the empirical baseline model based on real-time lighting energy consumption data. Specifically, the deviation between the two is calculated by using the mean square error. If the deviation exceeds the preset threshold (0.8℃² based on the deviation statistics under normal operating conditions), it means that if the two deviate from the empirical baseline, the reliability of the temperature sensor reading is judged to be low.

[0038] The fourth step in the specific process is as follows: In the subsequent joint judgment model calculation, the system temporarily discards the suspicious measured temperature value and instead uses the estimated temperature as a substitute. The estimated temperature is calculated by combining the load prediction model with the valve opening and the measured air volume.

[0039] In this embodiment, the load forecasting model details are as follows: a random forest regression model is specifically selected because the model has a strong ability to fit nonlinear data and is good at resisting interference, and is suitable for multi-parameter coupling scenarios. The input variables include five dimensions: historical indoor-outdoor temperature difference, lighting energy consumption, damper opening, air supply volume, and time factor (hour, weekday / holiday). The training process uses historical data from the past 12 months, divided into training, validation, and test sets in a 7:2:1 ratio. Data preprocessing includes removing outliers using the 3σ criterion and normalizing to the [0,1] interval using Min-Max. The model evaluation index is set to root mean square error (RMSE) ≤ 0.5℃, which meets the engineering accuracy requirements.

[0040] Inverse calculation logic: First, obtain the actual air supply volume through the damper opening-air volume curve (factory calibration), and combine it with the heat load value Q output by the load prediction model; Furthermore, clarify the heat balance formula. Definitions and values ​​of each parameter: Q is the heat load value (output of the load prediction model, unit kJ); c is the specific heat capacity of air (taken as 1.005 kJ / (kg·K), a standard air physical property parameter); m is the mass of air in the room that participates in heat exchange (calculated from the room volume V and air density ρ). (ρ is taken as 1.2 kg / m³, the air density under standard operating conditions). ΔT is the difference between the theoretical room temperature and the initial room temperature. ; Finally passed By working backward, we can calculate the theoretical room temperature under different damper adjustment states. t is the time interval between two adjacent samplings, in seconds, provided by a 1-second interrupt of the PLC clock.

[0041] The replacement state will continue until maintenance personnel complete sensor calibration or replacement. After calibration, the system will automatically switch back to the measured temperature value and update the empirical baseline. This mechanism accurately identifies latent sensor faults through independent variable cross-validation, avoiding ineffective maintenance caused by misjudging the control mechanism's failure, while ensuring the continuity of assessment through calculated temperature replacement.

[0042] Establish a long-term degradation assessment model for the regulation performance of terminal devices. It should be understood that this model is used to monitor the performance degradation of terminal devices after long-term operation, providing a basis for preventative maintenance.

[0043] During stable system operation, the average time required for the terminal unit to adjust the room temperature to the set value and the corresponding average damper opening are periodically recorded. Specifically, key adjustment performance data of the terminal unit are periodically recorded, with the period set based on equipment maintenance manual recommendations and the complexity of the operating environment, typically once a week.

[0044] The recorded content includes the average time required for the terminal device to adjust the room temperature from the deviation state to the set value (the average time of 3 complete adjustment processes within the cycle) and the average air valve opening during the corresponding adjustment process (the average time integral of the air valve opening during the adjustment period).

[0045] The average adjustment time and the average damper opening are combined to form a performance vector, which is then stored in the historical database in chronological order. Specifically, the average adjustment time and average damper opening of each record are used to form a two-dimensional performance vector, which is stored in the historical database in chronological order. The database synchronously records the operating environment parameters (such as indoor and outdoor temperature difference and room load peak) within the cycle to eliminate environmental interference.

[0046] Trend analysis stage: The trend fitting algorithm is used to analyze the evolution path of the performance vector. The fitting algorithm is a weighted algorithm that combines linear regression and exponential smoothing. The linear regression weight accounts for 0.6 (dominating the long-term trend capture), the exponential smoothing weight accounts for 0.4 (correcting short-term fluctuations), and the exponential smoothing coefficient is 0.3 (determined based on iterative optimization of historical data, taking into account both response speed and stability).

[0047] Early warning judgment process: Set trend judgment rules. When the fitted curve shows that under the same or similar environmental conditions (indoor and outdoor temperature difference fluctuation ≤2℃), the opening degree of the air valve required to achieve the same adjustment effect (temperature deviation coefficient ≤±5%) continues to increase for more than 3 cycles (cumulative increase ≥15%), and the adjustment time continues to extend synchronously (cumulative increase ≥20%), a performance degradation early warning is generated.

[0048] In the early warning application phase, the warning information clearly indicates the terminal device number, the performance degradation range (Euclidean distance between the current and initial performance vectors, unit: (min, % opening degree)), and possible causes, namely, mechanical performance degradation of the damper actuator (such as gear wear, reduced motor power) or increased duct resistance (such as dust accumulation, filter blockage). This warning is automatically pushed to the system maintenance plan module to prompt that in the next planned maintenance cycle, priority should be given to performing mechanical inspections (such as disassembly and inspection of the damper actuator, motor power testing) and duct cleaning (such as duct endoscopy inspection, filter replacement) on the terminal device.

[0049] The specific innovation lies in the shift from post-failure maintenance to preventive maintenance through a long-term degradation assessment model, reducing the risk of regulation failure due to performance degradation and extending the service life of equipment.

[0050] Monitor the current fluctuation spectrum of the damper actuator motor and compare it with the health reference spectrum of the damper bearing and the actual operating stroke of the damper; Specifically, the damper implements a motor current monitoring and bearing wear diagnosis and compensation mechanism. This mechanism is used to identify damper bearing wear early and dynamically compensate for it, preventing a decrease in airflow control accuracy.

[0051] Data acquisition stage: A current sensor is connected to the power supply circuit of the damper actuator motor to collect the current signal during motor operation in real time. The time-domain current signal is converted into a frequency-domain current fluctuation spectrum through Fourier transform. The frequency range of the spectrum analysis covers the typical wear characteristic frequency range of the damper bearing.

[0052] Benchmark establishment phase: Simultaneously establish a health benchmark spectrum library for air valve bearings. The benchmark spectrum is obtained by collecting the current spectrum of newly manufactured or recently replaced air valves during their full stroke operation. The benchmark spectrum is stored separately according to different stroke segments of the air valve (such as 0-20%, 20%-40% up to 80%-100% opening).

[0053] Wear assessment process: When the damper operates, the current fluctuation spectrum and the actual operating stroke data of the damper are recorded simultaneously. The real-time spectrum is compared with the health reference spectrum of the corresponding stroke segment to calculate the spectrum similarity and abnormal harmonic intensity.

[0054] When an abnormal harmonic that matches the characteristic frequency of mechanical wear is identified in the current spectrum, and the intensity of the harmonic increases as the valve travels further, it is determined that the valve bearing has early wear. A wear determination rule is set. When an abnormal harmonic appears in the real-time current spectrum that matches the characteristic frequency of bearing mechanical wear (such as the wear frequency of the inner ring, the wear frequency of the outer ring, and the wear frequency of the rolling element), and the stroke-harmonic intensity correlation analysis shows that the intensity of the abnormal harmonic increases with the advancement of the valve stroke, it is determined that the valve bearing has early wear.

[0055] It should be noted that the characteristic frequency of bearing mechanical wear is calculated using mechanical vibration theory formulas based on the geometric parameters of the bearing model, or obtained by experimentally calibrating a spectrum feature library of typical faulty bearings.

[0056] Based on this determination, dynamic friction compensation parameters for the air valve are automatically introduced into the control logic to offset the operating resistance caused by increased wear and maintain the linear accuracy of airflow control. In this embodiment, the dynamic compensation step is as follows: dynamic compensation is performed based on the wear determination result. Dynamic friction compensation parameters for the air valve are automatically introduced into the air valve control logic. The compensation parameter value is calculated based on the correlation between abnormal harmonic intensity and stroke. That is, the output amplitude of the motor drive current is dynamically adjusted according to the wear degree of different stroke segments to offset the increase in operating resistance caused by increased bearing wear.

[0057] Effect monitoring stage: After compensation, the correspondence between the damper opening and the actual air volume is continuously monitored. If the air volume control deviation returns to the preset accuracy range, the compensation parameters are maintained; if the deviation still exceeds the range, a bearing replacement warning is generated and pushed to the operation and maintenance system.

[0058] This mechanism enables early warning of bearing wear through current spectrum analysis, and combines dynamic compensation to maintain the linear accuracy of airflow control, avoid adjustment failure caused by mechanical wear, and extend the service life of the bearing.

[0059] During the system's nighttime rest period, the natural temperature recovery curve of the room was recorded, and this curve was fitted with the expected recovery model based on the building envelope design parameters and the indoor-outdoor temperature difference. Specifically, this involves a mechanism for monitoring thermal insulation performance during quiet nighttime periods and optimizing personalized preheating / precooling strategies. This mechanism dynamically adjusts start-stop strategies based on the building's actual thermal insulation performance to achieve a balance between comfort and energy efficiency.

[0060] Data acquisition phase: Set the system to be idle at night (e.g., from 22:00 to 6:00 the next day when the building is unoccupied). During this phase, the air conditioning terminal devices stop actively adjusting and only retain the low-frequency acquisition function of the temperature sensor (e.g., acquiring the indoor temperature every 5 minutes). Record the curve of the room temperature naturally rising (in winter) or falling (in summer) during the entire idle period. At the same time, the indoor and outdoor temperature difference data are collected synchronously through the outdoor temperature sensor.

[0061] Model Construction Phase: Based on the building envelope design parameters, a model for the expected rise / fall in thermal conductivity is constructed. These parameters include the thermal conductivity of the wall insulation material (k1, taken as 0.035 W / (m·K), conforming to GB / T10801.1 standard), the window heat transfer coefficient (k2, taken as 2.8 W / (m²·K), conforming to GB / T8479 standard), and the roof insulation performance (k3, taken as 0.04 W / (m·K)). The model uses a steady-state heat conduction formula. Calculate heat exchange rate. Heat exchange capacity of the building envelope (unit: W). The thermal conductivity / heat transfer coefficient of each building envelope (unit: W / (m·K) or W / (m²·K)). The area of ​​each building envelope (unit: m², obtained from architectural design drawings) is given. The difference between indoor and outdoor temperatures, in K. Combining the room volume with the specific heat capacity of air (c=1.005kJ / (kg·K)) and air density (ρ=1.2kg / m³), through (t is the time interval, V is the room volume) Calculate the theoretical temperature change rate to form the expected temperature curve. t is set to 300s, which is consistent with the temperature sampling period of 5min.

[0062] The measured temperature curve is fitted and compared with the expected temperature curve, and the deviation between the two is calculated using the mean absolute error (MAE).

[0063] If the measured recovery rate is consistently higher than the model prediction, it indicates that the actual thermal insulation performance of the room is lower than the design expectation. If the measured rise / fall rate is higher than the model prediction for three consecutive night cycles (measured rise is ≥0.3℃ / h faster than predicted in winter, and measured fall is ≥0.4℃ / h faster than predicted in summer), then the actual thermal insulation performance of the room is determined to be lower than the design expectation. The deviation range is analyzed simultaneously to quantify the degree of thermal insulation performance degradation (e.g., MAE=0.8℃ corresponds to a 25% degradation).

[0064] Based on this, a personalized preheating / precooling strategy is generated for the room. Before the start of the next work cycle, a longer advance start time and a gentler temperature rise rate than the standard strategy are automatically calculated to achieve a rebalancing of comfort and energy efficiency. In this embodiment, a personalized preheating / precooling strategy is generated based on the judgment result. The system automatically retrieves the historical load data of the area and the temperature setting requirements for the next working cycle. Combined with the degree of thermal insulation performance degradation, the system uses a thermal balance calculation model to back-calculate the advance start time and the rate of temperature rise / fall.

[0065] The core formula of the heat balance calculation model is: , The amount of heat exchange required to reach the set temperature (in kJ). The specific heat capacity of air (1.005 kJ / (kg·K)) The density of air is 1.2 kg / m³. Room volume (unit: m³, obtained from architectural design drawings). The difference between the target temperature and the initial temperature (in K). This is the theoretical adjustment time (in hours). The heat exchange required to reach the set temperature is calculated using this formula, and then the advance start time is calculated by combining the heating / cooling power of the air conditioning terminal.

[0066] Compared to the standard strategy, the advance start time is appropriately extended (e.g., 30 minutes in advance for the standard, adjusted to 45 minutes when the temperature decays by 25%), while the temperature rise / fall rate is reduced (e.g., 1℃ / 10 minutes for the standard, adjusted to 1℃ / 15 minutes) to avoid rapid heat loss or infiltration due to insufficient insulation. After the strategy is generated, it is stored in the control logic and automatically executed before the start of the next work cycle. During execution, the indoor temperature is monitored in real time; if the actual temperature reaches the set value ahead of schedule, the adjustment intensity is automatically reduced to further optimize energy consumption.

[0067] This invention solves the problem of low comfort or high energy consumption caused by the decay of building insulation due to the traditional fixed strategy by using a dynamic calibration strategy based on actual thermal insulation performance, and achieves personalized and precise adjustment.

[0068] Extract the coordinated change characteristics of temperature evaluation parameters (including normal measured temperature or estimated temperature after adaptive fault tolerance) and damper evaluation parameters (including damper adjustment parameters after current monitoring compensation) over time series to generate adjustment trajectory characteristic parameters.

[0069] In this embodiment, the cooperative change feature refers to the correlation between the temperature assessment parameter and the damper assessment parameter over time. This step achieves feature extraction through dynamic behavior phase diagram construction and region identification, specifically as follows: First, a dynamic behavior phase diagram is established with temperature evaluation parameters as the vertical axis and damper evaluation parameters as the horizontal axis. The temperature evaluation parameters on the vertical axis are selected as temperature deviation coefficients (to quantify the magnitude of temperature difference), and the damper evaluation parameters on the horizontal axis are selected as damper opening (to directly reflect the damper adjustment status). The scale range of the phase diagram's coordinate axes is determined by combining the normal operating range and abnormal threshold of the two types of parameters.

[0070] In the dynamic behavior phase diagram, the time series data of temperature deviation coefficient and damper opening are mapped in real time to form the system operation trajectory and identify the quadrant region where the trajectory is located. Specifically, the phase diagram is divided into four quadrants according to "temperature deviation coefficient level" and "damper opening level", and the quadrant boundaries are calibrated with reference to GB50736 standard and damper adjustment rated range.

[0071] It should be noted that the trajectory characteristic parameters specifically include two indicators: the trajectory quadrant distribution ratio and the trajectory movement rate. The trajectory quadrant distribution ratio refers to the proportion of time the trajectory spends in each quadrant, while the trajectory movement rate refers to the average rate at which the trajectory moves from one quadrant to another in the phase diagram. These two indicators together quantify the stability of the coordinated changes.

[0072] Among them, the quadrant corresponding to the operating state where the trajectory is continuously distributed in the same operating state with high temperature difference and high valve opening (corresponding to the first quadrant of the phase diagram) or the operating state with high temperature difference and low valve opening (corresponding to the second quadrant of the phase diagram) is defined as the abnormal trajectory mode.

[0073] Specifically, this step uses visualized phase diagrams to uncover the dynamic relationships between parameters, making up for the shortcomings of static parameter evaluation in reflecting the adjustment process. It provides an intuitive and accurate core basis for abnormal trajectory identification, enabling the detection of coordinated anomalies before the temperature deviates significantly, thus providing early warnings and reducing the impact on comfort.

[0074] When the adjusted trajectory feature parameters identify a preset abnormal trajectory pattern, the enhanced diagnostic state of the joint judgment model is activated. In this embodiment, the abnormal trajectory pattern is defined based on the quadrant distribution of the dynamic behavior phase diagram, specifically, the trajectory is continuously distributed in the operating state where a high temperature difference and a high valve opening occur simultaneously (first quadrant of the phase diagram), or in the operating state where a high temperature difference and a low valve opening occur (second quadrant of the phase diagram).

[0075] Specifically, the criteria for determining a sustained distribution are that the trajectory spends more than a preset percentage of its time in the target quadrant, and the duration reaches a set period. The preset percentage and set period are calibrated in conjunction with the air conditioning system's adjustment response time lag and historical fault data. This abnormal trajectory pattern is trained and verified by collecting multiple sets of normal operation data and multiple sets of typical fault data (valve jamming, sensor drift, control logic disorder, etc.) to ensure recognition accuracy.

[0076] It should be noted that, based on the statistics of 60 sets of fault samples, the false negative rate can be kept below 3% within the preset ratio of 60%–70% and the set period of 20–30 minutes. In this embodiment, 65% and 25 minutes are used.

[0077] Enhanced diagnostic status refers to the working mode in which the joint judgment model initiates additional diagnostic logic. After activation, the model first increases the sampling frequency and then calls up recent historical normal trajectory data (phase map trajectory and quadrant distribution data of the past 30 days) to perform comparative analysis through a similarity comparison algorithm.

[0078] After the enhanced diagnostic state is triggered, the next step includes attribution analysis of abnormal trajectory patterns; Obtain operational data from other terminal units served by the same air handling unit; Comparative analysis: If other terminal devices simultaneously exhibit similar abnormal trajectory patterns, the abnormality is attributed to pressure imbalance in the central air conditioning unit or air system. Specifically, these data are consistent with the data collection dimensions of this terminal device, including environmental, equipment operation, and control data. Based on the dynamic behavior phase diagram, trajectory analysis is performed on the operating data of other terminal devices to conduct comparative diagnostics. If other terminal devices within the service area of ​​the same air handling unit simultaneously exhibit similar abnormal trajectory patterns, i.e., they are all continuously distributed in the first or second quadrant, the abnormality is attributed to the central air conditioning unit (such as insufficient cooling capacity of the chiller unit or abnormal air supply temperature of the unit) or the air system pressure imbalance (such as blower failure or duct blockage). If only the current terminal device exhibits an abnormal trajectory pattern, while the trajectories of other terminal devices are in the normal quadrants (third and fourth quadrants), then the cause should be attributed by combining the temperature sensor fault tolerance mechanism and the valve current monitoring results.

[0079] Detection of control logic conflicts in the coordinated state of the execution end group; When the joint judgment model identifies that a certain terminal device has entered the enhanced diagnostic state, it simultaneously detects the operating modes of other terminal devices located in the same building area at the same time. In this embodiment, the operating modes of other terminal devices located in the same building area at the same time are detected simultaneously. The same building area can be defined according to the building functional zoning or the air conditioning water system zoning. The operating mode focuses on the cooling / heating mode and the corresponding air valve opening status.

[0080] If it is found that more than one terminal device is operating in conflicting modes, such as one terminal having its air valve fully open because it needs to cool, while the adjacent terminal has its air valve fully open because it needs to heat, it is determined that there is an energy waste condition caused by the offsetting of regional cooling and heating demands. At this point, instead of directly adjusting the terminal air valve, a high-level alarm is sent to the central control system. The alarm information clearly indicates the location, operating mode, and air valve opening of the conflicting terminal, pointing out that there may be problems such as large-area opening of external windows, extremely uneven distribution of internal heat sources, or conflicting thermostat settings in the area, and recommends manual verification and intervention.

[0081] For scenarios where only the terminal device itself malfunctions (excluding sensor and coordination conflict issues), extreme tests and model calibrations are performed. The airflow setpoint limit of the terminal device is temporarily lifted, and its air valve is instructed to perform a full-open to full-close extreme action test within a short period of time (the specific duration is determined based on the mechanical performance parameters of the air valve). A test warning will be sent to the central monitoring station before the test to avoid misjudgment by operation and maintenance personnel.

[0082] Monitor and record the range of changes in measured air volume during extreme testing and its final impact on room temperature; specifically, monitor and record key data during extreme testing in real time, including the damper opening change curve, the range of changes in measured air volume, the dynamic curve of indoor temperature change with air volume, and the final stable temperature, with the data sampling interval shortened to the millisecond level to capture instantaneous changes.

[0083] Based on the test data, the intrinsic relationship model between the damper adjustment capability and temperature control efficiency of the terminal device in the joint judgment model is calibrated, and the calibrated model is used for optimization evaluation. Specifically, the joint judgment model is calibrated based on test data. That is, the valve opening-air volume data sequence recorded in the extreme test is used to refit the valve opening-air volume curve by the least squares method and update its fitting parameters. Meanwhile, by using the damper's action timestamp and the room temperature dynamic response curve, cross-correlation analysis was performed to correct the time lag coefficient of the damper's adjustment response to temperature changes, ensuring that the model parameters match the actual performance of the terminal device. Finally, the calibrated model was used to re-evaluate the current adjustment effect and correct the initial evaluation bias.

[0084] This invention uses a combination of attribution analysis, collaborative conflict detection, limit testing, and model calibration to accurately locate the root cause of anomalies and achieve dynamic model optimization, thereby improving the accuracy of subsequent evaluations.

[0085] In enhanced diagnostic mode, an optimized assessment conclusion on the temperature regulation effect is output. This step is specifically implemented through an urgency quantification and dynamic push strategy, including: Based on the duration of abnormal trajectory patterns, deviation rate, presence of coordination conflicts, presence of sensor failures, damper wear status, and performance in extreme tests, the urgency index of regulation failure is calculated.

[0086] It should be understood that the urgency index is calculated using a weighted summation formula. The weights are calibrated using historical fault handling timeliness data and pass consistency verification. The specific weight allocation is as follows: the duration of abnormal trajectory accounts for 0.2, the rate of trajectory deviation from the normal quadrant accounts for 0.2, an additional weight is added when there is a coordination conflict or sensor failure, an additional weight is added when the air valve is severely worn, and the deviation rate between the measured air volume and the theoretical value in the extreme test accounts for 0.3. The five indicators are quantified into scores of 0-10 and then weighted and summed to finally obtain an urgency index of 0-10.

[0087] The maximum score for each item is 10 points, corresponding to: duration of 60 minutes, deviation rate of 1℃ / min, and air volume deviation of 30%. The rest are calculated by linear extrapolation.

[0088] Based on the urgency index, the output format and push strategy of the final assessment conclusion are dynamically adjusted. Specifically, the first threshold is set at 5 points, and the second threshold is set at 8 points (the second threshold is higher than the first threshold). The thresholds are set in conjunction with the impact range of equipment failure and the GB50736 safety standard.

[0089] When the urgency index exceeds the first threshold, the conclusion is pushed to the central monitoring station in the form of a pop-up alert, accompanied by an audible and visual alarm to prompt maintenance personnel to pay attention immediately. If there is a coordination conflict, the conflict warning sign should be highlighted. If there is sensor failure or damper wear, calibration / replacement reminders should be highlighted respectively. When the threshold is exceeded, the conclusion is automatically attached to the system maintenance work order system, clearly marking the abnormal location, attribution results, details of coordination conflict, key data of limit test and rectification suggestions, and assigning processing priority according to the index (e.g., index 9-10 is the highest priority, 7-8 is a high priority). Conditions with coordination conflict, sensor failure or severe bearing wear are marked as high priority.

[0090] Furthermore, the optimized evaluation conclusions, in addition to the core content, also include the original comprehensive score and pass / fail level, the correction instructions for the calibrated model parameters, the information on the damper compensation parameters and the thermal insulation performance evaluation results, as well as three key pieces of information: anomaly type determination (determined by combining sensor fault tolerance mechanism results, damper current monitoring data, actuator action test and limit test results), anomaly attribution results (derived by comparing and analyzing other terminal devices in the same air handling unit), and quantification of the degree of anomaly impact (calculated based on temperature deviation duration, energy consumption change, estimated energy waste due to coordination conflicts, and temperature fluctuation amplitude in the limit test).

[0091] Furthermore, the output visualization reports include parameter change curves, diagnostic logic trees, trajectory comparison charts of other terminal devices in the same unit, comparison tables of collaborative conflict terminal distribution and operation modes, extreme test data curves, comparison tables of model parameter calibration before and after, and thermal insulation performance fitting curves, allowing maintenance personnel to intuitively obtain comprehensive information.

[0092] This invention quantifies the urgency level by integrating multiple factors, further improving the practicality and accuracy of the assessment conclusions. It not only ensures timely response to various anomalies, but also provides data support for subsequent maintenance and model optimization, significantly improving the pertinence of operation and maintenance and the long-term reliability of the assessment system.

[0093] This invention generates dual-dimensional evaluation parameters for temperature and dampers by collecting full-dimensional operational data (including motor current and nighttime temperature data), and establishes a joint judgment model, a long-term attenuation evaluation model, a temperature sensor fault tolerance mechanism, a damper bearing wear diagnosis and compensation mechanism, and a nighttime heat preservation monitoring strategy optimization mechanism.

[0094] By extracting time-series collaborative features to identify anomalies and activate enhanced diagnostics, and incorporating attribution analysis, collaborative conflict detection, and limit test calibration, the system ultimately outputs optimized conclusions. This system solves the problems of the existing single-parameter evaluation's one-sidedness, the inability to predict long-term performance degradation, the hidden faults of sensors, the difficulty in identifying early wear of damper bearings, and the insufficient adaptation to building insulation degradation, achieving accurate evaluation of regulation effects, preventive maintenance, dynamic fault-tolerant compensation, and personalized strategy optimization.

[0095] Meanwhile, this invention can promptly detect energy waste caused by imbalances in regional heating and cooling demands. By identifying and diagnosing abnormal trajectories in advance, it avoids the reactive approach of addressing issues only after significant temperature deviations, thus improving indoor comfort. Furthermore, it provides maintenance personnel with clear directions for improvement, priority maintenance recommendations, and suggestions for manual intervention, reducing ineffective maintenance costs and energy waste.

[0096] In addition, by continuously accumulating data and optimizing the model library, the assessment and diagnosis become increasingly accurate, adapting to variable air volume air conditioning terminal devices in different building scenarios.

[0097] 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. An algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device, characterized in that, include: Collect operational data from variable air volume (VAV) air conditioning terminal units; Based on the aforementioned operational data, temperature evaluation parameters and damper evaluation parameters are generated. Based on the temperature evaluation parameters and the air valve evaluation parameters, a joint judgment model is established; Extract the coordinated change characteristics of the temperature evaluation parameters and the damper evaluation parameters over time series to generate adjustment trajectory feature parameters; When the adjusted trajectory feature parameters identify a preset abnormal trajectory pattern, the enhanced diagnostic state of the joint judgment model is activated; In the enhanced diagnostic state, an optimized assessment conclusion on the temperature regulation effect is output.

2. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, The step of extracting cooperative variation features to generate regulation trajectory feature parameters includes: Establish a dynamic behavior phase diagram with temperature evaluation parameters as the vertical axis and damper evaluation parameters as the horizontal axis; In the dynamic behavior phase diagram, the region where the system's operating trajectory is located is identified; Among them, the quadrants corresponding to the operating states where the trajectory is continuously distributed in the same direction as the operating states with high temperature difference and high valve opening, or high temperature difference and low valve opening, are defined as abnormal trajectory modes.

3. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 2, characterized in that, After triggering the enhanced diagnostic state, the next step includes attribution analysis of the abnormal trajectory patterns: Obtain operational data from other terminal units served by the same air handling unit; Comparative analysis: If other terminal devices simultaneously exhibit similar abnormal trajectory patterns, the abnormality is attributed to pressure imbalance in the central air conditioning unit or air system. If only this terminal device malfunctions, it is attributed to a fault in the terminal device itself or an abnormal load in the room.

4. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, The steps for outputting optimized evaluation conclusions specifically include: Based on the duration and deviation rate of the abnormal trajectory pattern, the urgency index of regulation failure is calculated. Based on the urgency index, the output format and push strategy of the final evaluation conclusion are dynamically adjusted. When the urgency index exceeds the first threshold, the conclusion is pushed to the central monitoring station in the form of a pop-up alert. When the threshold is exceeded by a higher second threshold, the conclusion is appended to the system maintenance work order system, and the processing priority is specified.

5. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, In enhanced diagnostic mode, the model performs the following operations: Temporarily release the airflow setting limit of the terminal device and instruct its air valve to perform a full-open to full-close limit test within a short period of time; Monitor and record the range of changes in measured air volume during extreme testing and its ultimate impact on room temperature; Based on the test data, the intrinsic relationship model between the damper adjustment capability and temperature control efficiency of the terminal device in the joint judgment model is calibrated, and the calibrated model is used for optimization evaluation.

6. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, Establish a long-term degradation assessment model for the regulation performance of terminal devices: During stable system operation, the average time required for the terminal device to adjust the room temperature to the set value and the corresponding average air valve opening are recorded periodically. The average adjustment time and the average damper opening are used to form a performance vector, which is then stored in the historical database in chronological order. The evolution path of the performance vector is analyzed by applying a trend fitting algorithm; when the fitting curve shows that the opening of the damper required to achieve the same adjustment effect continues to increase and the adjustment time is extended synchronously, an early warning is generated that the mechanical performance of the damper actuator of the terminal device is deteriorating or the air duct resistance is increasing. This warning is intended to prompt that mechanical inspections and duct cleaning of the terminal unit be prioritized in the next planned maintenance cycle.

7. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, Implement control logic conflict detection for end-group coordinated state: When the joint judgment model identifies that a certain terminal device has entered the enhanced diagnostic state, it simultaneously detects the operating modes of other terminal devices located in the same building area at the same time. If it is found that more than one terminal device is operating in conflicting modes, including one terminal having its air valve fully open because it needs to cool, while the adjacent terminal has its air valve fully open because it needs to heat, then it is determined that there is an energy waste condition caused by the offsetting of regional cooling and heating demands. At this point, instead of directly adjusting the terminal air valve, a high-level alarm is sent to the central control system, indicating that there may be a large area of ​​open windows, extremely uneven distribution of internal heat sources, or conflicting thermostat settings in the area, and manual verification and intervention are recommended.

8. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 1, characterized in that, Introducing cross-validation and adaptive fault-tolerance mechanisms for latent faults in temperature sensors: This mechanism is activated when the joint judgment model determines that the adjustment effect is abnormal, but the air valve evaluation parameters show that the adjustment mechanism is operating normally; Obtain the energy consumption data of the lighting circuits in the building area where the room is located, and use it as an independent proxy variable for the instantaneous heat source in that area; Establish an empirical baseline for the hysteretic correlation between temperature changes and lighting energy consumption changes in the area under normal sensor conditions; The current monitored temperature change sequence is compared with the temperature change sequence predicted based on real-time lighting energy consumption data. If the two deviate from the empirical baseline, the reliability of the temperature sensor reading is determined to be low. Subsequently, in the subsequent joint judgment model, the system temporarily discarded the suspicious measured temperature value and instead used the estimated temperature calculated based on the damper opening, air volume and load prediction model to replace it, until the sensor was calibrated or replaced.

9. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 2, characterized in that, Monitor the current fluctuation spectrum of the damper actuator motor and compare it with the health reference spectrum of the damper bearing and the actual operating stroke of the damper; When an abnormal harmonic that matches the characteristic frequency of mechanical wear is identified in the current spectrum, and the intensity of the harmonic increases as the valve travels further, it is determined that the valve bearing has early wear. Based on this determination, dynamic friction compensation parameters for the damper are automatically introduced into the control logic to counteract the operating resistance caused by increased wear and maintain the linear accuracy of airflow control.

10. The algorithm for analyzing and evaluating the temperature regulation effect of a variable air volume (VAV) air conditioning terminal device according to claim 2, characterized in that, Monitor the current fluctuation spectrum of the damper actuator motor and compare it with the health reference spectrum of the damper bearing and the actual operating stroke of the damper; When an abnormal harmonic that matches the characteristic frequency of mechanical wear is identified in the current spectrum, and the intensity of the harmonic increases as the valve travels further, it is determined that the valve bearing has early wear. Based on this determination, dynamic friction compensation parameters for the damper are automatically introduced into the control logic to counteract the operating resistance caused by increased wear and maintain the linear accuracy of airflow control.

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