Control method and system for variable-channel operation air handling unit with adjustable working conditions
By receiving startup commands for self-inspection and real-time data processing, and establishing an adjustment strategy model, the temperature and humidity fluctuations and sensor failure problems of traditional air handling units under complex working conditions are solved, achieving rapid adaptation and efficient operation, and reducing maintenance costs.
Patent Information
- Application Number
- CN202510836550.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-21
- Publication Date
- 2025-09-12
AI Technical Summary
Traditional air handling units are unable to adapt to dynamic switching when faced with complex and changing operating conditions, resulting in large fluctuations in temperature and humidity, substandard cleanliness, and a lack of real-time self-detection and redundant design when sensors fail, which can easily lead to chain failures and high maintenance costs.
A control method with adjustable working condition and variable channel operation is adopted. Self-inspection is performed by receiving the start command, data is collected and pre-processed in real time, an adjustment strategy model is established, and fusion analysis of multi-source data sets is realized. Adjustment decisions are optimized based on historical data, and three-level sensor calibration is used to reduce the false alarm rate of faults.
It realizes the rapid switching of air handling units under different working conditions, reduces energy consumption, lowers maintenance costs, improves sensor data accuracy and system stability, and avoids the blindness of single adjustment.
Smart Images
Figure CN120627367A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of air handling units, and in particular to a control method and system for an air handling unit with adjustable working condition and variable channel operation. Background Art
[0002] In the fields of industrial production and building environmental control, air handling units (AHUs) serve as core equipment for maintaining indoor environmental parameters such as temperature, humidity, and cleanliness. The scientific nature and reliability of their control methods directly impact production efficiency, environmental comfort, and energy consumption. However, constrained by the limitations of traditional control technologies, existing AHUs are gradually facing various technical bottlenecks when faced with complex and changing operating conditions. Traditional AHU control systems have the following problems: Traditional units rely on fixed parameters or simple threshold controls, making them unable to adapt to the dynamic switching requirements of multiple operating conditions. This often leads to large temperature and humidity fluctuations and substandard cleanliness. Secondly, when sensors fail or equipment anomalies occur, the lack of real-time self-checking and redundant design can easily lead to cascading failures. Fault location relies on manual troubleshooting, resulting in high maintenance costs.
[0003] Therefore, it is necessary to design a control method and system for an air handling unit with adjustable working conditions and variable channels. Summary of the Invention
[0004] In response to the technical defects in the background technology, the present invention proposes a control method and system for an air handling unit with adjustable working conditions and variable channels, which solves the above technical problems and meets practical needs. The specific technical solution is as follows: A control method for an air handling unit with adjustable working condition and variable channel operation comprises the following steps: Step S1: receiving an air handling unit startup command, parsing the startup command to obtain target operating parameters, triggering a sensor self-test program, and outputting a self-test result; Step S2: starting the air handling unit based on the start-up command and the self-test result, collecting operating condition data and environmental data in real time through the sensor group, pre-processing the operating condition data and environmental data, and fusing them to form a multi-source data set; Step S3: determining the current operating condition based on the target operating condition parameters and the multi-source data set, and comparing the target operating condition parameters with the multi-source data set based on the current operating condition to obtain a deviation data set; Step S4: Establish an adjustment strategy model based on historical data, input the deviation data set to obtain the best adjustment decision, and send a control instruction to the corresponding actuator based on the best adjustment decision; Step S5: The actuator receives the control instruction, performs an action based on the parsed control instruction, and generates an action log for uploading.
[0005] Furthermore, the step S5 further includes: When the actuator action is completed, the sensor group enters the dynamic monitoring phase, acquiring real-time working condition data and environmental data at a high sampling frequency to form a multi-source data set. The operation of step S3 is repeated to obtain a deviation data set, and the deviation data set is judged. When all parameters in the deviation data set are within the preset target threshold range, the action is determined to be valid, the current operating state is maintained, and an action log is generated and uploaded; When any parameter in the deviation data set exceeds the preset target threshold range, steps S3-S5 are repeated until the number of cycles reaches the set value or the parameters in the deviation data set are all within the preset target threshold range, the cycle ends and the action log is generated for upload.
[0006] Furthermore, the target operating condition parameters in the startup command include target temperature, target humidity, target air cleanliness, and a set of standard parameter values corresponding to different operating conditions, wherein the different operating conditions include summer full cooling mode, transition season cooling mode, winter heating mode, and mixed mode. Determining the current operating condition in step S3 specifically includes: The operating mode is determined based on the target temperature and target humidity in the target operating condition parameters, the current season is confirmed through environmental data in the multi-source data set, and the current operating condition is determined in combination with the current season and the operating mode.
[0007] Furthermore, in step S1, a start command is received through a human-computer interaction interface, the start command is parsed, target operating parameters are obtained, and a sensor self-test program is triggered. The sensor self-test program is used to initialize and verify the sensor group, specifically including: The controller obtains the initial data output by the sensor group, detects the communication link between each sensor in the sensor group and the controller, and collects statistics on abnormal data; The initial data output by the sensor group is compared with the built-in reference source. When the deviation between the sensor and the built-in reference source is greater than the preset value, the calibration process is triggered to calibrate the sensor and generate a calibration log. When the deviation between the sensor and the built-in reference source is less than the preset value, it is marked as normal. Verify the primary and backup sensors. If the data from the primary and backup sensors are consistent, mark them as normal. If the data from the primary and backup sensors are inconsistent, mark them as data consistency abnormal. Combined with the abnormal data detected by the communication link, the calibration log and the data consistency abnormality, a self-test result containing self-test data is generated. When the data in the self-test result meets the preset conditions, it is determined to be a serious fault, the unit is prohibited from starting and an alarm is issued.
[0008] Furthermore, the step S2 specifically includes: The air handling unit enters the standby phase upon receiving the start command and determines the self-test results. If the data in the self-test results meet the preset conditions, it is determined to be a serious fault, the unit is prohibited from starting, and an alarm is issued; if the data in the self-test results do not meet the preset conditions, it is determined to have passed the self-test and the unit is started. The pretreatment specifically includes: Use sliding average filtering algorithm to remove sudden noise in working condition data and environmental data; Eliminate obviously unreasonable data in working condition data and environmental data through data correlation verification; The fusion of the working condition data and the environmental data to form a multi-source data set specifically includes: The data of different sensors and different types in the sensor group are aligned by timestamps, and a comprehensive data vector is generated through a weighted fusion algorithm to form a multi-source data set.
[0009] Furthermore, the specific steps of obtaining the deviation dataset are as follows: Based on the current working condition, the standard parameter value set corresponding to the working condition is retrieved from the target working condition parameters, and the parameters in the standard parameter value set are compared one by one with the corresponding parameters in the multi-source data set to obtain the absolute deviation value, and a deviation data set consisting of multiple absolute deviation values is output.
[0010] Furthermore, the specific steps for establishing the adjustment strategy model are as follows: Collect historical operating data of the air handling unit under different operating conditions, including real-time operating condition data, actuator action log data, environmental data, historical deviation data sets and corresponding adjustment results; Clean, standardize and classify historical operation data to form a structured data set; Extract characteristic parameters that play a key role in adjustment decisions from structured data sets and select core features through statistical analysis; Establish a working condition feature knowledge base and case library based on core features and historical operating data; The structured data set is divided into training set, validation set and test set. Based on the working condition feature knowledge base and case library, an adjustment strategy model including the mechanism model architecture and data model architecture is established through the training set, validation set and test set. The adjustment strategy model is iteratively optimized through the continuously collected operation data.
[0011] Furthermore, the action log adopts a time-series data stream format and has a power-off resume mechanism. The power-off resume mechanism is specifically as follows: The action log file is cached locally and attempts to upload the action log to the controller. When the upload fails, it will automatically retry. When the number of retries exceeds the preset value, it will be marked as an upload failure and manual intervention measures will be taken.
[0012] A control system for an air handling unit with adjustable working conditions and variable channels, comprising: The controller is equipped with a decision-making module, a command parsing module, and a sensor self-test module to determine the start and stop of the air handling unit; The sensor group is connected to the controller and is equipped with a variety of different types of sensors for collecting working condition data and environmental data in real time and transmitting them to the controller; The actuator, including electric valves, variable frequency fans, compressors, heaters, humidifiers, and filters, is used to receive execution instructions generated by the controller and perform actions, generating action logs and uploading them to the controller; Human-machine interaction interface, used to input start-up commands and display the unit operating status.
[0013] Furthermore, the decision generation module includes: Data processing unit, used to collect historical operation data and perform pre-processing; A database construction unit is connected to the data processing unit, establishes a working condition feature knowledge base and a case library through the data collected by the data processing unit, and updates the working condition feature knowledge base and the case library according to the historical operation data collected in real time; The model training unit is used to train the adjustment strategy model including the mechanism model architecture and the data model architecture, and iteratively optimize the adjustment strategy model through the real-time updated working condition feature knowledge base and case library.
[0014] Compared with the prior art, the control method and system for an air handling unit with adjustable working conditions and variable channels provided by the present invention have the following beneficial effects: This invention integrates target operating parameters with environmental data to accurately determine four operating modes and rapidly switch between them. This not only saves energy but also addresses the parameter fluctuations that occur during mode switching in traditional systems. After the actuator operates, it undergoes high-frequency dynamic monitoring, verifying that deviation data meets standards through multiple cycles, thus avoiding the blindness of single adjustments.
[0015] This invention uses a sliding average filter to eliminate noise and a weighted fusion algorithm to generate a comprehensive data vector, solving the problem of sensor data distortion and increasing the proportion of valid data for key parameters. The sensor self-check program uses a three-level checksum to reduce false alarm rates, avoid unnecessary downtime, and lower maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 The present invention provides a flow chart of a control method for an air handling unit with adjustable working conditions and variable channels.
[0017] Figure 2The figure is a module diagram of a control system for an air handling unit with adjustable working conditions and variable channels in the present invention. DETAILED DESCRIPTION
[0018] In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "center," and "inner" are used to indicate positions or locations based on those shown in the accompanying drawings. These terms are intended solely to facilitate the description of the present invention and simplify the description. They are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and should not be construed to indicate or imply relative importance or implicitly specify the number of the technical features referred to. Thus, features defined as "first," "second," and the like may explicitly or implicitly include one or more of such features. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they can refer to fixed connections, removable connections, or integral connections; they can refer to direct connections, indirect connections through an intermediary, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.
[0019] The following describes the implementation of the present invention in conjunction with the accompanying drawings and relevant embodiments. The implementation of the present invention is not limited to the following embodiments, and the present invention relates to the relevant necessary components in this technical field, which should be regarded as the common knowledge in this technical field and can be known and mastered by technical personnel in this technical field.
[0020] See Figure 1 A control method for an air handling unit with adjustable working condition and variable channel operation comprises the following steps: Step S1: Receive the start-up command of the air handling unit, parse the start-up command to obtain the target operating parameters and trigger the sensor self-test program, and output the self-test results; the start-up command mainly includes the operating mode, such as heating, cooling and ventilation mode. The target operating parameters are the target temperature, humidity and cleanliness input by the user, which are the effects that the user expects the unit to achieve after operation. The sensor self-test program detects the sensor group of the unit to prevent excessive damage to the sensor group, which may cause unit failure, and avoid control failure caused by sensor abnormalities. The damaged sensor is discovered and the user is notified for repair as soon as possible, thereby reducing subsequent maintenance costs. By parsing the start-up command, the core control objectives of the unit operation are clarified, providing a quantitative benchmark for subsequent adjustments and avoiding blind adjustments caused by parameter ambiguity. The sensor self-test program is triggered, and the accuracy and availability of key sensor data are ensured through the triple mechanism of communication link detection, reference source comparison calibration, and master / slave sensor redundancy verification.
[0021] Step S2: The air handling unit is started based on the startup command and self-test results. The sensor group collects operating and environmental data in real time, preprocesses the operating and environmental data, and fuses them to form a multi-source data set. If the self-test results indicate excessive sensor group damage, resulting in a serious fault, the unit is prohibited from starting. If the self-test results indicate the sensor group is normal, the corresponding air duct is opened according to the operating mode in the startup command. For example, if the operating mode is heating mode, the heating duct is opened, and if the operating mode is cooling mode, the cooling duct is opened. The heating and cooling ducts are disconnected and connected via a separating valve. The multi-source data set contains both the unit's internal operating data and external environmental data. The sensor group collects operating and environmental data in real time, building a three-dimensional data network covering the equipment's operating status and the external environment, addressing the one-sidedness of traditional control that relies solely on a single parameter.
[0022] Step S3, determine the current operating condition based on the target operating condition parameters and the multi-source data set, and based on the current operating condition, compare the target operating condition parameters with the multi-source data set to obtain a deviation data set; different operating conditions correspond to different bias parameters, and the target operating condition parameters have bias parameters corresponding to different operating conditions. According to these bias parameters, the corresponding parameters in the multi-source data set are compared to obtain the absolute value of the difference to form a deviation data set. The deviation data set can reflect the deviation between the current unit operating status and the ideal operating condition that the user needs to achieve. The multi-source data set is updated in real time based on the real-time collected operating condition data and environmental data, and the update frequency can be set by the user.
[0023] Step S4: Establish an adjustment strategy model based on historical data, input the deviation dataset to obtain the optimal adjustment decision, and based on the optimal adjustment decision, send control instructions to the corresponding actuators. The optimal adjustment decision specifically involves adjusting the air volume, air valve group, surface cooler, and humidifier. The unit operating parameters are adjusted according to the actual unit operating status to achieve the ideal unit operating state required by the user. The adjustment strategy model quickly matches the optimal adjustment solution based on the deviation dataset, replacing the extensive adjustment that relies on manual experience in traditional control. The optimal adjustment decision output by the model enables coordinated action of the actuators, avoiding parameter coupling problems caused by blind adjustment of a single device and improving system stability.
[0024] Step S5: The actuator receives the control instruction, performs an action based on the parsed control instruction, and generates an action log for uploading.
[0025] In one embodiment of the present invention, step S5 further includes: When the actuator action is complete, the sensor group enters the dynamic monitoring phase, acquiring real-time operating condition data and environmental data at a high sampling frequency to form a multi-source data set. The operation in step S3 is repeated to obtain a deviation data set, which is then judged. When all parameters in the deviation data set are within the preset target threshold range, the action is determined to be valid, the current operating state is maintained, and an action log is generated and uploaded. Through high-frequency sampling, parameter fluctuations of the air handling unit after the actuator action are captured in real time, avoiding delayed monitoring of operating condition changes due to long sampling cycles. The multi-source data set formed by high-frequency sampling can eliminate accidental errors through time series analysis. Combined with the preprocessing algorithm in step S2, this further improves data credibility and avoids misjudgments caused by data noise.
[0026] When any parameter in the deviation data set exceeds the preset target threshold range, steps S3-S5 are repeated until the number of cycles reaches the set value or the parameters in the deviation data set are all within the preset target threshold range, ending the cycle and generating an action log for upload. Through multiple rounds of deviation verification, the fluctuation range of the operating parameters is controlled within the target threshold, avoiding overshoot or undershoot caused by a single adjustment, reducing the frequency of equipment start and stop, and extending the service life of the actuator. Through high-frequency data feedback, it adapts to environmental changes in real time and improves the operating reliability of the unit in different seasons and different operating conditions. The action log can provide a complete data chain for subsequent fault diagnosis. The number of cyclic adjustments and the deviation convergence speed can be used as a basis for judging the health status of the equipment. When the number of cycles frequently reaches the upper limit, it can warn of a decrease in sensor accuracy or an abnormality in the actuator, making it easier for maintenance personnel to troubleshoot in advance.
[0027] It should be noted that after the sensor group enters the dynamic monitoring stage, the frequency is switched from the conventional low-frequency mode to the high-frequency mode to ensure that subtle changes in the operating parameters of the unit during operation are quickly captured, and the multi-source data set and the deviation data set are quickly updated, so that the unit's operating status can quickly reach the user's historical operating status. The preset target threshold interval is differentially configured according to different operating conditions. For example, the temperature target threshold interval of the full cooling mode in summer is smaller than the temperature target threshold interval of the heating mode in winter. The target threshold interval is obtained through historical effective adjustment data statistics to ensure rationality and robustness.
[0028] In repeating steps S3-S5, when the parameters in the deviation data set are seriously deviated, that is, when the deviation value of a certain parameter far exceeds the preset target threshold range, a fast cycle mode is performed to speed up the cycle time of steps S3-S5, increase the sampling frequency, and give priority to verifying the deviation value of this parameter. When this parameter continues to far exceed the preset target threshold range for multiple cycles, the unit is determined to be faulty, the fault information is uploaded and an alarm is issued, and the operation and maintenance personnel are notified to handle it.
[0029] In one embodiment of the present invention, the target operating condition parameters in the startup command include target temperature, target humidity, target air cleanliness, and a set of standard parameter values corresponding to different operating conditions, wherein the different operating conditions include a summer full cooling mode, a transition season cooling mode, a winter heating mode, and a mixed mode. Determining the current operating condition in step S3 specifically includes: The operating mode is determined based on the target temperature and target humidity in the target operating condition parameters, the current season is confirmed through environmental data in the multi-source data set, and the current operating condition is determined in combination with the current season and the operating mode.
[0030] It should be noted that the target temperature refers to the indoor temperature that the user wants when the unit is operating. Similarly, the target humidity and target air cleanliness refer to the indoor humidity and indoor air cleanliness that the user wants when the unit is operating. The target temperature, target humidity and target air cleanliness can all be input by the user. The standard parameter value set corresponding to different working conditions is a built-in default parameter, which the user can change through the control software. The initial operating command of the operating mode and the target operating condition parameters can form a startup command. The initial operating command refers to the initial operating state of the unit, including cooling mode, heating mode and ventilation mode. After the unit determines the operating mode through the target temperature and target humidity, it switches to the determined operating mode. The unit has a built-in The fixed air duct corresponding to the operating mode is opened after the operating mode is determined. The operating modes include cooling and dehumidification mode, cooling mode only, heating mode only, heating and humidification mode, local heating and local cooling mode. The corresponding air duct and humidity control measures are opened according to the determined operating mode. When it is determined to be cooling and dehumidification mode or cooling mode only and the environmental data is determined to be high temperature in summer, it is determined to be summer full cooling mode. When it is determined to be cooling and dehumidification mode or cooling mode only and the environmental data is determined to be spring and autumn, it is determined to be transition season cooling mode. When it is determined to be heating mode only and heating and humidification mode and the environmental data is determined to be winter, it is determined to be winter heating mode. When it is determined to be local heating and local cooling mode, it is determined to be mixed mode. Among them, the summer full cooling mode is suitable for the high temperature and high humidity season in summer. At this time, the refrigeration system will be turned on according to the target temperature and target humidity to cool the air and reduce humidity. The introduction of fresh air will be turned off or reduced, and the fresh air will be deeply cooled and dehumidified. The air filter will continuously filter the air to ensure that the air cleanliness reaches the target air cleanliness. The summer full cooling mode is mainly for the purpose of cooling. At this time, the unit power is relatively high and the cooling demand is high.
[0031] The transition season cooling mode is suitable for spring and autumn, when the temperature difference between indoor and outdoor is not large. Outdoor fresh air is introduced first to cool the indoor air after filtration, and the fresh air channel is opened to reduce the operation of the compressor and reduce energy consumption. The fresh air volume is dynamically adjusted according to the indoor and outdoor temperature difference and air quality. When the indoor and outdoor temperature difference is low and the air quality is good, outdoor fresh air is introduced first to increase the fresh air volume and reduce the energy consumption of the unit while ensuring indoor comfort.
[0032] The winter heating mode is suitable for the low temperature season in winter. At this time, you need to open the heating duct, start the heating system to warm the air and turn on the humidifier to increase the humidity. When introducing fresh air, it needs to be preheated to prevent cold air from affecting the indoor temperature.
[0033] The mixed mode refers to a special working condition. For example, some areas need to be heated and other areas need to be cooled. At this time, the set target parameters are used to determine which part needs to be heated and which part needs to be cooled. The heating duct and the cooling duct are opened at the same time, and the heating duct and the cooling duct are separated by a separation valve. Zoning processing and dynamic adjustment are used to adapt to the different needs of users in the mixed mode. The air volume and air duct are dynamically allocated according to the different needs of different areas. The air handling unit is divided into different processing paths through the variable channel structure to perform heating, cooling, dehumidification and humidification operations respectively.
[0034] The four modes, coupled with target parameters and environmental data, enable adaptive adjustment for summer cooling, winter heating, transitional seasonal energy, and zoning management, avoiding the energy waste and lack of comfort associated with traditional single-mode operation. The hybrid mode addresses load conflicts between different zones in large buildings through zoning and variable channel technology. Traditional units struggle to simultaneously meet the diverse parameter requirements of multiple zones. This new approach improves system adaptability by dynamically adjusting processing paths.
[0035] In one embodiment of the present invention, in step S1, a start command is received through a human-computer interaction interface, the start command is parsed, target operating parameters are obtained, and a sensor self-test program is triggered. The sensor self-test program is used to initialize and verify the sensor group, and specifically includes: The controller obtains the initial data output by the sensor group, tests the communication link between each sensor in the sensor group and the controller, and collects and analyzes abnormal data. Communication link testing involves the controller performing two-way communication testing on the sensor group, including signal strength verification, data format verification, and response time monitoring. Signal strength verification specifically detects the sensor signal attenuation value, data format verification specifically parses the digital message output by the sensor and checks the CRC checksum, and response time monitoring specifically records the time from the sensor sending the request to the return of data. If a timeout occurs, it is determined to be a communication anomaly. When a communication link anomaly is detected, communication is retried with the abnormal sensor. If communication fails again, it is marked as a communication link anomaly. Signal strength verification, data format verification, and response time monitoring can detect sensor communication risks in advance.
[0036] The initial data output by the sensor group is compared with the built-in reference source. When the deviation between the sensor and the built-in reference source is greater than the preset value, the calibration process is triggered to calibrate the sensor and generate a calibration log. When the deviation between the sensor and the built-in reference source is less than the preset value, it is marked as normal. The system has a built-in high-precision reference module, that is, a built-in reference source, which is installed in a constant temperature and humidity chamber. Calibration is performed at regular intervals to ensure accuracy. The calibration log includes data before and after calibration, calibration time, and calibration coefficient information to support subsequent audit traceability.
[0037] Verify the primary and backup sensors. When the data of the primary and backup sensors are consistent, mark them as normal. When the data of the primary and backup sensors are inconsistent, mark them as data consistency abnormality. Calculate the relative deviation of the primary and backup sensor data. When the relative deviation is greater than the preset value, mark it as data consistency abnormality.
[0038] Combined with abnormal data detected by the communication link, calibration logs, and data consistency abnormalities, a self-test result containing self-test data is generated. When the data in the self-test result meets the preset conditions, it is determined to be a serious fault, the unit is prohibited from starting, and an alarm is issued. The preset conditions include the following: The number of sensor communication link abnormalities is greater than the preset value; when the number of abnormalities far exceeds the preset value, it is directly judged as a serious fault. When the number of abnormalities just exceeds the preset value, it is judged in combination with the other two conditions.
[0039] The data deviation after sensor calibration is greater than the preset value; when the data deviation after calibration far exceeds the preset value, it is directly judged as a serious fault. When the deviation just exceeds the preset value, it is judged in combination with the other two conditions.
[0040] The number of sensors with data consistency anomalies exceeds the preset value. When the number of anomalies far exceeds the preset value, it is directly judged as a serious fault. When the number of anomalies just exceeds the preset value, the other two conditions are combined to determine the fault.
[0041] When all three of the above conditions are met, it is determined to be a serious fault.
[0042] In one embodiment of the present invention, step S2 specifically includes: The air handling unit enters the standby stage upon receiving the start-up command and judges the self-test results. When the data in the self-test results meet the preset conditions, it is judged as a serious fault, the unit is prohibited from starting and an alarm is issued; when the data in the self-test results does not meet the preset conditions, it is judged as the self-test passed and the unit is started; when it is judged as a serious fault, the protection measures are triggered, the user is prompted on the human-computer interaction interface, and the fault information is sent to the preset operation and maintenance personnel via SMS or APP.
[0043] The pretreatment specifically includes: A sliding average filtering algorithm is used to remove sudden noise in operating condition data and environmental data; a sliding average filtering algorithm is used to increase the proportion of valid data and improve the reliability of control decisions.
[0044] Through data correlation verification, obviously unreasonable data in working condition data and environmental data are eliminated; the data correlation verification mechanism avoids misadjustment caused by false data.
[0045] The fusion of the working condition data and the environmental data to form a multi-source data set specifically includes: Data from different sensors and different types within a sensor group are aligned by timestamp, and a weighted fusion algorithm is used to generate a comprehensive data vector, forming a multi-source dataset. The timestamp alignment algorithm enables the system to capture transient changes in different parameters. A dynamic weight adjustment mechanism improves the reliability of the comprehensive data vector.
[0046] In one embodiment of the present invention, the specific steps of obtaining the deviation dataset are as follows: Based on the current operating condition, a set of standard parameter values corresponding to the target operating condition is retrieved from the target operating condition parameters. The parameters in the standard parameter value set are compared one by one with the corresponding parameters in the multi-source dataset to obtain absolute deviation values, and a deviation dataset consisting of multiple absolute deviation values is output. The current operating condition includes four operating conditions: full summer cooling, transitional season cooling, winter heating, and mixed mode. Standard parameter value sets are constructed for each of these four operating conditions. The standard parameter value set corresponding to each operating condition contains the core control parameters corresponding to that operating condition type. The absolute deviations are calculated by sampling the corresponding parameters in the multi-source dataset to obtain the absolute deviation values and obtain the deviation dataset corresponding to the core control parameters.
[0047] In one embodiment of the present invention, the specific steps of establishing the adjustment strategy model are as follows: Collect historical operating data of the air handling unit under different operating conditions, including real-time operating condition data, actuator action log data, environmental data, historical deviation data sets and corresponding adjustment results; Historical operating data is cleaned, standardized and classified to form a structured data set; data cleaning includes outlier detection and missing value filling, and classification is carried out by operating condition type, load level and adjustment result.
[0048] Extract characteristic parameters that play a key role in adjustment decisions from structured data sets and select core features through statistical analysis; Establish a working condition feature knowledge base and case library based on core features and historical operating data; The structured data set is divided into training set, validation set and test set. Based on the working condition feature knowledge base and case library, an adjustment strategy model including the mechanism model architecture and data model architecture is established through the training set, validation set and test set. The adjustment strategy model is iteratively optimized through the continuously collected operation data.
[0049] It should be noted that the operating condition characteristic knowledge base is a structured knowledge storage entity used to systematically summarize the characteristic parameter correlation, mechanism rules and prior knowledge of air handling units under different operating conditions. It is essentially an abstract expression of domain knowledge, providing physical mechanism-based constraint conditions and logical reasoning basis for the adjustment strategy model. The case library is a database that stores typical operating condition cases and corresponding adjustment strategies in the historical operation of air handling units. Each case contains a complete closed-loop information of "operating condition characteristics-deviation data-adjustment action-effect feedback" to support instance-based reasoning and model iterative optimization. Each case adopts a five-tuple structure, which includes the operating condition characteristic vector, deviation data set, actuator adjustment action set, adjusted result parameters and effect evaluation indicators. The adjustment strategy model can be expressed as:
[0050] Among them, X is the input feature vector, including real-time working condition data, real-time environmental data and historical deviation characteristics, K represents the mechanism rule set in the working condition feature knowledge base, and C represents the historical case set in the case library. is the mechanism model function, and the adjustment strategy is derived based on physical rules. is a data model function that adjusts strategies based on historical case learning. and These are fusion weights, determined through continuous optimization during model training. The output in the above formula is the actuator control strategy for the air handling unit, that is, the optimal adjustment decision. The output of this formula is essentially the historically optimal solution under the constraints of physical mechanisms, combining reliability, adaptability, and interpretability.
[0051] In one embodiment of the present invention, the action log adopts a time-series data stream format and has a power-off resume mechanism. The power-off resume mechanism is specifically as follows: The action log file is cached locally and attempts to upload it to the controller. Automatic retries are made if the upload fails. If the number of retries exceeds a preset value, an upload failure is flagged and manual intervention is initiated. The action log uses a structured data format with time series as the core dimension to ensure data traceability and time series analysis capabilities. It includes basic metadata, action details, and status feedback data. The power-off resume mechanism utilizes a three-tiered architecture of local caching, automatic retries, and fault classification to ensure that action logs are not lost in the event of network anomalies or power outages.
[0052] See Figure 2 , a control system for an air handling unit with adjustable working conditions and variable channels, comprising: The controller is equipped with a decision-making generation module, a command parsing module and a sensor self-test module, which are used to determine the start and stop of the air handling unit; the command parsing module is used to receive the start command input by the user and parse the command, thereby obtaining the core parameters in the start command and triggering the sensor self-test module. The sensor self-test module is used to detect the sensor group to determine whether the sensor group of the unit is normal, thereby determining whether to start the unit. The decision-making generation module is mainly used to generate execution instructions for the control actuator, which is used to adjust the operating parameters of the air handling unit to meet user needs.
[0053] The sensor group is connected to the controller and is equipped with a variety of different types of sensors for real-time collection of working condition data and environmental data and transmission to the controller; the sensor group has an anti-interference design and a surge protector is configured on the power supply end to reduce data distortion caused by electromagnetic interference.
[0054] The actuator, including electric valves, variable frequency fans, compressors, heaters, humidifiers and filters, is used to receive execution instructions generated by the controller and perform actions, generating action logs and uploading them to the controller. The actuator adopts a mechatronic design to achieve high-precision execution of control instructions and full-process traceability.
[0055] Human-machine interaction interface, used to input start-up commands and display the unit operating status.
[0056] In one embodiment of the present invention, the decision generation module includes: The data processing unit is used to collect historical operating data and perform preprocessing; the data processing unit builds a complete pipeline from data collection to preprocessing to ensure the quality and availability of data input to the model.
[0057] The database construction unit is connected to the data processing unit, establishes the working condition characteristic knowledge base and case library through the data collected by the data processing unit, and updates the working condition characteristic knowledge base and case library according to the historical operation data collected in real time; the database construction unit realizes the systematic management of the working condition characteristic knowledge base and case library.
[0058] The model training unit is used to train the adjustment strategy model, which includes both the mechanism model architecture and the data model architecture. This model is iteratively optimized using a real-time updated knowledge base of operating condition characteristics and case studies. The model training unit builds an intelligent engine that deeply integrates the mechanism model and the data model.
[0059] This invention integrates target operating parameters with environmental data to accurately determine four operating modes and rapidly switch between them. This not only saves energy but also addresses the parameter fluctuations that occur during mode switching in traditional systems. After the actuator operates, it undergoes high-frequency dynamic monitoring, verifying that deviation data meets standards through multiple cycles, thus avoiding the blindness of single adjustments.
[0060] This invention uses a sliding average filter to eliminate noise and a weighted fusion algorithm to generate a comprehensive data vector, solving the problem of sensor data distortion and increasing the proportion of valid data for key parameters. The sensor self-check program uses a three-level checksum to reduce false alarm rates, avoid unnecessary downtime, and lower maintenance costs.
[0061] The above description is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.
Claims
1. A control method for an air handling unit with adjustable working condition and variable channel operation, characterized in that: The following steps are involved: Step S1: receiving an air handling unit startup command, parsing the startup command to obtain target operating parameters, triggering a sensor self-test program, and outputting a self-test result; Step S2: starting the air handling unit based on the start-up command and the self-test result, collecting operating condition data and environmental data in real time through the sensor group, pre-processing the operating condition data and environmental data, and fusing them to form a multi-source data set; Step S3: determining the current operating condition based on the target operating condition parameters and the multi-source data set, and comparing the target operating condition parameters with the multi-source data set based on the current operating condition to obtain a deviation data set; Step S4: Establish an adjustment strategy model based on historical data, input the deviation data set to obtain the best adjustment decision, and send a control instruction to the corresponding actuator based on the best adjustment decision; Step S5: The actuator receives the control instruction, performs an action based on the parsed control instruction, and generates an action log for uploading.
2. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: The step S5 further includes: When the actuator action is completed, the sensor group enters the dynamic monitoring phase, acquiring real-time working condition data and environmental data at a high sampling frequency to form a multi-source data set. The operation of step S3 is repeated to obtain a deviation data set, and the deviation data set is judged. When all parameters in the deviation data set are within the preset target threshold range, the action is determined to be valid, the current operating state is maintained, and an action log is generated and uploaded; When any parameter in the deviation data set exceeds the preset target threshold range, steps S3-S5 are repeated until the number of cycles reaches the set value or the parameters in the deviation data set are all within the preset target threshold range, the cycle ends and the action log is generated for upload.
3. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: The target operating condition parameters in the startup command include target temperature, target humidity, target air cleanliness, and a set of standard parameter values corresponding to different operating conditions, wherein the different operating conditions include summer full cooling mode, transition season cooling mode, winter heating mode, and mixed mode. The determination of the current operating condition in step S3 specifically includes: The operating mode is determined based on the target temperature and target humidity in the target operating condition parameters, the current season is confirmed through environmental data in the multi-source data set, and the current operating condition is determined in combination with the current season and the operating mode.
4. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: In step S1, a start command is received through a human-computer interaction interface, the start command is parsed, target operating parameters are obtained, and a sensor self-test program is triggered. The sensor self-test program is used to initialize and verify the sensor group, specifically including: The controller obtains the initial data output by the sensor group, detects the communication link between each sensor in the sensor group and the controller, and collects statistics on abnormal data; The initial data output by the sensor group is compared with the built-in reference source. When the deviation between the sensor and the built-in reference source is greater than the preset value, the calibration process is triggered to calibrate the sensor and generate a calibration log. When the deviation between the sensor and the built-in reference source is less than the preset value, it is marked as normal. Verify the primary and backup sensors. If the data from the primary and backup sensors are consistent, mark them as normal. If the data from the primary and backup sensors are inconsistent, mark them as data consistency abnormal. Combined with the abnormal data detected by the communication link, the calibration log and the data consistency abnormality, a self-test result containing self-test data is generated. When the data in the self-test result meets the preset conditions, it is determined to be a serious fault, the unit is prohibited from starting and an alarm is issued.
5. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: The step S2 specifically includes: The air handling unit enters the standby phase upon receiving the start command and determines the self-test results. If the data in the self-test results meet the preset conditions, it is determined to be a serious fault, the unit is prohibited from starting, and an alarm is issued; if the data in the self-test results do not meet the preset conditions, it is determined to have passed the self-test and the unit is started. The pretreatment specifically includes: Use sliding average filtering algorithm to remove sudden noise in working condition data and environmental data; Eliminate obviously unreasonable data in working condition data and environmental data through data correlation verification; The fusion of the working condition data and the environmental data to form a multi-source data set specifically includes: The data of different sensors and different types in the sensor group are aligned by timestamps, and a comprehensive data vector is generated through a weighted fusion algorithm to form a multi-source data set.
6. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 3 is characterized in that: The specific steps for obtaining the deviation dataset are as follows: Based on the current working condition, the standard parameter value set corresponding to the working condition is retrieved from the target working condition parameters, and the parameters in the standard parameter value set are compared one by one with the corresponding parameters in the multi-source data set to obtain the absolute deviation value, and a deviation data set consisting of multiple absolute deviation values is output.
7. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: The specific steps for establishing the adjustment strategy model are as follows: Collect historical operating data of the air handling unit under different operating conditions, including real-time operating condition data, actuator action log data, environmental data, historical deviation data sets and corresponding adjustment results; Clean, standardize and classify historical operation data to form a structured data set; Extract characteristic parameters that play a key role in adjustment decisions from structured data sets and select core features through statistical analysis; Establish a working condition feature knowledge base and case library based on core features and historical operating data; The structured data set is divided into training set, validation set and test set. Based on the working condition feature knowledge base and case library, an adjustment strategy model including the mechanism model architecture and data model architecture is established through the training set, validation set and test set. The adjustment strategy model is iteratively optimized through the continuously collected operation data.
8. The control method for an air handling unit with adjustable working condition and variable channel operation according to claim 1 is characterized in that: The action log adopts a time-series data stream format and has a power-off resume mechanism. The specific power-off resume mechanism is: The action log file is cached locally and attempts to upload the action log to the controller. When the upload fails, it will automatically retry. When the number of retries exceeds the preset value, it will be marked as an upload failure and manual intervention measures will be taken.
9. A control system for an air handling unit with adjustable working conditions and variable channels, characterized in that: A control method for implementing any one of claims 1 to 8, comprising: The controller is equipped with a decision-making module, a command parsing module, and a sensor self-test module to determine the start and stop of the air handling unit; The sensor group is connected to the controller and is equipped with a variety of different types of sensors for collecting working condition data and environmental data in real time and transmitting them to the controller; The actuator, including electric valves, variable frequency fans, compressors, heaters, humidifiers, and filters, is used to receive execution instructions generated by the controller and perform actions, generating action logs and uploading them to the controller; Human-machine interaction interface, used to input start-up commands and display the unit operating status.
10. The control system for an air handling unit with adjustable working conditions and variable channels according to claim 9, characterized in that: The decision making module includes: Data processing unit, used to collect historical operation data and perform pre-processing; A database construction unit is connected to the data processing unit, establishes a working condition feature knowledge base and a case library through the data collected by the data processing unit, and updates the working condition feature knowledge base and the case library according to the historical operation data collected in real time; The model training unit is used to train the adjustment strategy model including the mechanism model architecture and the data model architecture, and iteratively optimize the adjustment strategy model through the real-time updated working condition feature knowledge base and case library.