High-efficiency intelligent control all-air handling unit and control method thereof

Abstract

The application discloses a kind of high-efficiency intelligent control full air treatment device, including body, edge computing gateway, execution control layer and sensor array.Edge computing gateway is as initiative prediction module, and preset time is started load prediction model output pre-regulation parameter;As energy efficiency optimization decision maker, periodically obtain the energy efficiency index calculated by sensor data, and generate optimization instruction by comparison through strategy library. Execution control layer adjusts fan, water valve and air valve according to instruction. The device makes system look ahead to adapt to load change and guarantee safety and comfort through initiative prediction and hierarchical constraint control;With "air-water collaborative optimization" as core, through the communication of air-side energy efficiency module and water-side energy efficiency module, the air supply is solved subsystem disconnection problem;Combined with air-water collaborative optimization and closed-loop optimization, effectively overcome air-water subsystem load disconnection, improve overall energy efficiency;With self-sensing optimization function, continuously dynamic approach optimal working condition, realize energy saving and comfort comprehensive optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of air conditioning technology, in particular to an efficient intelligent control full air treatment device and a control method thereof. BACKGROUND

[0002] As the core terminal equipment of central air conditioning system, the energy consumption of combined air handling unit accounts for a significant proportion of the total building energy consumption. The mainstream air treatment devices on the market generally use passive feedback control mode based on fixed set point, such as PID adjustment by monitoring room temperature deviation. This control mode has obvious response lag and cannot effectively follow the changes of building dynamic load, which easily leads to "over-regulation" or "under-regulation", affecting the environmental comfort and causing additional energy waste.

[0003] Further, the existing device has serious deficiencies in system coordination. The key actuators such as fan, water valve and air valve usually operate independently, lacking unified coordinated control mechanism. This "each for itself" operation mode leads to the disconnection of load demand of air system and water system, so that the whole air treatment device operates in non-coordinated condition for a long time, the overall energy efficiency of the system is far from optimal level, causing invisible energy loss.

[0004] In addition, the current products generally lack the ability to perceive and optimize their own energy efficiency. Most of the devices cannot calculate and track key energy efficiency indicators such as energy efficiency ratio (EER) and power consumption per unit air volume in real time, not to mention the intelligent decision-making function of automatic adjustment according to energy efficiency deviation. The adjustment of system parameters highly depends on human experience, which is difficult to ensure the continuous maintenance of high efficiency in complex actual operation environment. At the same time, the resistance of traditional filter module is large and uncontrollable, which also becomes an uncertain factor affecting the energy consumption of fan. Therefore, there is an urgent need for an intelligent air treatment solution that can realize active prediction, system coordination and continuous self-optimization. SUMMARY

[0005] In view of the above technical problems in the related art, the present application provides an efficient intelligent control full air treatment device and a control method thereof, which can overcome the above deficiencies of the prior art.

[0006] To achieve the above technical purposes, the technical solution of the present application is as follows:

[0007] An efficient intelligent control full air treatment device;

[0008] The efficient intelligent control full air treatment device and method comprise a machine body, and further comprise:

[0009] The edge computing gateway is configured as an active prediction module for starting a load prediction model at a preset time and obtaining external data through a communication module to output a pre-adjustment parameter; the edge computing gateway is further connected with a communication module for data interaction with a cloud platform and an external data source;

[0010] An execution control layer is connected with the edge computing gateway, and the execution control layer includes a frequency converter, an electric water valve actuator and a damper actuator for adjusting the rotation speed of the fan, the water flow and the fresh air ratio according to the pre-adjustment parameter and the optimization instruction;

[0011] A sensor array is used for real-time monitoring of environmental parameters and equipment operation parameters;

[0012] The edge computing gateway is further configured as an energy efficiency optimization decision maker, which has an optimization strategy library built-in and is configured to periodically obtain a current energy efficiency index calculated by the sensor array data, compare the current energy efficiency index with an expected energy efficiency index, and generate the optimization instruction according to the corresponding strategy in the optimization strategy library according to the comparison result, wherein the energy efficiency index includes unit air consumption power φ and cooling and heating energy efficiency ratio EER; the optimization strategy library at least includes:

[0013] When EER is lower than the expected value and φ is in the normal range, the optimization water side strategy is called;

[0014] When φ is higher than the expected value and EER is in the normal range, the optimization air side strategy is called;

[0015] When both φ and EER deviate from the expected value, the air-water collaborative optimization strategy is called;

[0016] When both φ and EER reach the expected value, the fresh air optimization strategy is called.

[0017] Further, the pre-adjustment parameter includes fan frequency initial value f0, electric water valve opening initial value θ0 and fresh air ratio initial value α0.

[0018] Further, the control logic of the edge computing gateway contains core constraints, including a safety protection constraint with the highest priority and a comfort guarantee constraint with the second priority; the generation and execution of all pre-adjustment parameters and optimization instructions must not violate the core constraints.

[0019] Further, the decision-making cycle of the energy efficiency optimization decision maker can be set.

[0020] Further, it further includes a dust removal energy saving module, which includes a primary filter and a low resistance electrostatic precipitator arranged along the airflow direction, and a differential pressure sensor is arranged for monitoring the resistance thereof, and an alarm is given when the resistance exceeds a set threshold.

[0021] Furthermore, it also includes a water-side energy efficiency module and a wind-side energy efficiency module. The water-side energy efficiency module is used to monitor the flow rate, supply and return water temperature and pressure difference of the water system, and the wind-side energy efficiency module is used to monitor the supply air and mixing air temperature. The wind-side energy efficiency module communicates with the water-side energy efficiency module to calculate the supply air volume based on the water system parameters and the wind-side temperature difference, and to perform wind-water load balance calculation.

[0022] Furthermore, it also includes an integrated cabinet for both strong and weak current systems, which is integrated with the electromechanical system of the main body; the edge computing gateway, communication module, cloud energy efficiency platform, frequency converter, multi-function meter and PLC main control screen are integrated in the integrated cabinet for both strong and weak current systems, and the integrated cabinet for both strong and weak current systems is connected to the sensor array and the execution control layer.

[0023] Furthermore, the load forecasting model is activated at a preset time each day and performs load forecasting by integrating historical operating data, weather forecasts, and building schedule information.

[0024] According to another aspect of the present invention, a control method for a highly efficient intelligent control all-air handling device is provided;

[0025] The method for this highly efficient intelligent control all-air handling device includes the following steps:

[0026] S1: Proactive prediction. At preset times each day, the edge computing gateway starts the load prediction model, which integrates historical operating data, weather forecasts and building schedule information to generate pre-adjustment parameters.

[0027] S2: Constraint verification to ensure that the pre-adjustment parameters meet the core constraints of safety protection and comfort assurance;

[0028] S3: Pre-adjustment execution, which sends the verified pre-adjustment parameters to the execution control layer to perform initial settings for the device;

[0029] S4: Data monitoring and energy efficiency calculation. Data is collected in real time through a sensor array. The real-time data from the sensor array triggers the edge computing gateway and calculates the current power consumption φ per unit air volume and the energy efficiency ratio (EER) for cooling and heating. The energy efficiency calculation cycle can be set.

[0030] S5: Dynamic optimization decision-making, periodically compares the current φ and EER with the expected values, triggers the corresponding strategy in the optimization strategy library based on the comparison results, and generates optimization instructions;

[0031] S6: Optimize execution, send optimization instructions to the execution control layer to dynamically fine-tune the device's operating parameters.

[0032] The beneficial effects of this invention are as follows: By introducing intelligent control logic with active prediction and hierarchical constraints, the air handling unit can proactively adapt to load changes and ensure safe and comfortable operation; furthermore, by combining the air-water synergy strategy and closed-loop energy efficiency optimization mechanism, the problem of load disconnection between subsystems is effectively solved, and the overall energy efficiency is improved at the system level; finally, the device has the ability to sense and optimize itself, and can continuously and dynamically approach the optimal operating conditions, achieving the comprehensive optimization goal of energy saving and comfort. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the overall structure and module connection of a high-efficiency intelligent control all-air treatment device according to an embodiment of the present invention;

[0035] Figure 2 This is a core control logic block diagram of a control method for a high-efficiency intelligent control all-air treatment device according to an embodiment of the present invention;

[0036] In the diagram: 1. Main unit; 2. Integrated power and low voltage cabinet; 3. Dust removal and energy saving module; 31. Primary filter; 32. Low-resistance electrostatic precipitator; 4. Water-side energy efficiency module; 5. Air-side energy efficiency module; 6. Edge computing gateway; 7. Communication module; 8. Cloud energy efficiency platform; 9. Frequency converter; 10. Multifunctional meter. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0038] It should be understood that in the description of the embodiments of the present invention, the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing the embodiments of the present invention and for simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of the present invention, "several" means two or more, unless otherwise explicitly specified.

[0039] like Figure 1 As shown, a high-efficiency intelligent control all-air treatment device according to an embodiment of the present invention includes a body 1, characterized in that it further includes:

[0040] Edge computing gateway 6 is electrically connected to and works in conjunction with 5G communication module 7. It is understood that the 5G communication module is merely one high-performance implementation of communication module 7 and is not a limitation on its specific technology type. Other wired or wireless communication modules, such as 4G, Wi-Fi, and Ethernet, are also applicable to this invention. Specifically, 5G communication module 7 is responsible for rapidly acquiring real-time updated information such as weather forecasts and construction schedules from external data sources such as cloud-based energy efficiency platform 8 and meteorological data servers. Edge computing gateway 6 is responsible for running the load forecasting model locally, processing the external data acquired by 5G communication module 7 and historical operating data from the sensor array, and ultimately generating pre-adjustment parameters.

[0041] An execution control layer, connected to the edge computing gateway 6, includes a frequency converter, an electric water valve actuator, and an air valve actuator, used to adjust the fan speed, water flow rate, and fresh air ratio according to the pre-adjustment parameters and optimization instructions;

[0042] Sensor arrays are used to monitor environmental parameters and equipment operating parameters in real time;

[0043] The edge computing gateway 6 is also configured as an energy efficiency optimization decision-maker. It has a built-in optimization strategy library and is configured to periodically obtain the current energy efficiency index calculated from the sensor array data, compare the current energy efficiency index with the expected energy efficiency index, and generate the optimization instruction by calling the corresponding strategy in the optimization strategy library according to the comparison result.

[0044] According to an embodiment of the present invention, a high-efficiency intelligent control all-air treatment device is provided. In a specific embodiment, the pre-adjustment parameters include the initial value of the fan frequency f0, the initial value of the electric water valve opening θ0, and the initial value of the fresh air ratio α0.

[0045] According to an embodiment of the present invention, in a specific embodiment of a high-efficiency intelligent control all-air treatment device, the control logic of the edge computing gateway 6 includes core constraints, which include a safety protection constraint with the highest priority and a comfort guarantee constraint with the second priority; the generation and execution of all pre-adjustment parameters and optimization instructions shall not violate the core constraints.

[0046] According to an embodiment of the present invention, a high-efficiency intelligent control all-air handling device is provided. In a specific embodiment, the energy efficiency indicators include power consumption per unit air volume φ and energy efficiency ratio (EER); the optimization strategy library includes at least:

[0047] When EER is lower than the expected value while φ is within the normal range, the optimized water-side strategy is invoked.

[0048] When φ is higher than the expected value but EER is within the normal range, the optimized wind-side strategy is invoked.

[0049] When both φ and EER deviate from the expected values, the Feng Shui collaborative optimization strategy is invoked.

[0050] When both φ and EER reach the expected values, the fresh air optimization strategy is invoked.

[0051] According to an embodiment of the present invention, in a specific embodiment of a high-efficiency intelligent control all-air treatment device, the decision cycle of the energy efficiency optimization decision-maker can be set.

[0052] According to an embodiment of the present invention, a high-efficiency intelligent control all-air treatment device further includes a dust removal and energy-saving module 3 in a specific embodiment. The dust removal and energy-saving module 3 includes a primary filter 31 and a low-resistance electrostatic precipitator 32 arranged along the airflow direction, and is equipped with a differential pressure sensor to monitor its resistance. When the resistance exceeds a set threshold, an alarm is triggered.

[0053] According to an embodiment of the present invention, a high-efficiency intelligent control all-air handling device further includes a water-side energy efficiency module 4 and a wind-side energy efficiency module 5 in a specific embodiment. The water-side energy efficiency module 4 is used to monitor the flow rate, supply and return water temperature and pressure difference of the water system, and the wind-side energy efficiency module 5 is used to monitor the supply air and mixing air temperature. The wind-side energy efficiency module 5 communicates with the water-side energy efficiency module 4 and is used to calculate the supply air volume based on the water system parameters and the wind-side temperature difference, and to perform wind-water load balance calculation.

[0054] According to an embodiment of the present invention, a high-efficiency intelligent control all-air treatment device further includes, in a specific embodiment, a power and weak current integrated cabinet 2, which is electromechanically integrated with the main body 1; the edge computing gateway 6, communication module 7, cloud energy efficiency platform 8, frequency converter 9, multi-function meter 10 and PLC main control screen are integrated in the power and weak current integrated cabinet 2, and the power and weak current integrated cabinet 2 is connected to the sensor array and the execution control layer.

[0055] According to an embodiment of the present invention, a high-efficiency intelligent control all-air treatment device is provided. In a specific embodiment, the load prediction model is activated at a preset time each day and performs load prediction by integrating historical operating data, weather forecasts, and building schedule information.

[0056] On the other hand, a control method for a high-efficiency intelligent control all-air handling device according to an embodiment of the present invention includes the following steps:

[0057] S1: Proactive prediction. At preset times each day, the edge computing gateway 6 starts the load prediction model, which integrates historical operating data, weather forecasts and building schedule information to generate pre-adjustment parameters.

[0058] S2: Constraint verification to ensure that the pre-adjustment parameters meet the core constraints of safety protection and comfort assurance;

[0059] S3: Pre-adjustment execution, which sends the verified pre-adjustment parameters to the execution control layer to perform initial settings for the device;

[0060] S4: Data monitoring and energy efficiency calculation. Data is collected in real time through a sensor array, and the current power consumption φ per unit air volume and the energy efficiency ratio EER are calculated.

[0061] S5: Dynamic optimization decision-making, periodically compares the current φ and EER with the expected values, triggers the corresponding strategy in the optimization strategy library based on the comparison results, and generates optimization instructions;

[0062] S6: Optimize execution, send optimization instructions to the execution control layer to dynamically fine-tune the device's operating parameters.

[0063] To facilitate understanding of the above technical solutions of the present invention, the following detailed description of the above technical solutions of the present invention will be provided through specific usage methods.

[0064] In practical use, the high-efficiency intelligent control all-air treatment device according to the present invention includes a main body 1, a strong and weak current integrated cabinet 2, a dust removal and energy saving module 3, a water-side energy efficiency module 4, a wind-side energy efficiency module 5, an edge computing gateway 6, and a PLC main control screen.

[0065] The integrated power and weak current cabinet 2 is electromechanically integrated with the main body 1, and integrates an edge computing gateway 6, a 5G communication module 7, a cloud energy efficiency platform 8, a frequency converter 9, a multi-functional meter 10, and a PLC main control screen, and is connected to a sensor array and an execution control layer. The dust removal and energy saving module 3 includes a primary filter 31 and a low-resistance electrostatic precipitator 32 arranged along the airflow direction, and is equipped with a differential pressure sensor to monitor its resistance. When the resistance exceeds a set threshold, an alarm is triggered. The water-side energy efficiency module 4 is used to monitor the flow rate, supply and return water temperature, and differential pressure of the water system. The air-side energy efficiency module 5 is used to monitor the supply air and mixed air temperature. The air-side energy efficiency module 5 communicates with the water-side energy efficiency module 4 to calculate the supply air volume based on the water system parameters and the air-side temperature difference, and to perform air-water load balance calculations.

[0066] A control method for a high-efficiency intelligent air handling unit is achieved through the following steps:

[0067] Forecast Phase (04:00 daily): The active forecasting module of edge computing gateway 6 is activated. The load forecasting model can employ various machine learning algorithms. In a preferred embodiment, a hybrid model combining a linear regression model and a gradient boosting tree model is used. The linear regression model serves as the baseline, providing high interpretability; the gradient boosting tree model is used to capture more complex nonlinear relationships and time-series features, thereby obtaining more accurate load forecasting results. This is merely a preferred embodiment of the invention and not a specific limitation on the load forecasting model. It reads operational data from the past few days, the daily weather forecast, and the building schedule, and uses its load forecasting model to predict the hourly heating and cooling loads for the day. Subsequently, it calculates the initial set of operating parameters that maximizes the expected energy efficiency: the initial value of the fan frequency f0, the initial value of the electric water valve opening θ0, and the initial value of the fresh air ratio α0.

[0068] Constraint Verification and Pre-Execution: The generated pre-adjustment parameters (f0, θ0, α0) are first verified by the core constraint module. The core constraints include safety protection constraints with the highest priority (e.g., setting an antifreeze temperature threshold of 5℃) and comfort assurance constraints with the second highest priority (e.g., maintaining the indoor temperature within the range of 18-26℃). Figure 2 As shown. For example, in winter, if the calculated f0, θ0, or α0 would cause the temperature of the antifreeze point to be lower than the antifreeze switch temperature setting (e.g., 5°C), it will be adjusted to a safe range. After verification, the parameters are sent to the execution control layer, and the device (e.g., Figure 1 (As shown) Start running with this setting.

[0069] Real-time monitoring and optimization phase (24-hour cycle):

[0070] Data acquisition: The sensor array continuously collects environmental parameters and equipment operating parameters;

[0071] Energy efficiency calculation: Edge computing gateway 6 acts as an energy efficiency optimization decision-maker, calculating the current power consumption per unit air volume φ and the energy efficiency ratio (EER) every 5 minutes, combined with... Figure 2 The control logic, through the loop of steps S5-S6, enables the system to respond to load changes within 5 minutes, while the traditional method takes 30 minutes. Dynamic optimization reduces the phenomenon of "over-adjustment / under-adjustment".

[0072] Optimization decision: Compare φ and EER with the expected values ​​at the current time step. Its built-in optimization strategy library contains explicit rules:

[0073] When EER is lower than the expected value while φ is within the normal range, the optimized water-side strategy is invoked.

[0074] When φ is higher than the expected value but EER is within the normal range, the optimized wind-side strategy is invoked.

[0075] When both φ and EER deviate from the expected values, the Feng Shui collaborative optimization strategy is invoked.

[0076] When both φ and EER reach the expected values, the fresh air optimization strategy is invoked.

[0077] The "normal range" can be determined based on the unit's rated performance parameters, statistical values ​​of historical operating data (such as average ± standard deviation), or dynamic thresholds set through system self-learning. For example, the normal range of power consumption φ per unit air volume can be set between 100% and 120% of the rated value.

[0078] For example, suppose that under the current operating conditions, the calculated EER value is 4.0, lower than the expected value of 4.5, while the φ value is normal. In this case, the energy efficiency optimization decision-maker will determine "EER low & φ normal," thus invoking the "optimize water side" strategy. This strategy may instruct the electric water valve actuator to gradually increase the opening from 50% to 52%, and observe the change in EER. The fresh air optimization strategy, when the energy efficiency indicators meet the standards, improves indoor air quality by adjusting the fresh air ratio or further saves energy by utilizing natural cooling.

[0079] Strategy Execution and Iteration: After fine-tuning the water valve, the system continues to monitor φ and EER for the next cycle. If EER increases to 4.3, the strategy is effective, and fine-tuning continues; if ineffective or causes abnormal φ, the "wind-water coordination" strategy may be triggered, simultaneously adjusting the fan frequency and water valve opening. Through this continuous, small-step, rapid optimization cycle, the device can quickly respond to various disturbances and always maintain stable operation in the high-efficiency zone.

[0080] Comparison table of traditional technology and the technology of this invention:

[0081] ;

[0082] ;

[0083] Summary of technical advantages: Through the above-mentioned systematic improvements, and through theoretical analysis and system simulation verification, this invention can achieve significant effects of improving the energy efficiency ratio (EER) and reducing the power consumption per unit air volume (φ) under typical operating conditions. At the same time, it solves the fundamental problems of traditional systems in terms of control lag, system coordination, and energy efficiency optimization.

[0084] In summary, the following beneficial effects are achieved by utilizing the above-described technical solution of the present invention:

[0085] Control mode innovation: Upgraded from traditional passive response to active predictive control, significantly reducing regulation lag and improving comfort and energy efficiency.

[0086] Systematic collaborative optimization: By adopting the "feng shui synergy" optimization strategy, the core problem of load disconnection in the feng shui subsystem was solved, and the overall energy efficiency at the system level was optimized.

[0087] It has self-sensing and self-optimization capabilities: the device can assess its own energy efficiency status in real time and make dynamic adjustments based on clear decision-making rules, possessing the basic capabilities of "AI operation and maintenance" to ensure long-term efficient operation.

[0088] Controllable resistance across the entire process: The low-resistance design and differential pressure monitoring of the dust removal module make the fan energy consumption a controllable variable, laying a solid foundation for wind-side optimization.

[0089] In a control experiment, the system using the control method of this invention and the traditional PID control system were operated under the same conditions for 24 hours. Data comparison showed that, while maintaining the same level of comfort, the system of this invention improved the energy efficiency ratio (EER) by about 15% and reduced the power consumption per unit air volume (φ) by about 10%, demonstrating a clear energy efficiency optimization effect.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart-controlled all-air handling device, comprising a body (1), characterized in that, Also includes: The edge computing gateway (6) is configured as an active prediction module, which is used to start the load prediction model at a preset time, and obtain external data through the communication module and output pre-adjustment parameters; it also includes a communication module (7) connected to the edge computing gateway (6) for data interaction with the cloud platform and external data sources; An execution control layer is connected to the edge computing gateway (6). The execution control layer includes a frequency converter, an electric water valve actuator, and an air valve actuator, which are used to adjust the fan speed, water flow rate, and fresh air ratio according to the pre-adjustment parameters and optimization instructions. Sensor arrays are used to monitor environmental parameters and equipment operating parameters in real time; The edge computing gateway (6) is also configured as an energy efficiency optimization decision-maker, which has a built-in optimization strategy library and is configured to periodically acquire the current energy efficiency index calculated from sensor array data, compare the current energy efficiency index with the expected energy efficiency index, and generate the optimization instruction by calling the corresponding strategy in the optimization strategy library according to the comparison result. The energy efficiency index includes power consumption φ per unit air volume and energy efficiency ratio (EER). The optimization strategy library includes at least: When EER is lower than the expected value while φ is within the normal range, the optimized water-side strategy is invoked. When φ is higher than the expected value but EER is within the normal range, the optimized wind-side strategy is invoked. When both φ and EER deviate from the expected values, the feng shui collaborative optimization strategy is invoked. When both φ and EER reach the expected values, the fresh air optimization strategy is invoked. The control logic of the edge computing gateway (6) includes core constraints, which include security protection constraints with the highest priority and comfort guarantee constraints with the second priority; the generation and execution of all pre-adjustment parameters and optimization instructions must not violate the core constraints. It also includes a water-side energy efficiency module (4) and a wind-side energy efficiency module (5). The water-side energy efficiency module (4) is used to monitor the flow rate, supply and return water temperature and pressure difference of the water system. The wind-side energy efficiency module (5) is used to monitor the supply air and mixing air temperature. The wind-side energy efficiency module (5) communicates with the water-side energy efficiency module (4) to calculate the supply air volume based on the water system parameters and the wind-side temperature difference, and to perform wind-water load balance calculation.

2. The intelligent control all-air treatment device according to claim 1, characterized in that, The pre-adjustment parameters include the initial value of the fan frequency f0, the initial value of the electric water valve opening θ0, and the initial value of the fresh air ratio α0.

3. The intelligent control all-air treatment device according to claim 1, characterized in that, The decision cycle of the energy efficiency optimization decision-maker can be set.

4. The intelligent control all-air treatment device according to claim 1, characterized in that, It also includes a dust removal and energy saving module (3), which includes a primary filter (31) and a low-resistance electrostatic precipitator (32) arranged along the airflow direction, and is equipped with a differential pressure sensor to monitor its resistance. When the resistance exceeds the set threshold, an alarm is triggered.

5. The intelligent control all-air treatment device according to claim 1, characterized in that, It also includes a strong and weak current integrated cabinet (2), which is electromechanically integrated with the body (1); the edge computing gateway (6), communication module (7), cloud energy efficiency platform (8), frequency converter (9), multi-function meter (10) and PLC main control screen are integrated in the strong and weak current integrated cabinet (2), and the strong and weak current integrated cabinet (2) is connected to the sensor array and the execution control layer.

6. The intelligent control all-air treatment device according to claim 1, characterized in that, The load forecasting model is activated at a preset time each day and performs load forecasting by integrating historical operating data, weather forecasts, and building schedule information.

7. A control method for an intelligent all-air handling device as described in any one of claims 1-6, characterized in that, Includes the following steps: S1: Active prediction: At the preset time point each day, the load prediction model is started by the edge computing gateway (6), which integrates historical operation data, weather forecast and building schedule information to generate pre-adjustment parameters; S2: Constraint verification to ensure that the pre-adjustment parameters meet the core constraints of safety protection and comfort assurance; S3: Pre-adjustment execution, which sends the verified pre-adjustment parameters to the execution control layer to perform initial settings for the device; S4: Data monitoring and energy efficiency calculation. Data is collected in real time through a sensor array. The real-time data from the sensor array triggers the edge computing gateway and calculates the current power consumption per unit air volume φ and the energy efficiency ratio (EER). The energy efficiency calculation cycle can be set. S5: Dynamic optimization decision-making, periodically compares the current φ and EER with the expected values, triggers the corresponding strategy in the optimization strategy library based on the comparison results, and generates optimization instructions; S6: Optimize execution, send optimization instructions to the execution control layer to dynamically fine-tune the device's operating parameters.

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