Air conditioner air cabinet control method, device and equipment for auxiliary material elevated warehouse and medium

Abstract

The invention discloses an air conditioner air cabinet control method, device and equipment for an auxiliary material elevated warehouse and a medium. The method comprises the steps that air cabinet equipment state parameters and elevated warehouse environment parameters corresponding to a target auxiliary material elevated warehouse are obtained; establishing a short-time heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse according to the elevated warehouse environment parameters, and determining environment prediction data based on the short-time heat and humidity load prediction curve; the environment prediction data and the air cabinet equipment state parameters are substituted into the target optimization model to determine air conditioner air cabinet control parameters; and performing group control on air cabinet equipment in the target auxiliary material elevated warehouse based on the air conditioner air cabinet control parameters. The short-time heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse is established, the control parameters are determined according to the prediction data and the air cabinet equipment state parameters, and group control is performed on the air conditioner air cabinets in the target auxiliary material elevated warehouse based on the control parameters, so that the control quality is improved; and a solid technical support is provided for energy conservation and consumption reduction of long-term operation of the elevated warehouse.

Description

Technical Field

[0001] This invention relates to the field of tobacco material storage technology, and in particular to a method, apparatus, equipment and medium for controlling air-conditioned air handling units in an auxiliary material elevated warehouse. Background Technology

[0002] High-bay storage for auxiliary materials is a crucial component of the cigarette processing system. Paper and film materials are highly sensitive to temperature and humidity during storage; even slight deviations in moisture content can cause deformation, moisture absorption, and adhesion, affecting the stability of subsequent processing steps. Therefore, most factories utilize large-space air conditioning systems, employing multiple air handling units to continuously supply air and maintain optimal temperature and humidity, ensuring the auxiliary materials remain in good condition within the storage area.

[0003] Currently, the environmental control of elevated warehouses largely follows the control methods of traditional HVAC systems. For example, each air handling unit operates independently, using its own outlet air temperature and humidity as the control target, and relying on PID control valves, fans, and humidifiers to maintain the setpoints. Traditional control methods lack coordination mechanisms between air handling units, and their control actions often cancel each other out: one side dehumidifies while the other humidifies, unnecessarily amplifying energy consumption. Secondly, elevated warehouses are large spaces with long air circulation paths; the PID control of a single air handling unit already has lag, making overshoot and repeated adjustments more likely, causing environmental fluctuations. Furthermore, the internal areas of elevated warehouses vary significantly; different levels and varying densities of auxiliary materials result in large differences in moisture load at different points. Summary of the Invention

[0004] This invention provides a method, device, equipment, and medium for controlling air conditioning units in an auxiliary material elevated warehouse. By establishing a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse, and determining control parameters based on the prediction data and the status parameters of the air handling unit equipment, the air conditioning units in the target auxiliary material elevated warehouse are then group-controlled based on the control parameters. This not only improves the control quality but also provides solid technical support for energy saving and consumption reduction in the long-term operation of the elevated warehouse.

[0005] According to one aspect of the present invention, a method for controlling the air handling unit of an auxiliary material elevated warehouse is provided, comprising:

[0006] Obtain the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse;

[0007] Based on the environmental parameters of the elevated warehouse, a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse is established, and environmental prediction data is determined based on the short-term heat and humidity load prediction curve.

[0008] Substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters.

[0009] The air handling unit equipment in the target auxiliary material elevated warehouse is controlled in groups based on the air handling unit control parameters.

[0010] According to another aspect of the present invention, an air conditioning unit control device for an auxiliary material elevated warehouse is provided, comprising:

[0011] The data acquisition module is used to acquire the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse;

[0012] The data prediction module is used to establish a short-time heat and humidity load prediction curve corresponding to the target auxiliary material high-rise warehouse based on the environmental parameters of the high-rise warehouse, and to determine environmental prediction data based on the short-time heat and humidity load prediction curve.

[0013] The parameter calculation module is used to substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters;

[0014] An air conditioning unit group control module is used to control the air handling unit equipment in the target auxiliary material high-rise warehouse based on the air conditioning unit control parameters.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the air conditioning unit control method for the auxiliary material high-bay warehouse according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the air conditioning fan control method for an auxiliary material high-bay warehouse according to any embodiment of the present invention.

[0020] The technical solution of this invention involves acquiring the status parameters of the air handling units (ALU) and the environmental parameters of the target ALU high-bay warehouse; establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse based on the environmental parameters, and determining environmental prediction data based on the short-term heat and humidity load prediction curve; substituting the environmental prediction data and the ALU status parameters into a target optimization model to determine the control parameters for the air handling units; and performing group control of the ALU equipment in the target ALU high-bay warehouse based on the air handling unit control parameters. Based on the above technical solution, by establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse, determining control parameters based on the prediction data and ALU status parameters, and then performing group control of the air handling units in the target ALU high-bay warehouse based on the control parameters, not only is the control quality improved, but a solid technical support is also provided for energy saving and consumption reduction in the long-term operation of the high-bay warehouse.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0023] Figure 1 This is a flowchart of an air conditioning unit control method for an auxiliary material elevated warehouse provided by an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the multi-point environmental data collection layout for elevated warehouses provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the MPC solver provided in an embodiment of the present invention;

[0026] Figure 4 This is a flowchart of an air conditioning unit control method for an auxiliary material elevated warehouse provided by an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the structure of an air conditioning unit control device for an auxiliary material elevated warehouse, provided in an embodiment of the present invention.

[0028] Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 This is a flowchart illustrating a method for controlling air conditioning units in an auxiliary material elevated warehouse, as provided in an embodiment of the present invention. This embodiment is applicable to situations where group control of air conditioning units in auxiliary material elevated warehouses is required. The method can be executed by an air conditioning unit control device for the auxiliary material elevated warehouse, which can be implemented in hardware and / or software and can be configured in an electronic device. For example... Figure 1 As shown, the method specifically includes the following steps:

[0032] S110. Obtain the status parameters of the air handling unit and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse.

[0033] The target auxiliary material high-bay warehouse can be an auxiliary material high-bay warehouse that requires air conditioning fan control. This warehouse can be used to store auxiliary materials for cigarette manufacturing. The fan control unit status parameters can be understood as the parameters of the air conditioning fan control unit during operation. The high-bay warehouse environmental parameters can be the environmental data of the target auxiliary material high-bay warehouse, which can be collected by sensors installed within the warehouse and can include historical and real-time environmental data.

[0034] Specifically, the system acquires the status parameters of the ventilation duct equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material. For example, through a sensor network deployed inside the ventilation duct equipment, such as temperature sensors, pressure transmitters, vibration sensors, and current transformers, it collects data including, but not limited to, fan speed, inlet and outlet air pressure difference, motor operating current, equipment vibration amplitude, and temperature of key components. It also records the equipment start-up and shutdown status and cumulative operating time. Furthermore, it deploys environmental sensors, such as temperature and humidity sensors, smoke detectors, dust concentration meters, and gas leak detection modules, in layers inside the high-bay warehouse. Based on the deployed environmental sensors, it collects the environmental parameters of the high-bay warehouse. It should be noted that the collected data can be preprocessed locally through an edge computing gateway, such as outlier filtering and data alignment, and then uploaded to the central monitoring platform to provide data support for subsequent equipment fault early warning and environmental safety control.

[0035] Based on the above technical solution, the step of obtaining the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse includes: collecting real-time high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse through environmental parameter sensors set at the data acquisition location of the target auxiliary material high-bay warehouse, and obtaining the status parameters of the air handling unit equipment in the target auxiliary material high-bay warehouse; obtaining historical high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse, and using the real-time high-bay warehouse environmental parameters and the historical high-bay warehouse environmental parameters as the high-bay warehouse environmental parameters.

[0036] The data acquisition locations can be pre-set sensor deployment locations for collecting environmental data. It should be noted that, to ensure the representativeness of the acquired environmental data, sensors can be deployed in different areas of the elevated warehouse to guarantee data representativeness. Figure 2 As shown, data collection locations can be upper and lower levels, wall-mounted aisles, or the central shelving area. Environmental parameters for the high-bay warehouse include indoor temperature T(i); indoor humidity RH(i); moisture content W(i); dew point Td(i); and outdoor temperature, humidity, and enthalpy H0. The status parameters of the air handling unit include start / stop status, airflow level, valve opening, fresh air ratio, and fan current.

[0037] Specifically, a multi-sensor system for environmental parameters, such as integrated temperature and humidity sensors, enthalpy calculation modules, and dew point temperature meters, is deployed in layers inside the elevated warehouse. Outdoor temperature and humidity sensors are installed outside the warehouse to collect outdoor environmental data. For the air handling units, status monitoring terminals are installed at the fan inlet and outlet, fresh air valves, and frequency converters to acquire real-time data on start / stop status, airflow level, valve opening, fresh air ratio, and fan current. It should be noted that the dew point temperature Td(i) represents the temperature at which condensation begins to appear when air is cooled to its lowest point under the current pressure without changing its moisture content; it is a key parameter reflecting the absolute moisture content of the air. Compared to relying solely on relative humidity RH(i), the dew point temperature does not change with fluctuations in the warehouse temperature, providing a more stable and accurate characterization of the moisture load level and condensation risk within the warehouse. The dew point parameter Td(i) is used for: condensation risk assessment. When the temperature of the goods surface or building envelope is close to or lower than Td(i), the system can identify potential condensation risks in advance and prioritize dehumidification measures such as reducing moisture content and adjusting the fresh air ratio to prevent auxiliary materials from getting damp and sticking together; the wet load prediction input Td(i), together with W(i), RH(i), etc., is used as the input of the short-term wet load prediction model to characterize the humidity change trend and improve the accuracy of load prediction.

[0038] The technical solution of this invention combines parameters such as outdoor enthalpy HO and indoor humidity W(i), and uses Td(i) to perform enthalpy and humidity analysis on the air state inside and outside the warehouse, providing a more reliable decision-making basis for setting the fresh air ratio of the air handling unit, selecting cooling / dehumidification conditions, and optimizing group control.

[0039] S120. Establish a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse based on the environmental parameters of the elevated warehouse, and determine environmental prediction data based on the short-term heat and humidity load prediction curve.

[0040] The short-term heat and humidity load prediction curve is used to predict the trend of heat and humidity load changes within the target elevated warehouse in the near future. Environmental prediction data can be obtained from the short-term heat and humidity load prediction curve and matched with a preset period. It should be noted that the prediction period can be set according to requirements, for example, it can be 10 minutes.

[0041] Specifically, feature variables are extracted from the environmental parameters of the elevated warehouse, including indoor and outdoor temperature and humidity, moisture content, enthalpy value and the operating status of the air handling unit. Based on the above variables, a dynamic linear model, recursive least squares or shallow neural network is used to establish a dynamic prediction model of heat and humidity load. Based on the prediction curve, the key environmental parameters are predicted to generate an environmental prediction dataset.

[0042] Based on the above technical solution, the step of establishing a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse according to the elevated warehouse environmental parameters includes: extracting historical environmental data from the elevated warehouse environmental parameters based on a preset data extraction period; establishing a prediction state vector based on the historical environmental data; and establishing the short-term heat and humidity load prediction curve based on the prediction state vector.

[0043] The preset data extraction period can be a pre-set data extraction period, such as 10 minutes. The predicted state vector can be understood as a state vector used to describe the environmental characteristics of the target elevated warehouse at a certain moment. The historical environmental data includes at least two of the following: indoor temperature, humidity, dew point temperature, outdoor temperature and humidity, enthalpy value, and disturbances caused by entering and leaving the warehouse.

[0044] Specifically, based on a preset data extraction cycle, historical environmental data is synchronously collected from the environmental parameter database of the elevated warehouse. The historical data is used to construct a predictive state vector according to the time window. The vector elements include the indoor and outdoor temperature and humidity difference, moisture content gradient, enthalpy change rate and disturbance amount at the current moment. The state vector is trained using the support vector regression (SVR) algorithm to generate a short-term heat and humidity load prediction curve for the next 30 minutes. The model parameters are updated in real time using the sliding window method.

[0045] For example, based on data from the past H periods (e.g., the past 60 minutes), a prediction curve for the next L periods is generated using a dynamic linear model, recursive least squares, or a shallow neural network. The prediction output includes: a future temperature prediction. Moisture load prediction Dew point trend Determining the prediction curve includes:

[0046] Data preprocessing and state vector construction: At the current time k, the prediction module extracts the following quantities from the historical data of the past H periods: indoor temperature, humidity, and dew point temperature. Outdoor temperature, humidity, and enthalpy: Inbound / outbound disturbance: This is used to characterize the number or volume of pallets / goods entering and exiting within the current period; related air handling unit operating status: air volume, fresh air ratio, etc. After denoising, outlier removal, and normalization, the current input / state vector is constructed. ,like: ;

[0047] Model structure and parameter identification: The prediction curve can be determined using a dynamic linear model and recursive least squares methods. ;

[0048] in, This represents the parameter vector for the wet load and temperature prediction model. The parameter vector is updated online using recursive least squares (RLS):

[0049] ;

[0050] in, This is the actual measured output. For example, the actual measured moisture content or temperature... Let covariance matrix be the variance matrix. This is the forgetting factor. Through the above recursive updates, the predictive model can automatically adapt to seasonal changes, inventory status, and operating conditions.

[0051] The prediction curve can also be achieved using a shallow feedforward neural network, to... The network takes input and obtains the corresponding predicted output through offline pre-training and online fine-tuning. This allows for the fitting of nonlinear relationships.

[0052] Multi-step rolling prediction generates prediction curves: After obtaining the model parameters at the current time step, the prediction curve is generated based on the current measured state. Starting from this point, predictive values ​​for the next L periods are generated using a rolling method:

[0053] Based on the above formula, we get ;Will Treating this as virtual historical data for the next step, and constructing a new state vector together with the predicted external disturbances. ,calculate External disturbances can be such as The estimated value.

[0054] After iterating L times in this manner, we obtain: This represents the predicted curve for the next L periods. Wherein, This represents an estimate of the disturbance to the wet load caused by inbound and outbound operations. It can be defined, for example, as the number of pallets entering and leaving the warehouse, the number of times a pallet enters or leaves the warehouse, or a representative weight value within each sampling period, used to characterize the additional wet load introduced by inbound and outbound operations. (Prediction) It can take 10-20 minutes.

[0055] S130. Substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters.

[0056] Among them, the air conditioning unit control parameters can be control data used to control the air conditioning units in the target elevated warehouse, which may include the unit number, start / stop status, air volume level, and fresh air ratio.

[0057] Specifically, environmental prediction data is spatiotemporally aligned with real-time air handling unit equipment status parameters to construct a multi-dimensional input vector. This vector is then substituted into a pre-trained target optimization model to output the optimal set of control parameters, including fresh air ratio adjustment values, fan frequency commands, and valve opening commands. These parameters are then sent to the air handling unit actuators in real time via PLC to achieve a dynamic balance between environmental stability and equipment energy efficiency.

[0058] Based on the above technical solution, before substituting the environmental prediction data and the air handling unit equipment status parameters into the target optimization model to determine the air handling unit control parameters, the method further includes: determining the target optimization function and target constraints corresponding to the target auxiliary material high-rise warehouse, and establishing the target optimization model based on the target optimization function and the target constraints.

[0059] The objective optimization function includes environmental deviation costs, energy consumption costs, and equipment start-up and shutdown costs; the objective constraints include environmental constraints, equipment capacity constraints, and safety constraints. The objective optimization function can be a pre-set objective function used to solve for the control parameters of the air handling unit. The objective constraints can be pre-set constraints.

[0060] Specifically, the target optimization function and target constraints corresponding to the target auxiliary material high-bay warehouse are determined, and the target optimization model is established based on the target optimization function and the target constraints. For example, the target function J is constructed using the MPC concept, including environmental deviation cost: , where l is the prediction step index, the l-th prediction time; L: prediction time domain length, i.e. the number of steps to predict forward (e.g., 10-20 steps, corresponding to 10-20 minutes). : The predicted temperature inside the reservoir at the (l)th prediction time; The predicted value of moisture content / moisture load in the reservoir at the (l)th prediction time; Set the temperature (target temperature) for the high-bay storage environment. : Moisture content / humidity setting value for the high-bay warehouse storage environment (target moisture content or relative humidity after conversion); It is used to measure the sum of squares of the deviations of temperature and humidity from the set targets over the entire prediction time domain. The larger the deviation, the better. The larger.

[0061] Energy consumption costs (fan energy consumption and surface cooler load): Where k is the energy consumption calculation or control cycle index; N is the number of cycles considered during optimization (which can be the same as (L) or set separately as needed). The instantaneous or average power of the fan in the k-th cycle (which can be the sum of the power of all fan units), in units such as kW; The cooling load of the surface cooler (or cold source) in the (k)th cycle can be expressed as cooling capacity or equivalent power. This is the energy consumption weight or conversion factor for wind turbines, used to convert wind turbine power into a uniform cost dimension. It is a weighting factor or conversion factor for cooling energy consumption, used to convert the cooling load into a uniform cost dimension; Reflected in the combined energy consumption cost of the fan and surface cooler in the prediction / optimization time domain, by adjusting This can reflect the relative importance of "fan power consumption" and "cooling consumption" in the total cost.

[0062] Equipment start-up and shutdown costs (to suppress frequent handover): ;in, The start-stop penalty weighting coefficient is used to control the importance of "avoiding frequent start-stop". The device state variable for the kth cycle can be: the on / off state of a single air handling unit (0: off, 1: on), or a scalar representation of the state vectors of multiple air handling units synthesized in some way; This is the absolute value of the state change from cycle k-1 to cycle k. This value is greater than 0 when a start-stop or gear shift occurs. Used to depict the costs associated with starting, stopping, and frequently switching the air handling unit; the more frequent the state changes, the higher the cost. The larger the value, the more likely it is to automatically reduce unnecessary starts and stops during optimization.

[0063] Regional equilibrium constraint: Controlling ΔT and ΔW to not exceed the upper-level set limits. ΔT is an indicator of the temperature difference between different areas of the warehouse (such as upper and lower floors, different aisles), for example, the average temperature difference between upper and lower floors. Or the maximum temperature difference across all measuring points; ΔW is an indicator of the difference in moisture content between different areas of the warehouse, such as the average moisture content difference between upper and lower layers. Or the maximum difference in moisture content at all measuring points; the upper-level set limit (such as...) (This refers to the maximum allowable temperature and humidity differences preset by management personnel in the host computer, used to ensure the spatial uniformity of the environment within the warehouse.) The constraints can be described as follows: ;

[0064] The overall optimization goal is: ; To optimize the overall objective function and measure the combined "cost" of environmental deviations, energy consumption, and equipment start-up and shutdown costs within the prediction time domain, the goal of the swarm control solver is to minimize (J) under constraints. Environmental deviation costs; Energy consumption costs (fan energy consumption and surface cooler load); Equipment start-up and shutdown costs (suppressing frequent handover).

[0065] In the above technical solution, model predictive control (MPC) is used to solve for the control parameters, such as... Figure 3 As shown, within each control cycle, the optimal start-up / shutdown combination and operating settings of the air handling units are automatically solved by integrating forecast information and constraints. Specifically, the control cycle is set to Δt (e.g., 5 minutes), and the forecast time domain length is N cycles. The following inputs are obtained in each control cycle t: Forecast disturbances and loads include: outdoor weather conditions (temperature, humidity, enthalpy, etc.) for the next N steps, sensible / latent heat load forecasts for each area for the next N steps, personnel numbers or CO2 production forecasts, and electricity price or demand constraints (e.g., time-of-use pricing, demand penalties); Current system status includes: current temperature and humidity, CO2 concentration, current start-up / shutdown status of each air handling unit, airflow level, valve opening, and humidifier power for each area; System constraint parameters include: Area comfort boundary: temperature. relative humidity CO2 upper limit C_max; Equipment capacity: nominal air volume of each air handling unit, air volume ratio of each setting (e.g., 40%, 60%, 80%), maximum cooling capacity of the surface cooler, maximum humidification capacity of the humidifier; Fresh air and IAQ constraints: fresh air ratio range of each air handling unit. The minimum total fresh air volume of the system; safety and operational constraints: minimum start-up and shutdown time of the air handling unit, maximum air volume variation rate, valve / humidifier adjustment speed limit, etc.

[0066] Therefore, within each control cycle, the MPC solver uses the following variables as decision variables: fan start / stop combination: binary variable : Represents the start / stop status of the i-th air handling unit at each future time (1 for running, 0 for stopped); Airflow level: Discrete variable : Represents the airflow level of the i-th air handling unit. The airflow level can also be represented as or modeled using 0 / 1 variables, for example and constraints This ensures that there are no gears when the machine is stopped and only one gear is used when the machine is running.

[0067] Fresh air ratio setting: a continuous variable : Represents the fresh air ratio of the i-th air handling unit; Cooler and humidifier settings: continuous variables : Cooler valve opening; continuous variable : Humidifier output power or duty cycle.

[0068] The above variables then collectively form the control sequence. This refers to "which air handling units are operating and their respective parameter settings".

[0069] The MPC solver constructs a multi-objective weighted function to comprehensively minimize energy consumption and the number of operation switching while satisfying comfort and safety constraints. The objective function takes the following form: ;

[0070] in: The deviations of room temperature, humidity, and CO2 from their respective target values; The comprehensive energy consumption model at time k (fan power + cooling capacity + humidification power consumption, etc.); This represents the number of times the air handling unit starts, stops, and switches gears within that cycle, used to penalize frequent switching. It is an adjustable weight used to balance comfort, energy consumption, and equipment lifespan.

[0071] Constraining the state of each region within the prediction time domain: Temperature constraint: Humidity constraint: CO2 constraint: ;

[0072] Load and air volume balance constraints: Total supply air volume meets predicted cooling / heating load requirements. The zoned air supply volume meets the required air volume for each zone and can be further subdivided according to the opening degree of the air valve or the air volume ratio.

[0073] Fresh air volume constraint: Total fresh air volume must meet the lower limit of IAQ. The fresh air ratio of each air handling unit should be maintained within the allowable range. ;

[0074] Equipment capacity and safety constraints: The output of the surface cooler and humidifier shall not exceed the capacity: Control the rate of change of the quantity to avoid excessively rapid adjustment. ;

[0075] Start / Stop Logic and Minimum Running Time Constraints: Minimum Start / Stop Time: Through Constraints The system remains unchanged over several cycles, ensuring that "it runs for at least M_on cycles after being turned on and stops for at least M_off cycles after being turned off"; unreasonable combinations (such as certain air handling units only being able to operate in pairs) are prohibited, which is achieved through logical constraints. or ;

[0076] Then, in each control period t, the following steps are performed: Based on the latest measurements and model, the load, indoor conditions, and external disturbances for the next N steps are updated and predicted. Based on the current state, prediction results, and constraints, a mixed-integer optimization problem (MIQP / MILP, etc.) is formed, with the objective function and constraints as described above. The optimization problem is input into the MPC solver to obtain the optimal control sequence for the next N steps. Only the control inputs from the first moment (current cycle) are extracted as the actual execution instructions, including: selecting the set of air handling units to operate. Air volume settings for each blower unit (e.g., 40%, 60%, 80%); Fresh air ratio for each air handling unit ; Cooler valve opening Humidifier power wait.

[0077] At the next control cycle t+1, a new state is collected, and steps 1–4 are repeated to achieve rolling optimization and closed-loop control. When the MPC solution fails or the computation time exceeds the limit, the system automatically switches to a preset degradation strategy (such as rule-based air handling unit combination and PID setting) to ensure safe system operation.

[0078] It should be noted that, based on the above scheme, a hierarchical solution can be adopted according to the air handling unit combination and settings. For example, a two-layer solution structure can be used: For the upper-layer solution: the air handling unit combination screening first prioritizes the air handling units based on efficiency and capacity; under the premise of satisfying "total air volume ≥ minimum air volume required for predicted load", a finite number of candidate start-stop combinations are enumerated; for each candidate combination, only continuous variables (air volume level, fresh air ratio, valve opening, etc.) are optimized, and the corresponding objective function value is calculated; the combination with the smallest objective value is selected as the final set of operating air handling units. For the lower-layer solution: after fixing the air handling unit combination, MPC is used to finely optimize the air volume level, fresh air ratio, surface cooler valve, and humidifier output to further improve energy saving and comfort performance.

[0079] S140. Based on the control parameters of the air conditioning unit, perform group control on the air handling unit equipment in the target auxiliary material high-rise warehouse.

[0080] Specifically, the generated control parameters, such as fresh air ratio, fan frequency, and valve opening, are sent to the controllers of each air handling unit in real time. A master-slave group control architecture is adopted, selecting one master air handling unit as the coordination node. Based on the temperature and humidity field distribution in the warehouse, its operating parameters are dynamically adjusted. The other slave air handling units follow the parameters of the master air handling unit as a benchmark and are proportionally controlled according to the regional heat and humidity load differences (e.g., the frequency of the air handling unit in the east zone = the frequency of the master air handling unit × 0.95).

[0081] Based on the above technical solution, after group control of the air handling unit equipment in the target auxiliary material high-bay warehouse based on the air handling unit control parameters, the method further includes: determining predicted temperature data based on the air handling unit control parameters, and obtaining the actual environmental parameters of the target auxiliary material high-bay warehouse after executing the air handling unit control parameters; determining a control deviation value based on the actual environmental parameters and the predicted temperature data; and adjusting the weight coefficients in the target optimization model based on the control deviation value if the control deviation value is greater than a preset deviation threshold.

[0082] The predicted temperature data can be the temperature data obtained through forecasting. The actual environmental parameters can be the environmental parameters collected by environmental sensors installed in the auxiliary material elevated warehouse. The control deviation value can be the difference between the predicted temperature data and the actual temperature data. The preset deviation threshold is a pre-set threshold used to determine whether the weight values ​​need to be adjusted.

[0083] Specifically, the predicted temperature value obtained through the prediction curve is acquired, and the actual environmental parameters are collected through a distributed temperature and humidity sensor network deployed in the library. The difference between the predicted temperature value and the actual environmental parameters is calculated. If the difference is greater than a preset deviation threshold, the weight coefficients in the target optimization model are adjusted based on the control deviation value.

[0084] For example, after the control command is executed, the actual temperature and humidity response is automatically monitored, and the prediction deviation is calculated. ,like: Where t: the sequence number of the current control cycle (current time); t+1: the time of the next sampling / control cycle after the current control cycle has completed the execution of the control command; The actual temperature value measured by the temperature sensor inside the warehouse at time t+1; The predicted temperature value for time t+1 is given by the prediction module at time t. The temperature prediction error at time t+1 is equal to the difference between the measured temperature and the predicted temperature, and is the prediction bias. Its specific form in the temperature dimension; The term "prediction bias" is a general term that can refer to both the aforementioned temperature prediction error and other related factors. This can also be extended to the prediction errors of other environmental quantities such as humidity and moisture content, used to guide the online correction of prediction model parameters (such as a1 and a2). If the deviation exceeds the limit, the prediction model will automatically update parameters a1, a2, etc., to make the prediction in the next cycle more accurate, achieving self-learning and self-optimization.

[0085] The technical solution of this invention involves acquiring the status parameters of the air handling units (ALU) and the environmental parameters of the target ALU high-bay warehouse; establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse based on the environmental parameters, and determining environmental prediction data based on the short-term heat and humidity load prediction curve; substituting the environmental prediction data and the ALU status parameters into a target optimization model to determine the control parameters for the air handling units; and performing group control of the ALU equipment in the target ALU high-bay warehouse based on the air handling unit control parameters. Based on the above technical solution, by establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse, determining control parameters based on the prediction data and ALU status parameters, and then performing group control of the air handling units in the target ALU high-bay warehouse based on the control parameters, not only is the control quality improved, but a solid technical support is also provided for energy saving and consumption reduction in the long-term operation of the high-bay warehouse.

[0086] In one possible implementation of the present invention Figure 4 A flowchart of an air conditioning unit control method for an auxiliary material elevated warehouse provided by an embodiment of the present invention is shown below. Figure 4As shown, this embodiment, after obtaining the air handling unit equipment status parameters and high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse, also includes the following steps:

[0087] S410. Based on the environmental parameters of the elevated warehouse, establish a regional balance index corresponding to the target auxiliary material elevated warehouse.

[0088] Among them, the regional balance index is an indicator used to determine whether the environment within the target elevated warehouse is balanced.

[0089] Specifically, the elevated warehouse can be divided into multiple monitoring zones based on the storage characteristics of the goods. Environmental sensors can be deployed in each zone to collect real-time data on indoor temperature, relative humidity, and airflow velocity. The temperature and humidity deviation index of each zone can then be calculated, and the corresponding zone uniformity index for the target auxiliary material elevated warehouse can be determined based on the temperature and humidity deviation index.

[0090] Based on the above technical solution, the step of establishing a regional equilibrium index corresponding to the target auxiliary material elevated warehouse based on the elevated warehouse environmental parameters includes: extracting real-time elevated warehouse environmental parameters from the elevated warehouse environmental parameters, and performing spatial modeling to determine the environmental distribution field based on the real-time elevated warehouse environmental parameters; determining upper-level and lower-level measuring points corresponding to the target auxiliary material elevated warehouse, calculating the average temperature difference and average moisture content difference between the upper-level and lower-level measuring points based on the environmental distribution field, and using the average temperature difference and average moisture content difference as the regional equilibrium index.

[0091] The environmental distribution field is a mathematical model describing the continuous distribution characteristics of environmental parameters within the target auxiliary material elevated warehouse in three-dimensional space. This environmental distribution field includes a temperature field, a humidity field, and an open-air temperature field. Upper-level measuring points can be data acquisition points located at the upper levels of the target auxiliary material elevated warehouse; correspondingly, upper-level measuring points can be data acquisition points located at the ground level of the target auxiliary material elevated warehouse.

[0092] Specifically, real-time elevated warehouse environmental parameters are extracted from the elevated warehouse environmental parameters, and spatial modeling is performed based on the real-time elevated warehouse environmental parameters to determine the environmental distribution field. Upper and lower measuring points corresponding to the target auxiliary material elevated warehouse are determined, and the average temperature difference and average moisture content difference between the upper and lower measuring points are calculated based on the environmental distribution field. The average temperature difference and average moisture content difference are used as the regional uniformity index.

[0093] For example, based on multi-point data collection, the environmental distribution field of the warehouse is constructed through interpolation or a small spatial model to obtain the temperature field. Moisture content field Dew point temperature field These parameters are used to characterize the environmental non-uniformity of a warehouse between upper and lower levels, different aisles, and shelving areas. The upper-level measurement point set is defined as follows: The lower-level measurement point set is The corresponding number of measuring points are as follows: Calculate the average temperature and average moisture content of the upper and lower layers: ;

[0094] The interlayer average difference index is obtained by calculating the average temperature and average moisture content of the upper and lower layers: ;in, For the first Indoor temperature measurements at each measuring point; For the first Indoor humidity at each measuring point (mass of water vapor contained in a unit mass of dry air); This is a set of measuring points located in the upper area of ​​the elevated warehouse. This is a set of measuring points located in the lower level area of ​​the elevated warehouse. The number of measurement points in the upper layer, i.e. Number of measurement points in the middle; The number of measurement points in the lower layer, i.e. Number of measurement points in the middle; This represents the average temperature of the upper and lower layers. This represents the average moisture content of the upper and lower layers. The average temperature difference between the upper and lower layers is used to measure the temperature uniformity between layers. The difference in average moisture content between upper and lower layers is used to measure the uniformity of humidity between layers. The thresholds for temperature difference and humidity difference are used to trigger an increase in regional weight or a penalty when the difference exceeds the threshold. The weighting coefficients assigned to the environmental deviation terms of the upper and lower layers in the optimization objective function are used to reflect the control priority of each region.

[0095] S420. Determine the deviation area of ​​the target auxiliary material high-rise warehouse according to the regional balance index, and adjust the weight value of the deviation area in the target optimization model.

[0096] The deviation region can be a region whose regional balance is greater than a preset balance threshold, and it can be an upper region or a lower region.

[0097] For example, if or Exceeding the set threshold (e.g.) If the current environment distribution is uneven, the weight of the relevant region in the environmental deviation term will be increased in the subsequent optimization solution. For example, weights will be set for the upper and lower layers respectively. Regions with larger deviations are given higher weights; a regional balance penalty term is added to the objective function. The penalty for excessively large situations is to prioritize the activation of air handling units closer to the deviation area when selecting air handling unit combinations, so that the area can be restored to the target state more quickly.

[0098] It should be noted that the environmental distribution field is not only used to describe the current spatial state of the warehouse, but also directly participates in the construction of the subsequent optimization objective function and the decision-making of the air handling unit group control, so that the group control strategy can simultaneously take into account the overall average environmental indicators and regional balance, and reduce the temperature and humidity differences between upper and lower floors and different channels.

[0099] The technical solution of this invention constructs a complete collaborative control system around "load forecasting + group control optimization". The four stages of forecasting, optimization, scheduling, and feedback are closely linked, enabling the air conditioning system to proactively anticipate environmental changes and adjust strategies in advance, rather than relying on experience-based judgment or single-point deviations for remediation. This results in the elevated warehouse air conditioning system exhibiting performance in environmental stability, energy consumption, and equipment reliability that is difficult to achieve with traditional control methods.

[0100] Figure 5 This is a schematic diagram of the structure of an air conditioning unit control device for an auxiliary material elevated warehouse, provided as an embodiment of the present invention. Figure 5 As shown, the device includes: a data acquisition module 510, a data prediction module 520, a parameter calculation module 530, and an air conditioning unit group control module 540.

[0101] Data acquisition module 510 is used to acquire the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse;

[0102] The data prediction module 520 is used to establish a short-time heat and humidity load prediction curve corresponding to the target auxiliary material high-rise warehouse based on the environmental parameters of the high-rise warehouse, and to determine environmental prediction data based on the short-time heat and humidity load prediction curve.

[0103] Parameter calculation module 530 is used to substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters;

[0104] The air conditioning unit group control module 540 is used to perform group control of the air conditioning unit equipment in the target auxiliary material high-rise warehouse based on the air conditioning unit control parameters.

[0105] Based on the above technical solution, the data acquisition module is used to acquire the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse, and then establish a regional balance index corresponding to the target auxiliary material high-bay warehouse based on the environmental parameters of the high-bay warehouse; determine the deviation region of the target auxiliary material high-bay warehouse according to the regional balance index, and adjust the weight value of the deviation region in the target optimization model.

[0106] Based on the above technical solution, the data acquisition module is used to extract real-time elevated warehouse environmental parameters from the elevated warehouse environmental parameters, and to perform spatial modeling to determine the environmental distribution field based on the real-time elevated warehouse environmental parameters. The environmental distribution field includes a temperature field, a humidity field, and an open-air temperature field. The module also determines upper and lower measuring points corresponding to the target auxiliary material elevated warehouse, calculates the average temperature difference and average humidity difference between the upper and lower measuring points based on the environmental distribution field, and uses the average temperature difference and average humidity difference as the regional uniformity index.

[0107] Based on the above technical solution, the data prediction module is used to extract historical environmental data from the environmental parameters of the elevated warehouse based on a preset data extraction cycle. The historical environmental data includes at least two of the following: indoor temperature, humidity, dew point temperature, outdoor temperature and humidity, enthalpy value, and disturbance amount of entering and leaving the warehouse. A prediction state vector is established based on the historical environmental data, and the short-term heat and humidity load prediction curve is established based on the prediction state vector.

[0108] Based on the above technical solution, the device further includes: a feedback adjustment module, used to determine predicted temperature data based on the air conditioning fan control parameters after controlling the fan equipment in the target auxiliary material high-bay warehouse based on the air conditioning fan control parameters, and to obtain the actual environmental parameters of the target auxiliary material high-bay warehouse after executing the air conditioning fan control parameters; to determine a control deviation value based on the actual environmental parameters and the predicted temperature data; and to adjust the weight coefficients in the target optimization model based on the control deviation value if the control deviation value is greater than a preset deviation threshold.

[0109] Based on the above technical solution, the parameter calculation module is used to determine the target optimization function and target constraints corresponding to the target auxiliary material high-rise warehouse, and to establish the target optimization model according to the target optimization function and the target constraints; wherein, the target optimization function includes environmental deviation cost, energy consumption cost and equipment start-up and shutdown cost; the target constraints include environmental constraints, equipment capacity constraints and safety constraints.

[0110] Based on the above technical solution, the data acquisition module is used to acquire real-time high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse through environmental parameter sensors set at the data acquisition location of the target auxiliary material high-bay warehouse, and to acquire the air handling unit status parameters of the air handling unit in the target auxiliary material high-bay warehouse; acquire historical high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse, and use the real-time high-bay warehouse environmental parameters and the historical high-bay warehouse environmental parameters as the high-bay warehouse environmental parameters; wherein, the high-bay warehouse environmental parameters include indoor temperature, relative humidity, moisture content, dew point temperature, outdoor temperature and humidity, and enthalpy value; the air handling unit status parameters include start / stop status, air volume level, valve opening, fresh air ratio, and fan current.

[0111] The technical solution of this invention involves acquiring the status parameters of the air handling units (ALU) and the environmental parameters of the target ALU high-bay warehouse; establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse based on the environmental parameters, and determining environmental prediction data based on the short-term heat and humidity load prediction curve; substituting the environmental prediction data and the ALU status parameters into a target optimization model to determine the control parameters for the air handling units; and performing group control of the ALU equipment in the target ALU high-bay warehouse based on the air handling unit control parameters. Based on the above technical solution, by establishing a short-term heat and humidity load prediction curve corresponding to the target ALU high-bay warehouse, determining control parameters based on the prediction data and ALU status parameters, and then performing group control of the air handling units in the target ALU high-bay warehouse based on the control parameters, not only is the control quality improved, but a solid technical support is also provided for energy saving and consumption reduction in the long-term operation of the high-bay warehouse.

[0112] The air conditioning unit control device for the auxiliary material high-bay warehouse provided in this embodiment of the invention can execute the air conditioning unit control method for the auxiliary material high-bay warehouse provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0113] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0114] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0115] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the air handling unit control method for an auxiliary material high-bay warehouse.

[0117] In some embodiments, the air conditioning unit control method for an auxiliary material high-bay warehouse can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the air conditioning unit control method for an auxiliary material high-bay warehouse described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the air conditioning unit control method for an auxiliary material high-bay warehouse by any other suitable means (e.g., by means of firmware).

[0118] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0121] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0122] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0123] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0124] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling the air handling unit of an auxiliary material elevated warehouse, characterized in that, include: Obtain the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse; Based on the environmental parameters of the elevated warehouse, a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse is established, and environmental prediction data is determined based on the short-term heat and humidity load prediction curve. Substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters. The air handling unit equipment in the target auxiliary material elevated warehouse is controlled in groups based on the air handling unit control parameters.

2. The method according to claim 1, characterized in that, After obtaining the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material warehouse, including: Based on the environmental parameters of the elevated warehouse, establish a regional balance index corresponding to the target auxiliary material elevated warehouse; The deviation region of the target auxiliary material high-rise warehouse is determined based on the regional balance index, and the weight value of the deviation region in the target optimization model is adjusted.

3. The method according to claim 2, characterized in that, The establishment of a regional equilibrium index corresponding to the target auxiliary material elevated warehouse based on the elevated warehouse environmental parameters includes: Real-time environmental parameters of the elevated warehouse are extracted from the environmental parameters of the elevated warehouse, and spatial modeling is performed based on the real-time environmental parameters of the elevated warehouse to determine the environmental distribution field, wherein the environmental distribution field includes temperature field, humidity field, and open-air temperature field; Determine the upper and lower measuring points corresponding to the target auxiliary material elevated warehouse, calculate the average temperature difference and average moisture content difference between the upper and lower measuring points based on the environmental distribution field, and use the average temperature difference and average moisture content difference as the regional uniformity index.

4. The method according to claim 1, characterized in that, The step of establishing a short-term heat and humidity load prediction curve corresponding to the target auxiliary material elevated warehouse based on the environmental parameters of the elevated warehouse includes: Historical environmental data is extracted from the environmental parameters of the elevated warehouse based on a preset data extraction cycle. The historical environmental data includes at least two of the following: indoor temperature, humidity, dew point temperature, outdoor temperature and humidity, enthalpy value, and disturbance amount when entering and leaving the warehouse. A predicted state vector is established based on the historical environmental data, and a short-term heat and humidity load prediction curve is established based on the predicted state vector.

5. The method according to claim 1, characterized in that, After group control of the air handling unit equipment in the target auxiliary material high-bay warehouse based on the air handling unit control parameters, the following steps are also included: Based on the air conditioning unit control parameters, the predicted temperature data is determined, and the actual environmental parameters of the target auxiliary material high-rise warehouse are obtained after the air conditioning unit control parameters are executed. The control deviation value is determined based on the actual environmental parameters and the predicted temperature data; If the control deviation value is greater than a preset deviation threshold, the weight coefficients in the target optimization model are adjusted based on the control deviation value.

6. The method according to claim 1, characterized in that, Before substituting the environmental prediction data and the air handling unit equipment status parameters into the target optimization model to determine the air handling unit control parameters, the process also includes: Determine the target optimization function and target constraints corresponding to the target auxiliary material high-rise warehouse, and establish the target optimization model based on the target optimization function and the target constraints; The objective optimization function includes environmental deviation cost, energy consumption cost, and equipment start-up and shutdown cost; the objective constraints include environmental constraints, equipment capability constraints, and safety constraints.

7. The method according to claim 1, characterized in that, The acquisition of the air handling unit equipment status parameters and high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse includes: The environmental parameter sensors set at the data acquisition location of the target auxiliary material high-bay warehouse collect real-time high-bay warehouse environmental parameters corresponding to the target auxiliary material high-bay warehouse, and obtain the air handling unit status parameters of the air handling unit in the target auxiliary material high-bay warehouse. Obtain the historical high-bay warehouse environment parameters corresponding to the target auxiliary material high-bay warehouse, and use the real-time high-bay warehouse environment parameters and the historical high-bay warehouse environment parameters as the high-bay warehouse environment parameters; The environmental parameters of the elevated warehouse include indoor temperature, relative humidity, moisture content, dew point temperature, outdoor temperature and humidity, and enthalpy; the status parameters of the air handling unit include start / stop status, air volume level, valve opening, fresh air ratio, and fan current.

8. An air conditioning unit control device for an auxiliary material elevated warehouse, characterized in that, include: The data acquisition module is used to acquire the status parameters of the air handling unit equipment and the environmental parameters of the high-bay warehouse corresponding to the target auxiliary material high-bay warehouse; The data prediction module is used to establish a short-time heat and humidity load prediction curve corresponding to the target auxiliary material high-rise warehouse based on the environmental parameters of the high-rise warehouse, and to determine environmental prediction data based on the short-time heat and humidity load prediction curve. The parameter calculation module is used to substitute the environmental prediction data and the air handling unit status parameters into the target optimization model to determine the air handling unit control parameters; An air conditioning unit group control module is used to control the air handling unit equipment in the target auxiliary material high-rise warehouse based on the air conditioning unit control parameters.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the air conditioning unit control method for the auxiliary material high-bay warehouse according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the air conditioning unit control method for the auxiliary material high-bay warehouse according to any one of claims 1-7.

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