Intelligent optimization control method of heating and ventilation system based on water freezing system and dehumidifier

Abstract

The invention discloses an intelligent optimization control method of a heating and ventilation system based on a water freezing system and a dehumidifier, and relates to the technical field of heating and ventilation control. The method comprises the steps of determining a water supply temperature interval and a water supply flow interval of a cold water host; according to the to-be-optimized chilled water outlet temperature, the cooling tower outlet water temperature, the tail end demand cooling capacity, the chilled water temperature difference, the cooling water temperature difference, the to-be-optimized chilled water flow, the water pump power, the cooling tower power, the operation time, the electricity price, the steam unit price, the to-be-optimized regeneration temperature of at least one rotary wheel dehumidifier, the to-be-optimized rotary wheel rotating speed and the to-be-optimized treatment air volume, the to-be-optimized rotary wheel rotating speed of the at least one rotary wheel dehumidifier is optimized. Constructing a target function; under the constraint condition, the target function is solved based on the interval multi-target particle swarm algorithm by taking the minimum target function as the target, and target values corresponding to the to-be-optimized frozen water outlet temperature, the to-be-optimized frozen water flow, the to-be-optimized regeneration temperature, the to-be-optimized rotating wheel rotating speed and the to-be-optimized processing air volume are obtained; and controlling the heating and ventilation system by using the target value. The control efficiency of the heating and ventilation system can be improved.

Description

Technical Field

[0001] This invention relates to the field of HVAC control technology, and in particular to an intelligent optimization control method for HVAC systems based on chilled water systems and dehumidifiers. Background Technology

[0002] In industries with stringent requirements for temperature and humidity precision, HVAC systems are core infrastructure ensuring stable production processes. Their core function is to achieve precise temperature and humidity control within the workshop through the coordinated operation of the chilled water subsystem and dehumidification unit. While existing HVAC systems possess some automation capabilities, significant shortcomings remain in the coordinated implementation of precise temperature and humidity control with energy-saving optimization, failing to achieve system-level energy balance. Therefore, current technologies lack suitable HVAC system control solutions, failing to address the issue of timely, precise, and comprehensive control of HVAC systems to improve energy efficiency and reduce overall costs while fully meeting user needs. Summary of the Invention

[0003] This invention provides an intelligent optimization control method for HVAC systems based on chilled water systems and dehumidifiers to solve the problem of low control efficiency in HVAC systems.

[0004] According to one aspect of the present invention, an intelligent optimization control method for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier is provided. The method is applied to the HVAC system, which includes a chilled water system and at least one rotary dehumidifier. The rotary dehumidifier is equipped with a front-mounted cooling coil. The chilled water system includes a chiller, a water pump, and a cooling tower. The method includes:

[0005] Based on the dehumidification correlation model, the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized are determined for the chiller.

[0006] Based on the chilled water outlet temperature to be optimized, cooling tower outlet temperature, terminal cooling capacity, chilled water temperature difference, cooling water temperature difference, chilled water flow rate to be optimized, water pump power, cooling tower power, operating time, electricity price, steam unit price, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized, an objective function is constructed.

[0007] Constraints are constructed based on the range of values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the air volume to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of at least one rotary dehumidifier.

[0008] Under the constraints, with the objective function as the minimum, the objective function is solved based on the interval multi-objective particle swarm algorithm to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the air volume to be optimized, and the regeneration temperature, rotor speed and air volume to be optimized of at least one rotary dehumidifier.

[0009] The target value is used to control the HVAC system.

[0010] According to another aspect of the present invention, an intelligent optimization control device for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier is provided. The device is configured in the HVAC system, which includes a chilled water system and at least one rotary dehumidifier, the rotary dehumidifier being equipped with a front cooling coil. The device includes:

[0011] The interval determination module is used to determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller based on the dehumidification correlation model.

[0012] The function construction module is used to construct an objective function based on the chilled water outlet temperature to be optimized, chilled water flow rate to be optimized, air volume to be optimized, water pump power, cooling tower power, running time, electricity price, steam unit price, and the regeneration temperature, rotor speed and air volume to be optimized of the at least one rotary dehumidifier.

[0013] The constraint construction module is used to construct constraint conditions based on the value ranges corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of the at least one rotary dehumidifier.

[0014] The target value determination module is used to solve the objective function based on the interval multi-objective particle swarm algorithm under the constraints, with the objective function as the minimum, to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of the at least one rotary dehumidifier to be optimized.

[0015] The system control module is used to control the HVAC system using the target value.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable 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 execute the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier as described in any embodiment of the present invention.

[0018] 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 intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, comprising a computer program / instructions, which, when executed by a processor, implement the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier as described in any embodiment of the present invention.

[0020] This invention uses a dehumidification correlation model to determine the water supply temperature and flow rate ranges, ensuring the matching of the front cooling coil dehumidification capacity with the dehumidification wheel dehumidification capacity, and avoiding excessive workshop temperature and humidity due to insufficient dehumidification. Simultaneously, constraints limit the range of each parameter value, preventing the system from operating under overload conditions and reducing the risk of failures such as front cooling coil frosting, fan overload, and wheel aging. No manual adjustment of subsystem parameters is required; the optimal control parameter set is automatically solved using a multi-objective particle swarm optimization algorithm, achieving closed-loop operation of condition perception, model calculation, parameter optimization, and control execution. The effective combination of the chilled water system and the dehumidifier can meet the stringent temperature and humidity requirements at the terminal, minimizing operating costs while satisfying workshop temperature and humidity constraints. It is adaptable to complex industrial scenarios and has broad engineering application value.

[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 1This is a flowchart of an intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier, provided by an embodiment of the present invention.

[0024] Figure 2 This is a flowchart of another intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier, provided by an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of an intelligent optimization control device for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier, provided in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier, according to an embodiment of the present invention. Detailed Implementation

[0027] 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.

[0028] 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 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.

[0029] Furthermore, it should be noted that the information collected in the technical solution of this invention is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data all comply with the relevant laws, regulations and standards of relevant countries and regions, necessary confidentiality measures have been taken, and public order and good morals are not violated. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0030] Figure 1This is a flowchart illustrating an intelligent optimization control method for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier, provided by an embodiment of the present invention. The method is applied to an HVAC system, which includes a chilled water system and at least one rotary dehumidifier. The rotary dehumidifier is equipped with a front-mounted cooling coil. The chilled water system includes a chiller, a water pump, and a cooling tower. This embodiment is applicable to situations requiring HVAC system control. The method can be executed by an intelligent optimization control device for an HVAC system based on a chilled water system and a dehumidifier. This device can be implemented in hardware and / or software and can be configured in an electronic device with corresponding data processing capabilities, such as a server within the HVAC system. Figure 1 As shown, the method includes:

[0031] S110. Based on the dehumidification correlation model, determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller.

[0032] The HVAC system refers to a comprehensive system with heating, ventilation, and air conditioning functions. An HVAC system includes a chilled water system and at least one rotary dehumidifier, which is equipped with front-mounted cooling coils, etc. The chilled water system includes a chiller, water pump, and cooling tower. The chilled water outlet temperature is the temperature of the chilled water provided by the chilled water system. The chilled water flow rate is the flow rate of chilled water transported from the chilled water system to the rotary dehumidifier in the HVAC system. The chilled water outlet temperature to be optimized is the chilled water outlet temperature that needs to be optimized. The chilled water flow rate to be optimized is the chilled water flow rate that needs to be optimized. The supply water temperature range is the range of values ​​for the chilled water outlet temperature. The supply water flow rate range is the range of values ​​for the chilled water flow rate. The dehumidification correlation model is a data-driven mathematical model that can be used to quantify the mapping relationship between key indicators such as chilled water outlet temperature and dehumidification capacity.

[0033] Specifically, by using a dehumidification correlation model, the appropriate temperature range corresponding to the chilled water outlet temperature of the chiller unit to be optimized is found, i.e., the water supply temperature range, which ensures that the dehumidification effect meets the standard, and also enables the system to save energy and operate stably.

[0034] S120. Construct an objective function based on the chilled water outlet temperature to be optimized, chilled water flow rate to be optimized, water pump power, cooling tower power, running time, electricity price, steam unit price, and the regeneration temperature, rotor speed, and processing air volume of at least one rotary dehumidifier to be optimized.

[0035] Among them, processing air volume is the airflow generated by the HVAC system. Processing air volume is the flow rate of air carrying heat and moisture exchange with chilled water; the larger the air volume, the more air is processed per unit time, and the wider the dehumidification or cooling coverage, but the higher the fan energy consumption. Regeneration temperature is the heating temperature set during the regeneration process (i.e., the process of restoring dehumidification capacity) of the rotary dehumidifier in the HVAC system. That is, the regeneration temperature is the temperature of the heated air in the regeneration section of the rotary dehumidifier. Parameters to be optimized refer to parameters that need to be adjusted in conjunction with other parameters to achieve the overall goal (such as minimizing operating costs). Chilled water flow rate to be optimized is the chilled water flow rate that needs to be optimized. Rotor speed is the rotational speed of the rotary dehumidifier's rotor during operation. Rotor speed to be optimized is the rotor speed that needs to be optimized. Processing air volume to be optimized is the processing air volume that needs to be optimized. Regeneration temperature to be optimized is the regeneration temperature that needs to be optimized. Operating time is the continuous operating time of the HVAC system under specific operating conditions. The objective function is a mathematical expression constructed based on the system optimization objective, which achieves the optimal function value by quantifying the mapping relationship between variables and the optimization objective. The objective function is used to achieve a preset optimization goal.

[0036] Specifically, key adjustable parameters affecting the dehumidification effect, energy consumption, and operational stability of the HVAC system (chilled water temperature of the chiller, chilled water flow rate of the chiller, regeneration temperature of the rotary dehumidifier, rotor speed of the rotary dehumidifier, and processing air volume of the rotary dehumidifier) ​​are integrated with cooling tower outlet water temperature, terminal cooling demand, chilled water temperature difference, cooling water temperature difference, water pump power, cooling tower power, operating time, electricity price, and steam unit price to establish an objective function. This function can directly reflect the relationship between the parameter combination and the optimization objective, making it easier to find the optimal parameter combination in the future.

[0037] S130. Constraints are constructed based on the range of values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized for at least one rotary dehumidifier.

[0038] Among them, the constraints are the boundary rules of the objective function.

[0039] Specifically, by limiting the legal value range of the parameters to be optimized, it is ensured that the optimization result (parameter combination) meets the requirements of dehumidification effect, safe equipment operation, reasonable energy consumption, and compliance with industry standards, avoiding situations where the parameters are optimal but practically infeasible (such as condensation on the coil due to excessively low chilled water temperature, or equipment burnout due to excessively high regeneration temperature). For example, a set of constraints can be constructed based on the equipment safety threshold, performance requirements, and industry standards of the parameters to be optimized. The set of constraints includes: chilled water outlet temperature to be optimized, chilled water flow rate to be optimized, chilled water outlet temperature to be optimized meeting a first value range, chilled water flow rate to be optimized meeting a second value range, regeneration temperature to be optimized for at least one rotary dehumidifier meeting a third value range, rotary dehumidifier speed to be optimized for at least one rotary dehumidifier meeting a fourth value range, and air volume to be optimized for at least one rotary dehumidifier meeting a fifth value range. The first, second, third, fourth, and fifth value ranges can be set based on actual conditions.

[0040] S140. Under the constraints, with the objective function as the minimum, the objective function is solved based on the interval multi-objective particle swarm algorithm to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized.

[0041] Among them, the interval multi-objective particle swarm optimization (PSO) algorithm is an optimization algorithm. The core logic of the interval multi-objective PSO algorithm is: simulating the foraging behavior of bird flocks, treating each combination of parameters to be optimized as a particle, and finding the set of non-dominated solutions (i.e., the optimal solution) that simultaneously satisfies all constraints and makes multiple objective functions optimal (e.g., minimized) through particle position updates within the parameter interval. The objective value is the specific value of the parameters to be optimized that satisfies all constraints and makes the objective function optimal (i.e., minimized) after being solved by the interval multi-objective PSO algorithm. The objective value is a parameter setting value that can be directly used in the actual operation of the HVAC system.

[0042] Specifically, given that the chilled water outlet temperature, chilled water flow rate, regeneration temperature of at least one rotary dehumidifier, rotor speed of at least one rotary dehumidifier, and processing air volume of at least one rotary dehumidifier meet the corresponding constraints, the interval multi-objective particle swarm optimization algorithm is used to find a set of specific parameter values ​​that minimize the total system cost. This set of values ​​represents the target values ​​for the chilled water outlet temperature, chilled water flow rate, regeneration temperature of at least one rotary dehumidifier, rotor speed of at least one rotary dehumidifier, and processing air volume of at least one rotary dehumidifier. These target values ​​can then be directly used to control the HVAC system.

[0043] S150. The HVAC system is controlled using target values.

[0044] Specifically, the target value obtained by solving the interval multi-objective particle swarm optimization algorithm will be used as the operation control parameter of the HVAC system and input into the system controller to allow the system to operate automatically according to the target value, ultimately achieving the goals of minimum energy consumption, dehumidification compliance, and equipment safety.

[0045] Optionally, based on the dehumidification correlation model, the supply water temperature range for the chilled water outlet temperature to be optimized of the chiller is determined, including: for each rotary dehumidifier, using the dehumidification correlation model, based on the current flow rate of the front cooling coil and the target dehumidification capacity, the minimum supply water temperature and supply water flow rate of the rotary dehumidifier are determined; based on the minimum supply water temperature and supply water flow rate of at least one rotary dehumidifier, the supply water temperature range for the chilled water outlet temperature to be optimized and the supply water flow rate range for the chiller flow rate to be optimized are determined.

[0046] Rotary dehumidifiers are core equipment in HVAC systems for deep dehumidification. They can be used in series with front cooling coils (the front cooling coils pre-cool and dehumidify, then the rotary dehumidifier performs deep dehumidification). The front cooling coil is a heat exchange device located upstream of the rotary dehumidifier ("front" refers to the front end in the airflow direction). It contains chilled water, and through heat and moisture exchange between the air and the chilled water, it achieves pre-cooling and preliminary dehumidification of the air. Current flow rate refers to the chilled water flow rate through the front cooling coil under current operating conditions. Target dehumidification capacity is the dehumidification capacity required under specific operating conditions. The minimum supply water temperature of the rotary dehumidifier is the minimum chilled water supply temperature required for a single rotary dehumidifier, provided that the current flow rate of the front cooling coil is fixed and the target dehumidification capacity of the dehumidifier is met.

[0047] Specifically, for each rotary dehumidifier in the HVAC system, a dehumidification correlation model is first used to calculate the minimum chilled water temperature required to complete the task. This temperature is calculated based on the current chilled water flow rate of the upstream cooling coils and the dehumidification task the dehumidifier needs to perform. If the temperature is lower than this, the dehumidification capacity will exceed the demand, potentially leading to overcooling and requiring reheating, thus increasing energy consumption. Then, combined with the minimum supply water temperature for all rotary dehumidifiers, the adjustable range of the chilled water outlet temperature for the chiller is determined (ensuring all dehumidifiers achieve the dehumidification target without wasting energy). This is the supply water temperature range. For example, when determining the chiller's chilled water outlet temperature based on the supply water temperature range, it can be set to the minimum of the minimum supply water temperatures for all rotary dehumidifiers. If the minimum supply water temperature for a dehumidifier is very low, then the chilled water outlet temperature must also be very low, which may lead to low efficiency of the refrigeration unit. Therefore, after determining the specific chilled water outlet temperature within the supply water temperature range, the flow rate is then adjusted to meet the needs of each dehumidifier.

[0048] Optionally, the objective function can be expressed as:

[0049] ;

[0050] in, The total operating cost of the HVAC system, For the operating costs of the chilled water system, The total operating cost of the dehumidifier, The operating cost of the i-th dehumidifier.

[0051] Optionally, the formula for the operating cost of the chilled water system can be expressed as:

[0052] ;

[0053] in, For the operating costs of the chilled water system, For the power of the chilled water system, For electricity price, This refers to the runtime.

[0054] The formula for calculating the power of the chilled water system is as follows:

[0055] ;

[0056] in, For the power of the chilled water system, This refers to the power of the chiller unit. For water pump power, The power rating is for the cooling tower. The power rating is for the water pump's operating power. The power rating is for the cooling tower's operating power.

[0057] Optionally, the power of the chiller unit can be expressed as a function formula:

[0058] ;

[0059] in, It refers to the power of the chiller unit. It is the outlet water temperature of the cooling tower. It is the chilled water outlet temperature of the chiller. It is the end-user demand for cooling. It's the temperature difference of the chilled water. It's the cooling water temperature difference. This refers to the chilled water flow rate of the chiller. The cooling tower outlet temperature is the temperature of the cooling water returning to the chiller after being cooled by the cooling tower. Terminal cooling demand is the amount of heat that needs to be removed from the terminal equipment per hour to maintain the set temperature. Terminal cooling demand is essentially the workload of the chiller. Terminal equipment refers to equipment that needs to maintain a set temperature (such as air conditioning in industrial plants). Chilled water temperature difference is the difference between the chilled water outlet temperature and the chilled water inlet temperature. The chilled water outlet temperature is the temperature of the chilled water flowing from the chiller to the terminal equipment. The chilled water inlet temperature is the temperature of the chilled water flowing from the terminal equipment back to the chiller. Cooling water temperature difference is the difference between the cooling water supply temperature and the cooling water return temperature. The cooling water supply temperature is the temperature of the chilled water flowing from the chiller to the cooling tower. The cooling water return temperature is the temperature of the chilled water flowing from the cooling tower back to the chiller.

[0060] Optionally, the function formula for the pump power can be expressed as:

[0061] ;

[0062] in, For water pump power, The number of water pumps to be turned on. For the frequency of the chilled water pump, The frequency of the cooling-side water pump is specified. The pump power is the operating power of the water pump. The number of pumps in operation is the number of pumps that are currently running. The chilled-side water pump is responsible for driving the circulation of chilled water between the chiller and terminal devices (such as air conditioners). The chilled-side water pump frequency is the electrical signal frequency of the chilled-side water pump's motor speed. The cooling-side water pump is responsible for driving the circulation of cooling water between the chiller and the cooling tower. The cooling-side water pump frequency is the electrical signal frequency of the cooling-side water pump's motor speed.

[0063] Optionally, the functional formula for cooling tower power can be expressed as:

[0064] ;

[0065] in, For cooling tower power, The number of cooling towers to be turned on. This refers to the cooling tower frequency. Cooling tower power is the operating power of the cooling tower. Number of cooling towers in operation is the number of cooling towers that are currently running. Cooling tower frequency is the frequency of the cooling tower fan motor.

[0066] Optionally, the formula for dehumidifier operating costs can be expressed as:

[0067] ;

[0068] in, The operating cost of the i-th dehumidifier, Let be the steam consumption of the i-th dehumidifier. This is the unit price of steam. This refers to the runtime.

[0069] Optionally, the formula for the steam consumption of a dehumidifier can be expressed as:

[0070] ;

[0071] in, Let be the steam consumption of the i-th dehumidifier. Let be the regeneration temperature of the i-th dehumidifier. Let be the rotor speed of the i-th dehumidifier. Let be the processing air volume of the i-th dehumidifier.

[0072] This invention uses a dehumidification correlation model to determine the water supply temperature range, ensuring the matching of the front cooling coil dehumidification capacity and the dehumidification capacity of the dehumidifier, thus avoiding excessive workshop temperature and humidity due to insufficient dehumidification. Simultaneously, it limits the range of parameters through constraints, preventing the system from operating under overload conditions and reducing the risk of failures such as front cooling coil frosting, fan overload, and dehumidifier aging. No manual adjustment of subsystem parameters is required; the optimal control parameter set is automatically solved using a multi-objective particle swarm optimization algorithm, achieving closed-loop operation of condition perception, model calculation, parameter optimization, and control execution. The effective combination of the chilled water system and the dehumidifier can meet the stringent temperature and humidity requirements at the terminal, minimizing operating costs while satisfying workshop temperature and humidity constraints. It is adaptable to complex industrial scenarios and has broad engineering application value.

[0073] Figure 2This is a flowchart of another intelligent optimization control method for an HVAC system based on a chilled water system and a dehumidifier, provided by an embodiment of the present invention. Based on the above embodiments, this embodiment optimizes the process of "constructing an objective function based on the chilled water outlet temperature, cooling tower outlet temperature, terminal cooling demand, chilled water temperature difference, cooling water temperature difference, chilled water flow rate, pump power, cooling tower power, operating time, electricity price, steam unit price, and the regeneration temperature, rotor speed, and processing air volume of at least one rotary dehumidifier," providing an optional implementation scheme. For example... Figure 2 As shown, the method includes:

[0074] S210. Based on the dehumidification correlation model, determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller.

[0075] S220. Based on the chilled water outlet temperature to be optimized, cooling tower outlet temperature, terminal cooling demand, chilled water temperature difference, cooling water temperature difference, chilled water flow rate to be optimized, water pump power, cooling tower power, running time, electricity price, steam unit price, and the regeneration temperature, rotor speed and air volume to be optimized of at least one rotary dehumidifier to be optimized, construct the objective function.

[0076] S230. Constraints are constructed based on the range of values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized for at least one rotary dehumidifier.

[0077] S240. Under the constraints, with the objective function as the minimum, the objective function is solved based on the interval multi-objective particle swarm algorithm to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized.

[0078] S250, Use target values ​​to control the HVAC system.

[0079] Optionally, based on the chilled water outlet temperature to be optimized, cooling tower outlet temperature, terminal cooling demand, chilled water temperature difference, cooling water temperature difference, chilled water flow rate to be optimized, operating time, and the regeneration temperature, rotor speed, and processing air volume of at least one rotary dehumidifier to be optimized, an objective function is constructed, including: determining the chiller power based on the chilled water outlet temperature to be optimized, cooling tower outlet temperature, terminal cooling demand, chilled water temperature difference, cooling water temperature difference, and chilled water flow rate to be optimized; determining the chilled water system power based on the chiller power, pump power, and cooling tower power; determining the chilled water system operating cost based on the chilled water system power, operating time, and electricity price; determining the total dehumidifier operating cost based on the operating time, steam unit price, and the regeneration temperature, rotor speed, and processing air volume of at least one rotary dehumidifier to be optimized; and constructing an objective function based on the chilled water system operating cost and the total dehumidifier operating cost.

[0080] Here, chiller power refers to the operating power of the chiller in the chilled water system. Electricity price is the cost per unit of electricity. Steam price is the cost per unit of steam. Chilled water system power refers to the operating power of the chilled water system. Chilled water system operating cost is the total cost of electricity consumed by the chilled water system within a set operating time. Dehumidifier total operating cost is the total cost of electricity consumed by at least one rotary dehumidifier within a set operating time (mainly for regenerative heating and rotary drive motor).

[0081] Specifically, the real-time power consumption of the chiller is calculated. This power is related to the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling demand, the chilled water temperature difference, the cooling water temperature difference, and the chilled water flow rate to be optimized. Based on the chiller power, pump power, and cooling tower power, the chilled water system power is obtained. Then, based on the chilled water system power, operating time, and electricity price, the total electricity cost of the chiller is calculated. The total cost of the rotary dehumidifier is calculated. The total cost of the rotary dehumidifier is related to the dehumidifier's regeneration temperature to be optimized, the dehumidifier's rotor speed to be optimized, the dehumidifier's air volume to be optimized, the steam unit price, and the operating time. These two costs are added together to construct a mathematical formula (i.e., the objective function) that minimizes the total cost. The subsequent step is to use an algorithm to find the parameter combination that minimizes the total cost. By accurately linking parameters and modularizing cost breakdown, it achieves precise mapping between energy consumption and cost, making optimization objectives more targeted, facilitating system-level energy consumption balance, and providing a clear objective function structure. This reduces the computational complexity of interval multi-objective particle swarm optimization algorithms, improves the speed and stability of finding the optimal solution, and is suitable for real-time control in industrial scenarios.

[0082] Optionally, the total operating cost of the dehumidifier is determined based on the operating time, steam unit price, and the regeneration temperature, rotor speed, and air volume to be optimized for at least one rotary dehumidifier. This includes: for each rotary dehumidifier, determining the steam consumption of that dehumidifier based on its regeneration temperature, rotor speed, and air volume to be optimized; determining the dehumidifier operating cost of that dehumidifier based on its steam consumption, operating time, and steam unit price; and determining the total operating cost of the dehumidifier based on the operating cost of at least one rotary dehumidifier.

[0083] Optionally, the dehumidification correlation model is trained as follows: Sample parameter data is determined; the sample parameter data includes historical operating data and label dehumidification data; the historical operating data is input into a neural network to obtain predicted dehumidification data; the neural network includes an input layer, an output layer, and at least one fully connected layer; the training loss is determined based on the predicted dehumidification data and label dehumidification data; the neural network is iteratively trained using the training loss to obtain the dehumidification correlation model.

[0084] The sample parameter data consists of the sample data used to train the dehumidification correlation model. Historical operating data comprises the feature data within the sample parameter data. This refers to key operating parameter data related to dehumidification performance recorded during the past operation of the HVAC system (such as chilled water outlet temperature, chilled water flow rate, processing air volume, threshold valve opening, inlet air temperature and humidity, etc.), which is collected and stored in real-time through sensors and the control system. Tag dehumidification data comprises the tag data within the sample parameter data. This tag dehumidification data refers to the actual dehumidification performance data achieved by the HVAC system (such as actual dehumidification volume), corresponding to the historical operating data, and is used to measure the accuracy of the model's predictions. It is obtained through direct sensor measurement or precise calculation based on historical operating data. Predicted dehumidification data is the dehumidification performance prediction value output by the model after inputting historical operating data into the neural network to be trained. Training loss is a mathematical metric used to quantify the difference between the predicted dehumidification data and the tag dehumidification data.

[0085] Specifically, sample parameter data is acquired, including historical operating data and label dehumidification data. Historical operating data (such as chilled water outlet temperature, chilled water flow rate, processing air volume, regeneration temperature, etc.) is input into a neural network to be trained, allowing the model to predict dehumidification effects and obtain predicted dehumidification data. The training loss for the predicted dehumidification data and the label dehumidification data is determined, and the weights and biases of the neural network are adjusted based on backpropagation of the training loss. The model is iteratively trained until the training loss converges to a preset threshold, resulting in a dehumidification correlation model. During the training process of the above model, the sample parameter data is comprehensive, the model fits more closely to actual operating conditions, the neural network structure is simple and efficient, balancing accuracy and computational speed, and the model output directly serves system optimization, making it highly practical.

[0086] Optionally, the training loss can be determined based on the predicted dehumidification data and the labeled dehumidification data, including: determining the data difference based on the predicted dehumidification data and the labeled dehumidification data; and using the squared average of the data difference as the training loss.

[0087] The data difference is the difference between the predicted dehumidification data and the label dehumidification data.

[0088] Specifically, for each set of training data, the predicted dehumidification data is subtracted from the labeled dehumidification data to obtain the data difference for a single set. The square of each data difference is calculated, and the sum of all squared differences is divided by the number of data sets. The result is the training loss. The smaller the training loss, the closer the model prediction is to the actual value.

[0089] Optionally, it may also include: collecting raw running data during historical operation; performing preprocessing operations on the raw running data to obtain historical running data; the preprocessing operations include outlier handling, missing value handling, data alignment, and data normalization.

[0090] The raw operating data consists of unprocessed parameter data directly collected from sensors and controllers during the historical operation of the HVAC system. Preprocessing involves a series of standardized processes to remove noise, fill gaps, standardize data formats, and eliminate dimensional bias, ensuring high accuracy and generalization ability of the subsequently trained dehumidification correlation model. Outlier handling identifies and processes abnormal data (such as sudden changes caused by equipment malfunctions) that deviate from the normal range, preventing interference with model training and ensuring data authenticity. Missing value handling identifies and fills in missing data to prevent incomplete training samples. Data alignment synchronizes raw operating data from different sources and with different collection frequencies using a unified timestamp, ensuring consistency in the temporal dimension of each data set. Data normalization refers to mapping parameter data, after handling outliers and missing values ​​and data alignment, to a unified numerical range (such as [0,1] or [-1,1]), eliminating the influence of differences in the units and ranges of different parameters, balancing the weights of each parameter during model training, and improving training efficiency and prediction accuracy.

[0091] Specifically, outlier handling can utilize methods such as box plots to detect and process outliers. Missing value handling can employ interpolation or time-series-based imputation methods (such as linear interpolation and time-series prediction imputation). Data alignment unifies data to the same timestamps (e.g., 5-minute intervals) for time alignment. This improves data quality, removes outliers, fills in missing values, and avoids model prediction bias caused by low-quality data; it better adapts to model training, ensuring correct parameter correspondences, and normalization eliminates the influence of units, improving model training efficiency and generalization ability; it ensures model stability, as standardized data formats make the model's prediction results more stable under different operating conditions, avoiding model overfitting caused by data noise.

[0092] This invention employs a dehumidification correlation model, setting the current flow rate of the front-side cooling coil and the target dehumidification capacity, to calculate the minimum water supply temperature and flow rate required to meet dehumidification needs. It then aggregates the minimum water supply temperatures and flow rates of all dehumidifiers to form system-level chilled water supply temperature and flow rate ranges. Using historical operating data and labeled dehumidification data as samples, a basic neural network structure of "input layer, fully connected layer, and output layer" is adopted. The model is iteratively optimized through training loss analysis of predicted dehumidification data and labeled dehumidification data to ultimately generate the dehumidification correlation model. Mean squared error is used as the training loss to quantify the model's prediction accuracy. The original operating data undergoes four preprocessing steps: outlier handling, missing value handling, data alignment, and normalization, to ensure the quality of the historical operating data input into the model.

[0093] Figure 3 This is a schematic diagram of an intelligent optimization control device for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier, provided by an embodiment of the present invention. This embodiment is applicable to situations involving the control of HVAC systems. The device can be implemented in hardware and / or software and can be configured in electronic devices with corresponding data processing capabilities, such as a server within the HVAC system. The HVAC system includes a chilled water system and at least one rotary dehumidifier. The rotary dehumidifier is equipped with a front-mounted cooling coil. The chilled water system includes a chiller, a water pump, and a cooling tower. Figure 3 As shown, the device includes:

[0094] The interval determination module 310 is used to determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller based on the dehumidification correlation model.

[0095] The function construction module 320 is used to construct an objective function based on the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling demand, the chilled water temperature difference, the cooling water temperature difference, the chilled water flow rate to be optimized, the water pump power, the cooling tower power, the running time, the electricity price, the steam unit price, and the regeneration temperature, the rotor speed and the air volume to be optimized of at least one rotary dehumidifier to be optimized.

[0096] The constraint construction module 330 is used to construct constraints based on the value ranges corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of at least one rotary dehumidifier.

[0097] The target value determination module 340 is used to solve the objective function based on the interval multi-objective particle swarm algorithm under the constraint condition, with the objective function as the minimum objective function. The objective function is obtained by obtaining the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized.

[0098] The system control module 350 is used to control the HVAC system using target values.

[0099] This invention uses a dehumidification correlation model to determine the water supply temperature range, ensuring the matching of the front cooling coil dehumidification capacity and the dehumidification capacity of the dehumidifier, thus avoiding excessive workshop temperature and humidity due to insufficient dehumidification. Simultaneously, it limits the range of parameters through constraints, preventing the system from operating under overload conditions and reducing the risk of failures such as front cooling coil frosting, fan overload, and dehumidifier aging. No manual adjustment of subsystem parameters is required; the optimal control parameter set is automatically solved using a multi-objective particle swarm optimization algorithm, achieving closed-loop operation of condition perception, model calculation, parameter optimization, and control execution. The effective combination of the chilled water system and the dehumidifier can meet the stringent temperature and humidity requirements at the terminal, minimizing operating costs while satisfying workshop temperature and humidity constraints. It is adaptable to complex industrial scenarios and has broad engineering application value.

[0100] Optional, function building module 320 includes:

[0101] The chiller power determination unit is used to determine the chiller power based on the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling demand, the chilled water temperature difference, the cooling water temperature difference, and the chilled water flow rate to be optimized.

[0102] The chilled water system power determination unit is used to determine the chilled water system power based on the chiller power, water pump power, and cooling tower power.

[0103] The first cost determination unit is used to determine the operating cost of the chilled water system based on the power, operating time and electricity price of the chilled water system.

[0104] The second cost determination unit is used to determine the total operating cost of the dehumidifier based on the operating time, the unit price of steam, and the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of at least one rotary dehumidifier.

[0105] The function determination unit is used to construct the objective function based on the operating cost of the chilled water system and the total operating cost of the dehumidifier.

[0106] Optionally, the interval determination module 310 includes:

[0107] The temperature and flow rate determination unit is used to determine the minimum water supply temperature and water supply flow rate of each rotary dehumidifier using a dehumidification association model based on the current flow rate of the front cooling coil and the target dehumidification capacity.

[0108] The interval determination unit is used to determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller, based on the minimum supply water temperature and supply flow rate of at least one rotary dehumidifier.

[0109] Optionally, the device may also include: a dehumidification correlation model training module;

[0110] The dehumidification correlation model training module includes:

[0111] The sample data determination unit is used to determine sample parameter data; the sample parameter data includes historical operating data and label dehumidification data.

[0112] The predictive data acquisition unit is used to input historical operating data into a neural network to obtain predictive dehumidification data; the neural network includes an input layer, an output layer, and at least one fully connected layer;

[0113] The training loss determination unit is used to determine the training loss based on the predicted dehumidification data and the labeled dehumidification data.

[0114] The model acquisition unit is used to iteratively train the neural network using training loss to obtain the dehumidification correlation model.

[0115] Optionally, the second cost determination unit includes:

[0116] The steam consumption determination subunit is used to determine the steam consumption of each rotary dehumidifier based on the dehumidifier's regeneration temperature to be optimized, the dehumidifier's rotor speed to be optimized, and the dehumidifier's processing air volume to be optimized.

[0117] The dehumidifier operating cost subunit is used to determine the dehumidifier operating cost based on steam consumption, operating time, and steam unit price.

[0118] The total operating cost subunit for dehumidifiers is used to determine the total operating cost of dehumidifiers based on the operating cost of at least one rotary dehumidifier.

[0119] Optionally, the apparatus may also include: a data preprocessing module;

[0120] The data preprocessing module includes:

[0121] The raw operation data acquisition unit is used to collect raw operation data during historical operation.

[0122] The data preprocessing unit is used to preprocess the raw running data to obtain historical running data; the preprocessing operations include outlier handling, missing value handling, data alignment, and data normalization.

[0123] The intelligent optimization control device for HVAC systems based on chilled water systems and dehumidifiers provided in this embodiment of the invention can execute the intelligent optimization control method for HVAC systems based on chilled water systems and dehumidifiers provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0124] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0125] Figure 4 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.

[0126] like Figure 4 As 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 from storage unit 18 into the RAM 13. 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.

[0127] 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.

[0128] 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, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as intelligent optimization control methods for HVAC systems based on chilled water systems and dehumidifiers.

[0129] In some embodiments, the intelligent optimization control method for a chilled water system and dehumidifier-based HVAC system 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 intelligent optimization control method for a chilled water system and dehumidifier-based HVAC system described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the intelligent optimization control method for a chilled water system and dehumidifier-based HVAC system by any other suitable means (e.g., by means of firmware).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include 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.

[0135] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via 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 host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0136] 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 no limitation is imposed herein.

[0137] 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 smart optimization control method for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier, characterized in that, The method is applied to a heating, ventilation, and air conditioning (HVAC) system, which includes a chilled water system and at least one rotary dehumidifier. The rotary dehumidifier is equipped with a front-mounted cooling coil. The chilled water system includes a chiller, a water pump, and a cooling tower. The method includes: Based on the dehumidification correlation model, the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized are determined for the chiller. Based on the chilled water outlet temperature to be optimized, cooling tower outlet temperature, terminal cooling capacity, chilled water temperature difference, cooling water temperature difference, chilled water flow rate to be optimized, water pump power, cooling tower power, operating time, electricity price, steam unit price, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized, an objective function is constructed. Constraints are constructed based on the range of values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of the at least one rotary dehumidifier. Under the constraints, with the objective function as the minimum, the objective function is solved based on the interval multi-objective particle swarm optimization algorithm to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of at least one rotary dehumidifier to be optimized. The target value is used to control the HVAC system.

2. The method according to claim 1, characterized in that, The objective function is constructed based on the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling capacity demand, the chilled water temperature difference, the cooling water temperature difference, the chilled water flow rate to be optimized, the water pump power, the cooling tower power, the operating time, the electricity price, the steam unit price, and the regeneration temperature, the rotor speed, and the air volume to be processed of at least one rotary dehumidifier to be optimized. The objective function includes: Based on the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling capacity, the chilled water temperature difference, the cooling water temperature difference, and the chilled water flow rate to be optimized, the chiller power is determined; The power of the chilled water system is determined based on the power of the chiller, the power of the water pump, and the power of the cooling tower. The operating cost of the chilled water system is determined based on the system's power, operating time, and electricity price. Based on the operating time, steam unit price, and the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of at least one rotary dehumidifier, the total operating cost of the dehumidifier is determined. Based on the operating costs of the chilled water system and the total operating costs of the dehumidifier, an objective function is constructed.

3. The method according to claim 1, characterized in that, The determination of the supply water temperature range for the chilled water outlet temperature to be optimized and the supply water flow rate range for the chilled water flow rate to be optimized based on the dehumidification correlation model includes: For each of the rotary dehumidifiers, the minimum water supply temperature and water supply flow rate of the rotary dehumidifier are determined using the dehumidification association model based on the current flow rate of the front cooling coil and the target dehumidification capacity. Based on the minimum supply water temperature and supply water flow rate of at least one rotary dehumidifier, the supply water temperature range for the chilled water outlet temperature to be optimized and the supply water flow rate range for the chilled water main unit to be optimized are determined.

4. The method according to any one of claims 1-3, characterized in that, The dehumidification correlation model was trained in the following manner: Determine the sample parameter data; the sample parameter data includes historical operating data and label dehumidification data; The historical operating data is input into a neural network to obtain predicted dehumidification data; the neural network includes an input layer, an output layer, and at least one fully connected layer; The training loss is determined based on the predicted dehumidification data and the labeled dehumidification data; The neural network is iteratively trained using the training loss to obtain the dehumidification correlation model.

5. The method according to claim 2, characterized in that, The total operating cost of the dehumidifier is determined based on the operating time, steam unit price, and the regeneration temperature, rotor speed, and air volume to be optimized for at least one rotary dehumidifier, including: For each rotary dehumidifier, the steam consumption of the rotary dehumidifier is determined based on the regeneration temperature to be optimized, the rotary speed to be optimized, and the air volume to be optimized. Based on the steam consumption, the operating time, and the steam unit price, the dehumidifier operating cost of the rotary dehumidifier is determined. The total operating cost of the dehumidifier is determined based on the operating cost of at least one rotary dehumidifier.

6. The method according to claim 4, characterized in that, The method further includes: Collect raw operational data from historical operation processes; The original running data is preprocessed to obtain historical running data; the preprocessing operations include outlier handling, missing value handling, data alignment, and data normalization.

7. An intelligent optimization control device for a heating, ventilation, and air conditioning (HVAC) system based on a chilled water system and a dehumidifier, characterized in that, The device is configured in a heating, ventilation, and air conditioning (HVAC) system, which includes a chilled water system and at least one rotary dehumidifier. The rotary dehumidifier is equipped with a front-mounted cooling coil. The chilled water system includes a chiller, a water pump, and a cooling tower. The device includes: The interval determination module is used to determine the supply temperature range of the chilled water outlet temperature to be optimized and the supply flow range of the chilled water flow rate to be optimized for the chiller based on the dehumidification correlation model. The function construction module is used to construct an objective function based on the chilled water outlet temperature to be optimized, the cooling tower outlet temperature, the terminal cooling capacity, the chilled water temperature difference, the cooling water temperature difference, the chilled water flow rate to be optimized, the water pump power, the cooling tower power, the running time, the electricity price, the steam unit price, and the regeneration temperature, the rotor speed and the air volume to be optimized of at least one rotary dehumidifier. The constraint construction module is used to construct constraint conditions based on the value ranges corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, the regeneration temperature to be optimized, the rotor speed to be optimized, and the air volume to be optimized of the at least one rotary dehumidifier. The target value determination module is used to solve the objective function based on the interval multi-objective particle swarm algorithm under the constraints, with the objective function as the minimum, to obtain the target values ​​corresponding to the chilled water outlet temperature to be optimized, the chilled water flow rate to be optimized, and the regeneration temperature, rotor speed and processing air volume of the at least one rotary dehumidifier to be optimized. The system control module is used to control the HVAC system using the target value.

8. 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; The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier, as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the intelligent optimization control method for a heating, ventilation, and air conditioning system based on a chilled water system and a dehumidifier according to any one of claims 1-6.

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