Self-organizing construction method of energy-saving group control strategy for variable air volume air conditioning system

Abstract

The present application relates to a self-organizing construction method for an energy-saving group control strategy for a variable air volume air conditioning system. It is applicable to the field of building energy conservation and intelligent building control technology. The technical solution includes: based on the equipment composition of the target air conditioning system, calling the intelligent agent individual optimization model class corresponding to each device from the pre-built intelligent agent class library, and generating a corresponding intelligent agent individual optimization model for each specific device in the system; based on the topological structure of the target air conditioning system, establishing a connection relationship between the intelligent agent individual optimization models corresponding to each device in the system; based on the connection relationship between the intelligent agent individual optimization models, adding collaborative optimization rules between the intelligent agent individual optimization models and adjacent intelligent agent individual optimization models in the intelligent agent individual optimization model in turn to form an energy-saving group control strategy for the target air conditioning system; obtaining the real-time operating parameters of the target air conditioning system, inputting them into the energy-saving group control strategy, optimizing the control action through the collaborative optimization algorithm, and outputting the optimal control action for each device in the system.

Description

Technical Field

[0001] The present invention relates to a self-organizing construction method for an energy-saving group control strategy of a variable air volume air conditioning system, which is applicable to the technical fields of building energy conservation and intelligent building control. Background Art

[0002] Variable air volume (VAV) air conditioning systems are the most widely used terminal air conditioning system in large public buildings. They dynamically adjust the air volume delivered to each terminal to meet individual load fluctuations. Research shows that VAV systems contribute significantly to a building's total energy consumption. Optimizing VAV systems through appropriate energy-saving group control strategies, thereby reducing system operating energy consumption while meeting the demands of each terminal, is crucial for achieving building energy conservation.

[0003] Actual variable air volume (VAV) air conditioning systems are highly individualized, with varying equipment composition and topology. Existing VAV system energy-saving group control strategies generally employ a top-down development approach, requiring domain engineers to develop each strategy case-by-case based on the specific form and characteristics of the target system. This process is time-consuming and labor-intensive, requiring a high level of expertise. Existing energy-saving control strategies are difficult to cost-effectively transfer to other VAV systems, hindering their widespread adoption and application in the field. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a self-organizing construction method of an energy-saving group control strategy for a variable air volume air conditioning system is provided.

[0005] The technical solution adopted by the present invention is: a self-organizing construction method of an energy-saving group control strategy for a variable air volume air conditioning system, characterized by comprising:

[0006] Based on the equipment composition of the target air-conditioning system, the agent individual optimization model class corresponding to each device is retrieved from the pre-built agent class library, and a corresponding agent individual optimization model is generated for each specific device in the system;

[0007] Based on the topological structure of the target air-conditioning system, the connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system is established;

[0008] Based on the connection relationship between the individual optimization models of intelligent agents, collaborative optimization rules between the individual optimization models of adjacent intelligent agents are added to the individual optimization models of intelligent agents in turn to form an energy-saving group control strategy for the target air-conditioning system;

[0009] The real-time operating parameters of the target air-conditioning system are obtained and input into the energy-saving group control strategy. The control actions are optimized through the collaborative optimization algorithm, and the optimal control actions of each device in the system are output.

[0010] The intelligent agent class library includes one or more of the following: an air handling unit intelligent agent individual optimization model class, an air duct intelligent agent individual optimization model class, and an air conditioning terminal intelligent agent individual optimization model class.

[0011] The air handling unit intelligent agent individual optimization model class includes:

[0012]

[0013]

[0014]

[0015]

[0016] Among them, the subscript ahu represents the air handling unit, the superscript in represents the inlet side parameters, out represents the outlet side parameters, m represents the mass flow rate, T represents the temperature, p represents the pressure, P ahu is the operating energy consumption of the air handling unit, P ahu，rt is the rated power of the air handling unit, f is the operating frequency of the blower, f rt is the rated operating frequency of the blower, m ahu，rt is the rated flow rate of the air handling unit on the air supply side, Q ahu is the cooling capacity of the air handling unit, c p is the constant pressure specific heat capacity of air, ρ air is the air density, g is the acceleration due to gravity, p rt is the rated pressure head of the blower, is the inlet flow rate of the chilled water side of the air handling unit, is the inlet temperature of the chilled water side of the air handling unit, a1-a4 and b1-b3 are the performance coefficients of the air handling unit.

[0017] The air duct agent individual optimization model class includes:

[0018] min{0}

[0019]

[0020]

[0021] Where m represents mass flow, superscript in represents inlet side parameters, out represents outlet side parameters, T represents temperature, p represents pressure, subscript ad represents duct, Ω outis the air supply branch pipe assembly, s i is the pipe impedance of the air supply branch pipe i.

[0022] The air-conditioning terminal agent individual optimization model class includes:

[0023] min C room =-β·(T room -T sp ) 2

[0024]

[0025]

[0026] Among them, T represents temperature, m represents mass flow, p represents pressure, subscript room represents air conditioning terminal, T sp Set the temperature for the terminal room, C room is the thermal comfort of the end user, β is the weight coefficient between the operating energy consumption and the thermal comfort of the end user, Q room is the cooling load of the terminal room, c p is the constant pressure specific heat capacity of air, A dp is the cross-sectional area of ​​the air valve of the variable air volume air conditioning box, is the opening of the air valve of the variable air volume air conditioning box, exp(·) is the exponential operator, a dp -c dp is the air valve resistance characteristic coefficient.

[0027] Based on the equipment composition of the target air-conditioning system, the agent individual optimization model class corresponding to each device is retrieved from the pre-built agent class library, and a corresponding agent individual optimization model is generated for each specific device in the system, including:

[0028] Set the performance parameters of the individual optimization model of the intelligent agent corresponding to each device, including capacity parameters and undetermined coefficients;

[0029] According to actual control requirements, set the variables to be optimized in the individual optimization model of the intelligent agent corresponding to each device;

[0030] According to the actual operating capabilities of the equipment, set the upper and lower limits of the optimization of the operating parameters of the individual optimization model of the intelligent agent corresponding to each device.

[0031] The method of sequentially adding collaborative optimization rules between the individual optimization models of the intelligent agents and the adjacent individual optimization models of the intelligent agents based on the connection relationship between the individual optimization models of the intelligent agents includes:

[0032] Consistency constraints between the export parameters of the individual optimization model of its upstream intelligent agent and the inlet parameters of the individual optimization model of its downstream intelligent agent;

[0033] The consistency constraint includes one or more of a temperature equality constraint, a flow equality constraint, and a pressure equality constraint.

[0034] The real-time operating parameters of the target air-conditioning system are obtained and input into the energy-saving group control strategy, and the control action is optimized through the collaborative optimization algorithm, and the optimal control action of each device in the system is output, including:

[0035] Initialize the global variable matrix and the Lagrange multiplier matrix for each agent Among them, N cc N represents the total number of consistency constraints of each agent’s individual optimization model. cc，i represents the number of consistency constraints of the agent individual optimization model i;

[0036] Set the penalty coefficient ρ, the number of iterations k = 0 and the upper limit of the number of iterations K max ;

[0037] According to the consistency constraints added to the individual optimization models of each agent, the collaborative optimization model of each agent is set as shown in the following formula:

[0038]

[0039] stg i ≤0,h i =0

[0040] Among them, f i represents the objective function of the agent individual optimization model i, g i represents the inequality constraint matrix of the agent individual optimization model i, h i represents the equality constraint matrix of the agent individual optimization model i, ||·||2 represents the 2-norm operator, represents the global variable value related to the agent individual optimization model i in the kth iteration, represents the Lagrange multiplier value associated with the agent individual optimization model i in the kth iteration, The parameter matrix representing the inlet and outlet temperature, flow rate, and pressure of the agent individual optimization model i;

[0041] Each agent solves the above collaborative optimization model in parallel to obtain the optimal solution for the k+1th iteration.

[0042] Update the Lagrange multiplier matrix and global variable matrix of the k+1th round as shown below:

[0043]

[0044]

[0045] Where, subscript l represents the set of agents adjacent to agent i, and N is the number of agents;

[0046] To judge whether the collaborative optimization process has reached convergence, the following formula is shown:

[0047]

[0048]

[0049] If the above formula is satisfied or the number of iterations k≥K max , this optimization process ends; otherwise, the next round of iteration is carried out.

[0050] A self-organizing device for constructing an energy-saving group control strategy for a variable air volume air conditioning system, characterized by comprising:

[0051] The model generation module is used to retrieve the agent individual optimization model class corresponding to each device from the pre-built agent class library based on the equipment composition of the target air-conditioning system, and generate a corresponding agent individual optimization model for each specific device in the system;

[0052] The model connection module is used to establish the connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system based on the topological structure of the target air-conditioning system;

[0053] A strategy formation module is used to add collaborative optimization rules between individual optimization models of intelligent agents based on the connection relationship between the individual optimization models of intelligent agents, thereby forming an energy-saving group control strategy for the target air-conditioning system;

[0054] The algorithm optimization module is used to obtain the real-time operating parameters of the target air-conditioning system, input them into the energy-saving group control strategy, optimize the control actions through the collaborative optimization algorithm, and output the optimal control actions for each device in the system.

[0055] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system are implemented.

[0056] An electronic device has a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and is characterized in that when the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system are implemented.

[0057] A variable air volume air conditioning system, characterized by comprising:

[0058] the electronic device;

[0059] The actuator is provided corresponding to each device in the variable air volume air conditioning system and is used to control the corresponding device based on the optimal control action output by the electronic device.

[0060] The beneficial effects of the present invention are: based on the equipment composition of the target air-conditioning system, the present invention retrieves the intelligent agent individual optimization model class corresponding to each device from the pre-built intelligent agent class library, and then quickly generates the corresponding intelligent agent individual optimization model for the equipment in the system, and uses the predefined intelligent agent individual optimization model to standardize the thermal characteristics and operating characteristics of different types of equipment in the variable air volume air-conditioning system, thereby realizing the rapid construction of the intelligent agent model.

[0061] The present invention connects individual optimization models of intelligent agents based on the topological structure of the actual system, thereby self-organizing and establishing collaborative optimization rules between intelligent agents to achieve energy-saving optimization of the target system.

[0062] For different variable air volume air conditioning systems, the present invention can quickly build a system-level energy-saving group control strategy according to its equipment composition and topological structure through the reuse and connection of intelligent individual optimization models. It has high development efficiency, can be applied to different forms of variable air volume air conditioning systems, and has good energy-saving effects.

[0063] The individual optimization model class of the air-conditioning terminal intelligent agent in the present invention takes into account the thermal comfort of the end user, so that the present invention can effectively improve the operating efficiency of the variable air volume air-conditioning system while ensuring the thermal comfort of the end user. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Schematic diagram of the structure of the variable air volume air conditioning system in Example 1.

[0065] Figure 2 This is a flow chart of the self-organizing construction method of the energy-saving group control strategy in Example 1.

[0066] Figure 3 is the cooling load demand on a typical summer day in Example 1.

[0067] Figure 4 The results of the system operation energy consumption in Example 1 are compared.

[0068] Figure 5 The results of the indoor temperature of the terminal room in Example 1 are compared. DETAILED DESCRIPTION

[0069] Example 1: Figure 1As shown in the figure, the variable air volume air conditioning system in this embodiment consists of one air handling unit and five variable air volume air conditioning boxes, which respectively adjust the temperature of the corresponding air-conditioned terminal rooms. The rated power and rated air volume of the air handling unit are 8.5kW and 417m 3 / min, with a rated cooling capacity of 160kW. Each room measures 12m x 16m x 4m and is an office-type room, with office hours from 8:00 am to 7:00 pm.

[0070] like Figure 2 As shown, the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system in this embodiment includes the following steps:

[0071] S1. Based on the equipment composition of the target air-conditioning system, the intelligent agent individual optimization model class corresponding to each device is retrieved from the pre-built intelligent agent class library, and the corresponding intelligent agent individual optimization model is generated for each specific device in the system.

[0072] S1-1. For the types of equipment that may be included in the variable air volume air conditioning system, establish a corresponding intelligent agent individual optimization model class for each type of equipment and build an intelligent agent class library.

[0073] The types of equipment that a variable air volume air conditioning system may contain include air handling units, air ducts, and air conditioning terminals. The individual optimization model class of the intelligent agent for each type of equipment is constructed as follows:

[0074] A. The individual optimization model of the air handling unit agent is shown as follows:

[0075]

[0076]

[0077]

[0078] Among them, the subscript ahu represents the air handling unit, the superscript in represents the inlet side parameters, out represents the outlet side parameters, m represents the mass flow rate, T represents the temperature, p represents the pressure, P ahu is the operating energy consumption of the air handling unit, P ahu，rt is the rated power of the air handling unit, f is the operating frequency of the blower, f rt is the rated operating frequency of the blower, m ahu,rt is the rated flow rate of the air handling unit on the air supply side, Q ahu is the cooling capacity of the air handling unit, c p is the constant pressure specific heat capacity of air, ρ air is the air density, g is the acceleration due to gravity, p rt is the rated pressure head of the blower, is the inlet flow rate of the chilled water side of the air handling unit, is the inlet temperature of the chilled water side of the air handling unit, a1-a4 and b1-b3 are the performance coefficients of the air handling unit.

[0079] B. The individual optimization model of the air duct agent is shown as follows:

[0080] min{0}

[0081]

[0082]

[0083] Among them, the subscript ad represents the air duct, Ω out is the air supply branch pipe assembly, s i is the pipe impedance of the air supply branch pipe i.

[0084] C. The individual optimization model of the air-conditioning terminal agent is as follows:

[0085] min C room =-β·(T room -T sp ) 2

[0086]

[0087]

[0088] Among them, the subscript room represents the air conditioning terminal, T sp Set the temperature for the terminal room, C room is the thermal comfort of the end user, β is the weight coefficient between the operating energy consumption and the thermal comfort of the end user, Q room is the cooling load of the terminal room, A dp is the cross-sectional area of ​​the air valve of the variable air volume air conditioning box, is the opening of the air valve of the variable air volume air conditioning box, exp(·) is the exponential operator, a dp -c dp is the air valve resistance characteristic coefficient.

[0089] S1-2. According to the equipment composition of the target air-conditioning system, the intelligent agent individual optimization model class corresponding to each device is retrieved from the intelligent agent class library. Through the instantiation of the intelligent agent class, the corresponding intelligent agent individual optimization model is generated for each specific device to complete the initialization of the intelligent agent model.

[0090] Set the performance parameters for each device agent's individual optimization model, including capacity parameters and undetermined coefficients. Capacity parameters include the air handling unit's rated power, rated flow rate, and rated cooling capacity. Undetermined coefficients include the air handling unit's coefficient of performance, the duct impedance of each branch, and the cross-sectional area and resistance characteristic coefficient of the variable air volume air conditioner's damper.

[0091] Based on actual control requirements, set the variables to be optimized for each device agent's individual optimization model. For the air handling unit, the variables to be optimized include the supply air temperature setpoint and the blower operating frequency. For the air conditioning terminal, the variables to be optimized include the variable air volume (VAV) air conditioner damper opening and the air volume supplied to the terminal room.

[0092] Based on the actual operating capacity of the equipment, the upper and lower limits of the optimization model's operating parameters for each device agent are set. The operating parameters of the air handling unit agent include inlet and outlet flow rates, temperatures, and pressures, fan operating frequency, and cooling capacity. The operating parameters of the duct agent include inlet and outlet flow rates, temperatures, and pressures. The operating parameters of the air conditioning terminal agent include indoor temperature and inlet flow rates, temperatures, and pressures.

[0093] S2. Based on the topological structure of the target air-conditioning system, establish the connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system.

[0094] In this embodiment, the air handling unit intelligent body is first connected to the air duct intelligent body, and the air duct intelligent body is then connected to the five air conditioning terminal (variable air volume air conditioning box) intelligent bodies respectively.

[0095] S3. Based on the connection relationship between the individual optimization models of intelligent agents, collaborative optimization rules between the individual optimization models of adjacent intelligent agents are added to the individual optimization models of intelligent agents in turn to form an energy-saving group control strategy for the target air-conditioning system.

[0096] The collaborative optimization rules added to each agent's individual optimization model include consistency constraints between the export parameters of its upstream agent model and the import parameters of its downstream agent model. The two consistency constraints are further divided into temperature equality constraints, flow equality constraints, and pressure equality constraints, as shown in the following equations:

[0097]

[0098]

[0099]

[0100] Among them, j represents the upstream agent adjacent to agent i, and k represents the downstream agent adjacent to agent i.

[0101] S4. Obtain the real-time operating parameters of the target air-conditioning system, input them into the energy-saving group control strategy, optimize the control actions through the collaborative optimization algorithm, and output the optimal control actions for each device in the system.

[0102] S4-1: Initialize the global variable matrix and the Lagrange multiplier matrix for each agent Among them, N cc represents the total number of consistency constraints of each agent, N cc，i Represents the number of consistency constraints that agent i has.

[0103] In this embodiment, N cc =6, air handling unit agent N cc，1 =1, duct agent N cc，2 =6, air-conditioning terminal intelligent agent 1-5N cc，3 -N cc，7 =1.

[0104] S4-2: Set the penalty coefficient ρ, the number of iterations k = 0, and the upper limit of the number of iterations K max In this example, the penalty coefficient ρ is set to 0.5, the number of iterations k is set to 0, and the upper limit of the number of iterations K is set to max =150.

[0105] S4-3: According to the consistency constraints added by each agent, the collaborative optimization model of each agent is set as shown in the following formula:

[0106]

[0107] stg i ≤0,h i =0

[0108] Among them, f i represents the objective function of agent i, g i represents the inequality constraint matrix of agent i, h i represents the equality constraint matrix of agent i, ||·||2 represents the 2-norm operator, represents the global variable value related to agent i in the kth iteration, represents the Lagrange multiplier value associated with agent i in the kth iteration, The parameter matrix representing the inlet and outlet temperature, flow rate, and pressure of agent i.

[0109] S4-4: Each agent solves the above collaborative optimization model in parallel to obtain the optimal solution for the k+1th iteration

[0110] S4-5: Update the Lagrange multiplier matrix and global variable matrix of the k+1th round as shown below:

[0111]

[0112]

[0113] Wherein, the subscript l represents the set of agents adjacent to agent i, and N is the number of agents, in this example N = 7.

[0114] S4-6: Determine whether the collaborative optimization process has reached convergence, as shown in the following formula:

[0115]

[0116]

[0117] In this embodiment, ε1=ε2=0.01. If the above formula is satisfied or the number of iterations k≥K max , the optimization process ends and goes to step S4-7. Otherwise, repeat steps S4-4 to S4-6.

[0118] S4-7: Output the optimal variable to be optimized to the actuator for execution of the control action.

[0119] The working condition data of a typical summer day are selected to verify the energy saving effect of the group control strategy. The cooling load demand on that day is as follows: Figure 3 As shown. The traditional rule-based group control strategy is set for performance comparison. Under this strategy, the air handling unit fan always runs at the power frequency, and the valve opening of the terminal variable air volume air conditioning box is always kept fully open. The indoor temperature setting value T of each terminal room sp At 26°C, the air handling unit inlet pressure is -50Pa (gauge pressure), room pressure p room The air supply temperature of the air handling unit is 25Pa (gauge pressure). The control interval is uniformly set to 1h.

[0120] Comparison of system energy consumption under two group control strategies Figure 4 As shown in the figure. Because the air handling unit's blower always operates at rated power under the traditional rule-based group control strategy, its operating energy consumption remains high at the rated power. In contrast, the energy-saving group control strategy constructed in this embodiment uses a model optimization approach to calculate the optimal control solution under different operating conditions. The total operating energy consumption of the system dropped from 107.5 kWh to 72.3 kWh, a reduction of 32.8%.

[0121] Comparison of the indoor temperature results of each terminal room under the two group control strategies Figure 5As shown in the figure. Under the rule-based control strategy, the air handling unit's blower always delivers air to the terminal at the rated air volume, lacking effective regulation of the terminal temperature. Consequently, the indoor temperature deviates significantly from the set value (26°C) during most time periods under this strategy, reducing the thermal comfort of the end user. In contrast, the energy-saving group control strategy constructed in this embodiment adjusts the air volume of each room through coordination between intelligent agents, maintaining the indoor temperature of each room near the set value with a maximum deviation of less than 0.5K, achieving a balance between operating energy consumption and terminal thermal comfort.

[0122] In summary, this embodiment can quickly construct a localized energy-saving group control strategy based on the actual VAV air conditioning system's equipment composition and topology. This strategy boasts high development efficiency and versatility, making it applicable to various VAV air conditioning system configurations. Furthermore, the energy-saving group control strategy constructed in this embodiment offers high energy efficiency and temperature control capabilities, effectively improving the operating efficiency of VAV air conditioning systems while ensuring thermal comfort for end users.

[0123] Example 2: This example is a self-organizing construction device for an energy-saving group control strategy of a variable air volume air conditioning system, including: a model generation module, a model connection module, a strategy formation module, an algorithm optimization module, etc.

[0124] In this example, the model generation module is used to retrieve the intelligent agent individual optimization model class corresponding to each device from the pre-built intelligent agent class library based on the equipment composition of the target air-conditioning system, and generate a corresponding intelligent agent individual optimization model for each specific device in the system.

[0125] In this embodiment, the model connection module is used to establish a connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system based on the topological structure of the target air-conditioning system.

[0126] In this example, the strategy formation module is used to add collaborative optimization rules between the individual optimization models of intelligent agents based on the connection relationship between the individual optimization models of intelligent agents, and form an energy-saving group control strategy for the target air-conditioning system.

[0127] In this embodiment, the algorithm optimization module is used to obtain the real-time operating parameters of the target air-conditioning system, input them into the energy-saving group control strategy, optimize the control actions through the collaborative optimization algorithm, and output the optimal control actions for each device in the system.

[0128] Example 3: This example is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system in Example 1 are implemented.

[0129] Example 4: This example is an electronic device having a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system in Example 1 are implemented.

[0130] Example 5: This example is a variable air volume air conditioning system, in which each device is equipped with an actuator for controlling its action. The system also includes the electronic device in Example 4, which outputs the optimal result of the variable to be optimized to the actuator for executing the control action.

Claims

1. A self-organizing construction method for an energy-saving group control strategy for a variable air volume air conditioning system, characterized in that: include: Based on the equipment composition of the target air-conditioning system, the agent individual optimization model class corresponding to each device is retrieved from the pre-built agent class library, and a corresponding agent individual optimization model is generated for each specific device in the system; Based on the topological structure of the target air-conditioning system, the connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system is established; Based on the connection relationship between the individual optimization models of intelligent agents, collaborative optimization rules between the individual optimization models of adjacent intelligent agents are added to the individual optimization models of intelligent agents in turn to form an energy-saving group control strategy for the target air-conditioning system; Obtain the real-time operating parameters of the target air-conditioning system, input them into the energy-saving group control strategy, optimize the control actions through the collaborative optimization algorithm, and output the optimal control actions for each device in the system; The agent class library includes one or more of the following: an air handling unit agent individual optimization model class, an air duct agent individual optimization model class, and an air conditioning terminal agent individual optimization model class; The air handling unit intelligent agent individual optimization model class includes: ； ； ； ； Among them, the subscript Indicates air handling unit, superscript represents the inlet side parameters, Indicates the export side parameters, represents the mass flow rate, Indicates temperature, Indicates pressure, is the operating energy consumption of the air handling unit, is the rated power of the air handling unit, is the operating frequency of the blower, is the rated operating frequency of the blower, is the rated flow rate of the air supply side of the air handling unit, is the cooling capacity of the air handling unit, is the constant pressure specific heat capacity of air, is the air density, is the acceleration due to gravity, is the rated pressure head of the blower, is the inlet flow rate of the chilled water side of the air handling unit, is the inlet temperature of the chilled water side of the air handling unit, as well as is the coefficient of performance of the air handling unit.

2. The self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to claim 1 is characterized in that: The air duct agent individual optimization model class includes: ； ； ； in, Indicates mass flow rate, superscript represents the inlet side parameters, Indicates the export side parameters, Indicates temperature, Indicates pressure, subscript Indicates air duct, For the air supply branch pipe collection, Air supply branch pipe The pipeline impedance.

3. The self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to claim 1 is characterized in that: The air-conditioning terminal agent individual optimization model class includes: ； ； ； in, Indicates temperature, represents the mass flow rate, Indicates pressure, subscript Indicates the air conditioning terminal, Set the temperature for the terminal room, For the thermal comfort of end users, is the weight coefficient between operating energy consumption and thermal comfort of end users, is the cooling load of the terminal room, is the constant pressure specific heat capacity of air, is the cross-sectional area of ​​the air valve of the variable air volume air conditioning box, is the opening of the air valve of the variable air volume air conditioning box, is the exponential operator, is the air valve resistance characteristic coefficient.

4. The self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to claim 1 is characterized in that: Based on the equipment composition of the target air-conditioning system, the agent individual optimization model class corresponding to each device is retrieved from the pre-built agent class library, and a corresponding agent individual optimization model is generated for each specific device in the system, including: Set the performance parameters of the individual optimization model of the intelligent agent corresponding to each device, including capacity parameters and undetermined coefficients; According to actual control requirements, set the variables to be optimized in the individual optimization model of the intelligent agent corresponding to each device; According to the actual operating capabilities of the equipment, set the upper and lower limits of the optimization of the operating parameters of the individual optimization model of the intelligent agent corresponding to each device.

5. The self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to claim 1 is characterized by: The method of sequentially adding collaborative optimization rules between the individual optimization models of the intelligent agents and the adjacent individual optimization models of the intelligent agents based on the connection relationship between the individual optimization models of the intelligent agents includes: Consistency constraints between the export parameters of the individual optimization model of its upstream intelligent agent and the inlet parameters of the individual optimization model of its downstream intelligent agent; The consistency constraint includes one or more of a temperature equality constraint, a flow equality constraint, and a pressure equality constraint.

6. The self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to claim 1 is characterized in that: The real-time operating parameters of the target air-conditioning system are obtained and input into the energy-saving group control strategy, and the control action is optimized through the collaborative optimization algorithm, and the optimal control action of each device in the system is output, including: Initialize the global variable matrix and the Lagrange multiplier matrix for each agent ;in, represents the total number of consistency constraints of each agent's individual optimization model, Represents the agent individual optimization model The number of consistency constraints; Set the penalty coefficient , number of iterations and the upper limit of the number of iterations ; According to the consistency constraints added to the individual optimization models of each agent, the collaborative optimization model of each agent is set as shown in the following formula: ； ； in, Represents the agent individual optimization model The objective function, Represents the agent individual optimization model The inequality constraint matrix of Represents the agent individual optimization model The equality constraint matrix, represents the 2-norm operator, Indicates the Optimization model of individual agents in round iterations Related global variable values, Indicates the Optimization model of individual agents in round iterations The associated Lagrange multiplier values, Represents the agent individual optimization model The parameter matrix of inlet and outlet temperature, flow rate and pressure; Each agent solves the above collaborative optimization model in parallel to obtain the The optimal solution of round iteration ; Update The Lagrange multiplier matrix and global variable matrix of the wheel are shown as follows: ； ； Among them, the subscript Representation and Agent The set of adjacent agents, is the number of agents; To judge whether the collaborative optimization process has reached convergence, the following formula is shown: ； ； If the above formula or the number of iterations is satisfied , this optimization process ends; otherwise, the next round of iteration is carried out.

7. A self-organizing device for constructing an energy-saving group control strategy for a variable air volume air conditioning system, characterized in that: include: The model generation module is used to retrieve the agent individual optimization model class corresponding to each device from the pre-built agent class library based on the equipment composition of the target air-conditioning system, and generate a corresponding agent individual optimization model for each specific device in the system; The model connection module is used to establish the connection relationship between the individual optimization models of the intelligent agents corresponding to each device in the system based on the topological structure of the target air-conditioning system; A strategy formation module is used to add collaborative optimization rules between individual optimization models of intelligent agents based on the connection relationship between the individual optimization models of intelligent agents, thereby forming an energy-saving group control strategy for the target air-conditioning system; The algorithm optimization module is used to obtain the real-time operating parameters of the target air-conditioning system, input them into the energy-saving group control strategy, optimize the control actions through the collaborative optimization algorithm, and output the optimal control actions for each device in the system; The agent class library includes one or more of the following: an air handling unit agent individual optimization model class, an air duct agent individual optimization model class, and an air conditioning terminal agent individual optimization model class; The air handling unit intelligent agent individual optimization model class includes: ； ； ； ； Among them, the subscript Indicates air handling unit, superscript represents the inlet side parameters, Indicates the export side parameters, represents the mass flow rate, Indicates temperature, Indicates pressure, is the operating energy consumption of the air handling unit, is the rated power of the air handling unit, is the operating frequency of the blower, is the rated operating frequency of the blower, is the rated flow rate of the air supply side of the air handling unit, is the cooling capacity of the air handling unit, is the constant pressure specific heat capacity of air, is the air density, is the acceleration due to gravity, is the rated pressure head of the blower, is the inlet flow rate of the chilled water side of the air handling unit, is the inlet temperature of the chilled water side of the air handling unit, as well as is the coefficient of performance of the air handling unit.

8. A storage medium storing a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the self-organizing construction method of the energy-saving group control strategy of the variable air volume air conditioning system according to any one of claims 1 to 6 are implemented.

10. A variable air volume air conditioning system, characterized in that: include: The electronic device according to claim 9; The actuator is provided corresponding to each device in the variable air volume air conditioning system and is used to control the corresponding device based on the optimal control action output by the electronic device.

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