Energy-saving Prediction Method for Central Air Conditioning System Based on Simulation

Abstract

The present invention discloses an energy-saving prediction method for a central air-conditioning system based on simulation. The method includes: obtaining target air duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system; constructing an environmental prediction function based on the target air duct parameters, target environmental parameters, and target equipment parameters; performing environmental simulation on the target environment where the ventilation duct system is located to obtain a fluid simulation function corresponding to the target environment; performing system simulation based on the environmental prediction function and the fluid simulation function to obtain equipment simulation parameters; and performing energy consumption prediction based on the equipment simulation parameters and the environmental prediction function to obtain an energy consumption prediction result. By combining environmental parameters and equipment parameters to achieve energy consumption prediction, the present invention can improve the accuracy and timeliness of energy consumption prediction, and thus can timely adjust relevant parameters of the ventilation duct system according to environmental changes to improve the ventilation quality.

Description

Technical Field

[0001] The present invention relates to the technical field of multi - energy utilization, and more specifically, to an energy - saving prediction method for a central air - conditioning system based on simulation. Background Art

[0002] Underground mines, especially deep - shaft mines with a depth exceeding 1000 meters, face severe challenges such as high temperature and high humidity due to their special environment. In such an environment, the mine ventilation and refrigeration systems need to effectively control the temperature and humidity to ensure the safety and comfort of miners. For this reason, central air - conditioning systems are widely used underground for refined temperature and humidity regulation. However, due to the ever - changing underground environment, how to optimize the energy consumption and regulation efficiency of these systems has become an urgent problem to be solved in order to optimize the design and regulation of the mine ventilation and cooling systems.

[0003] Currently, the energy - consumption optimization of mine ventilation and refrigeration systems mostly relies on physical models and simulation technologies, which usually establish the relationships between factors such as temperature and humidity, air flow, and equipment power.

[0004] Existing systems usually have problems with lagging responses. In particular, the adjustment speeds of traditional fans and refrigeration units are relatively slow and cannot quickly respond to changes in the underground environment. For example, in the case of sudden temperature changes or increased ventilation requirements, the system cannot be adjusted to the optimal state in time, resulting in energy waste and discomfort in the working environment of miners. Summary of the Invention

[0005] In view of the above - mentioned defects of the prior art, the technical problem to be solved by the present invention is how to improve the real - time performance and accuracy of the energy - consumption adjustment of the central air - conditioning system.

[0006] To solve at least one of the above - mentioned technical problems, the present invention provides an energy - saving prediction method for a central air - conditioning system based on simulation.

[0007] According to one aspect of the present disclosure, there is provided an energy - saving prediction method for a central air - conditioning system based on simulation, including:

[0008] Obtaining target air - duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system;

[0009] Constructing an environmental prediction function based on the target air - duct parameters, the target environmental parameters, and the target equipment parameters;

[0010] Performing a simulation on the ventilation duct system and the target environment where the ventilation duct system is located to obtain a fluid simulation function;

[0011] Performing a system simulation based on the environmental prediction function and the fluid simulation function to obtain equipment simulation parameters;

[0012] Based on the device simulation parameters and the environmental prediction function, perform energy consumption prediction to obtain an energy consumption prediction result.

[0013] In some possible embodiments, the obtaining of the target environmental parameters corresponding to the ventilation duct system includes:

[0014] In response to a parameter acquisition instruction, acquire the initial duct parameters, initial environmental parameters, and initial device parameters corresponding to the ventilation duct system;

[0015] Perform data preprocessing on the initial duct parameters, the initial environmental parameters, and the initial device parameters to obtain the target duct parameters, the target environmental parameters, and the target device parameters.

[0016] In some possible embodiments, the constructing of the environmental prediction function based on the target environmental parameters and the target device parameters includes:

[0017] Based on the target environmental parameters, respectively construct a temperature change function, a humidity change function, and a wind speed change function;

[0018] Based on the target duct parameters and the target device parameters, construct a first device power function and a second device power function;

[0019] Based on the temperature change function, the humidity change function, the wind speed change function, the first device power function, and the second device power function, construct the environmental prediction function.

[0020] In some possible embodiments, the performing of environmental simulation on the target environment where the ventilation duct system is located to obtain a fluid simulation function corresponding to the target environment includes:

[0021] Based on the air flow velocity of each ventilation duct in the ventilation duct system in the target environment, determine the fluid flow function;

[0022] Based on the fluid flow function and the pressure distribution parameters in the target environment, determine the pressure field function; and,

[0023] Based on the wind speed distribution parameters in the target environment, determine the wind speed field function.

[0024] In some possible embodiments, the performing of system simulation based on the environmental prediction function and the fluid simulation function to obtain device simulation parameters includes:

[0025] Acquire the resistance parameters corresponding to the ventilation duct system;

[0026] Calculate the resistance value to be adjusted in the ventilation duct system based on the resistance parameter, the target device parameter, the fluid flow function, and the wind speed field function;

[0027] Input the resistance value to be adjusted into the air duct adjustment function and the fan adjustment function respectively to obtain the air duct diameter adjustment information and the fan configuration adjustment information.

[0028] In some possible embodiments, the energy consumption prediction based on the device simulation parameter and the environment prediction function to obtain the energy consumption prediction result includes:

[0029] Based on the device simulation parameter and the environment prediction function, determine the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter at the current moment;

[0030] Perform fuzzy processing on the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter to obtain multiple environment parameter data sets;

[0031] Perform energy consumption prediction based on a preset energy consumption adjustment instruction and the multiple environment parameter data sets to obtain the energy consumption prediction result.

[0032] In some possible embodiments, the energy consumption prediction based on a preset energy consumption adjustment instruction and the multiple environment parameter data sets to obtain the energy consumption prediction result includes:

[0033] Obtain a preset energy consumption adjustment instruction;

[0034] Associate the predicted energy consumption adjustment instruction with the multiple environment parameter data sets to obtain a fan control instruction and a power control instruction;

[0035] Perform energy consumption prediction based on the fan control instruction and the power control instruction to obtain the energy consumption prediction result.

[0036] According to the second aspect of the present disclosure, there is provided an energy-saving prediction device for a central air-conditioning system, the device includes:

[0037] A target parameter acquisition module for acquiring target air duct parameters, target environment parameters, and target device parameters corresponding to the ventilation duct system;

[0038] An environment prediction function construction module for constructing an environment prediction function based on the target air duct parameter, the target environment parameter, and the target device parameter;

[0039] An environment simulation module for performing environment simulation on the target environment where the ventilation duct system is located to obtain a fluid simulation function corresponding to the target environment;

[0040] A system simulation module, configured to perform system simulation based on the environment prediction function and the fluid simulation function to obtain device simulation parameters;

[0041] An energy consumption prediction module, configured to perform energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

[0042] According to a third aspect of the present disclosure, there is provided an electronic device, which includes a processor and a memory. At least one instruction and at least one program segment are stored in the memory, and the at least one instruction and the at least one program segment are loaded and executed by the processor to implement the energy-saving prediction method for a central air-conditioning system as described above.

[0043] According to a fourth aspect of the present disclosure, there is provided a computer storage medium, in which at least one instruction and at least one program segment are stored, and the at least one instruction and the at least one program segment are loaded and executed by a processor to implement the energy-saving prediction method for a central air-conditioning system as described above.

[0044] Implementing the present invention has the following beneficial effects:

[0045] In the present invention, by obtaining the target air duct parameters, target environment parameters, and target device parameters corresponding to the ventilation duct system, and then constructing an environment prediction function and a fluid simulation function based on the target air duct parameters, target environment parameters, and target device parameters, the correlation between environmental changes and system operation can be established to improve the correlation degree between the two; further, based on the environment prediction function and the fluid simulation function, system simulation is performed to obtain device simulation parameters, and the structural layout and parameters of the ventilation duct system can be effectively adjusted according to the device simulation parameters, so as to effectively adjust the ventilation duct system and improve the operation efficiency of the central air-conditioning system; based on the device simulation parameters and the environment prediction function, energy consumption prediction is performed to obtain an energy consumption prediction result, so that the central air-conditioning system can be adjusted to the optimal state in time when there is a sudden temperature change or an increase in ventilation demand, thereby avoiding energy waste and discomfort in the working environment of miners. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic flow chart corresponding to the energy-saving prediction method for a central air-conditioning system based on simulation provided by an embodiment of the present invention;

[0048] Figure 2 A flow schematic diagram corresponding to the system provided by the embodiment of the present invention;

[0049] Figure 3 A flow schematic diagram corresponding to the energy consumption prediction provided by the embodiment of the present invention;

[0050] Figure 4 A structural schematic diagram corresponding to an energy-saving prediction device for a central air-conditioning system provided by the embodiment of the present invention. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0053] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0054] The special term "exemplary" here means "serving as an example, embodiment, or illustrative". Any embodiment described here as "exemplary" does not have to be construed as superior to or better than other embodiments.

[0055] As used herein, the term "and / or" merely describes an associated relationship between associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A alone, both A and B existing simultaneously, and B alone. Additionally, the term "at least one" as used herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may mean including any one or more elements selected from the set consisting of A, B, and C.

[0056] In addition, to better illustrate the present disclosure, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can be implemented without certain specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0057] Figure 1 It is a schematic flowchart corresponding to the energy-saving prediction method for a central air-conditioning system based on simulation. The execution subject can be any terminal capable of implementing the energy-saving prediction method for a central air-conditioning system, such as a computer, a server cluster, etc. The application scenario of the method can be an underground mine to achieve real-time prediction of the energy consumption of the central air-conditioning system by combining the environmental data inside a specific mine and the structural data of a specific ventilation duct system, and then making real-time adjustments to the ventilation of the central air-conditioning system based on the prediction results. As Figure 1 shown, the energy-saving prediction method for a central air-conditioning system based on simulation includes:

[0058] Step S101: Obtain target duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system;

[0059] In a specific embodiment, various sensors can be installed to collect environmental data such as temperature, humidity, and wind speed in the underground mine in real time, that is, target environmental parameters; at the same time, basic structural data such as duct diameter and duct curvature of the ventilation duct system installed in the underground mine are collected, that is, target duct parameters; and equipment parameters such as fans and refrigeration units in the central air-conditioning system are collected, that is, target equipment parameters.

[0060] The obtaining of the target environmental parameters corresponding to the ventilation duct system includes:

[0061] In response to a parameter acquisition instruction, obtain the initial duct parameters, initial environmental parameters, and initial equipment parameters corresponding to the ventilation duct system;

[0062] Specifically, appropriate sensors need to be selected for collecting data on the mine environment. Sensors such as temperature, humidity, wind speed, air pressure, and air flow direction are usually used. According to the mine depth, environmental conditions, and equipment requirements, the following types of sensors are selected: temperature sensors for monitoring temperature changes in different areas of the mine; humidity sensors for measuring the relative humidity in the mine; wind speed sensors for detecting wind speed and air flow direction; and air pressure sensors for monitoring air pressure changes.

[0063] To effectively collect data, a data acquisition system can be used to connect with the sensors. Each sensor is connected to the data acquisition system through analog signals or digital signals, and the data acquisition system has real-time data processing and storage functions. After the data acquisition system is started, it begins to collect real-time temperature T, humidity H, and wind speed V as initial environmental parameters.

[0064] Meanwhile, it is necessary to collect the basic structural information of each air duct in the ventilation duct system, such as air duct diameter D, air duct curvature β, etc. as initial air duct parameters. For key parts such as the main and auxiliary air shafts and air ducts in the mine, their geometric information can be collected through sensors or measuring devices. In addition to the initial environmental parameters and initial air duct parameters, the equipment parameters corresponding to the fans and refrigeration units in the central air conditioning system are also important information required for optimizing the central air conditioning system. For equipment such as fans and refrigeration units, information such as their power, working status, and efficiency needs to be collected as initial equipment parameters.

[0065] Perform data preprocessing on the initial air duct parameters, the initial environmental parameters, and the initial equipment parameters to obtain the target air duct parameters, the target environmental parameters, and the target equipment parameters.

[0066] In a specific embodiment, after all the collected data is transmitted to the data acquisition system, data preprocessing and data synchronization are required. Data preprocessing includes steps such as removing noise and correcting missing data. Data synchronization can ensure that the data of each sensor and the equipment data are aligned in time for subsequent analysis and simulation, thereby improving the accuracy and reliability of system optimization and energy consumption prediction.

[0067] Step S102: Based on the target air duct parameters, the target environmental parameters, and the target equipment parameters, construct an environmental prediction function;

[0068] In a specific embodiment, according to the obtained target air duct parameters, target environmental parameters, and target equipment parameters mentioned above, a system dynamics model corresponding to the ventilation duct system in the underground mine can be established, that is, an environmental prediction function. The environmental prediction function can simulate factors such as temperature and humidity changes, equipment power regulation, and environmental feedback in the underground mine, and then use system dynamics simulation to calculate energy consumption.

[0069] Specifically, constructing an environmental prediction function based on the target environmental parameters and the target device parameters includes:

[0070] Based on the target environmental parameters, construct a temperature change function, a humidity change function, and a wind speed change function respectively;

[0071] In a specific embodiment, it can be considered that the target environmental parameters include the parameters used in the following function construction processes, such as temperature parameters, humidity parameters, and wind speed parameters.

[0072] The change in temperature is not only affected by the input of external air and equipment refrigeration, but is also closely related to the heat load in the mine (such as mining operations, equipment heat generation, etc.). Assuming that the heat conduction and convection inside the mine can be represented by a simple thermodynamic model, i.e., the temperature change function, the temperature change function is:

[0073]

[0074] where, T is the mine temperature at time t, in °C; C is the heat capacity of the air in the mine, in J / °C; is the heat flow of the input air, in W; is the output of the heat inside the mine, such as the heat exhausted by the fan, in W; is the heat load inside the mine, in W.

[0075] The change in mine humidity is related to factors such as water vapor in the air, the working state of the cooling equipment, and air flow velocity. The humidity change function is:

[0076]

[0077] where, H is the relative humidity of the mine at time t, in %; is the humidity capacity of the air in the mine, in g / %; is the amount of water vapor in the input air, in g / s; is the amount of wet air flowing out, in g / s; is the heat released by the condensation of water vapor, in W.

[0078] The change in wind speed directly affects the air flow and the transfer of temperature and humidity. The relationship between the fan power and the air flow can be described by the wind speed change function. The wind speed change function is:

[0079]

[0080] where, V is the wind speed inside the mine at time t, in m / s; is the dynamic response parameter of the air velocity in the mine, with the unit of kg / (m*s); is the energy input to the fan, with the unit of W; is the energy of the air flowing out, with the unit of W.

[0081] Construct the first equipment power function and the second equipment power function based on the target air duct parameters and the target equipment parameters;

[0082] In a specific embodiment, it can be considered that the target equipment parameters include the parameters used in the following function construction process, such as fan efficiency, chiller efficiency, etc. In the mine ventilation and refrigeration system, the energy transfer mainly includes the exchange of heat and moisture, and the energy transfer process involves the change of heat load, the power output of the chiller and the energy consumption of the fan.

[0083] Further establish the energy transfer function of the system through the computational fluid dynamics (CFD) simulation results and the target equipment parameters. The energy transfer function is the first equipment power function and the second equipment power function. Among them, the first equipment power function can be used to describe the relationship between the power output of the fan and factors such as air velocity and air duct diameter, and the second equipment power function can be used to describe the relationship between the power of the chiller and the cooling capacity.

[0084] Specifically, the first equipment power function is:

[0085]

[0086] Among them, is the power of the fan at time t, with the unit of W; is the efficiency of the fan, with the unit between dimensionless (0-1); is the air density, with the unit of kg / m 3 ; is the cross-sectional area of the air duct, with the unit of m 2 ; is the air velocity, with the unit of m / s.

[0087] The second equipment power function is:

[0088]

[0089] Among them, is the cooling capacity of the chiller at time t, with the unit of W; is the efficiency of the chiller, with the unit between dimensionless (0-1); is the power of the chiller, with the unit of W.

[0090] Construct the environmental prediction function based on the temperature change function, the humidity change function, the wind speed change function, the first device power function, and the second device power function.

[0091] In a specific embodiment, a simulation tool can be used to perform dynamic simulation to obtain the environmental prediction function. The environmental prediction function can reflect the temperature and humidity change trends, the time required for the temperature and humidity to adjust to a stable state, and the total energy consumption of the fan and the refrigeration unit. The environmental prediction function can be:

[0092]

[0093] Wherein, is the total energy consumption of the ventilation duct system at time t, with the unit of joule (J); is the power of the fan, with the unit of watt (W); is the power of the refrigeration unit, with the unit of watt (W).

[0094] Step S103: Simulate the ventilation duct system and the target environment where the ventilation duct system is located to obtain a fluid simulation function;

[0095] In a specific embodiment, the ventilation duct system and the target environment where it is located are simulated by combining CFD simulation. The obtained fluid simulation function is used to calculate the optimized layout of the ventilation duct system, the air flow distribution (wind speed, pressure field), and the optimized configuration (pipe size, fan power).

[0096] Use the computational fluid dynamics (CFD) method to simulate the complex ventilation pipe network in the underground mine, and analyze the air flow path, wind speed distribution, and pipe resistance, etc. Combine the above environmental prediction function to optimize the layout of the ventilation duct, the duct size, and the configuration of the fan to improve the overall ventilation efficiency.

[0097] Specifically, use the computational fluid dynamics (CFD) method to establish a three-dimensional fluid model of the ventilation duct system in the underground mine. This model will describe key factors such as the air flow path, wind speed distribution, pressure distribution, and pipe resistance. The specific process is as follows:

[0098] Establish a three-dimensional geometric model: Construct a model of the ventilation duct system inside the mine in CFD software (such as ANSYS Fluent, COMSOL, etc.). The model includes each air duct, branch pipe, and various underground equipment (fans, cooling systems, etc.).

[0099] Determine physical properties and boundary conditions: Determine the fluid properties (such as air density, viscosity, etc.) and boundary conditions (inlet air velocity, outlet pressure, equipment power, etc.) for each duct area, that is, the fluid simulation function. The environmental simulation of the target environment where the ventilation duct system is located to obtain the fluid simulation function corresponding to the target environment includes:

[0100] Determine the fluid flow function based on the flow velocity of air in each ventilation duct of the ventilation duct system in the target environment;

[0101] In a specific embodiment, it can be assumed that air is an ideal gas, and the air density can be calculated by the following equation:

[0102]

[0103] where ρ is the air density, with the unit of kg / m 3 ; P is the air pressure, with the unit of Pa; R is the gas constant of air, R = 287.1 J / (kg·K); T is the temperature, with the unit of K (Kelvin).

[0104] Furthermore, combining the air density function and the air flow velocity, determine the fluid flow function corresponding to each ventilation duct in the ventilation duct system; the fluid flow function can be:

[0105]

[0106] where v is the fluid velocity vector, with the unit of m / s; ρ is the fluid density, with the unit of kg / m 3 ; P is the pressure, with the unit of Pa; μ is the dynamic viscosity of the fluid, with the unit of Pa ·s; f is the body force, such as gravity, with the unit of N / m 3 .

[0107] The boundary conditions include the wind speed and pressure input by the fan and the refrigeration unit, the boundary conditions of the pipeline interface, and the outlet pressure of the exhaust port, etc.

[0108] Based on the fluid flow function and the pressure distribution parameters in the target environment, determine the pressure field function; and,

[0109] In a specific embodiment, the pressure distribution parameters at each point inside the mine are obtained through CFD simulation to obtain the pressure field function, and the pressure field function can be expressed as:

[0110]

[0111] where is the pressure at the coordinate point (x, y, z), with the unit of Pa; is the initial pressure, with the unit of Pa; g is the acceleration due to gravity, with the unit of m / s 2 ; ρ is the fluid density, with the unit of kg / m 3 .

[0112] Determine the wind speed field function based on the wind speed distribution parameters in the target environment.

[0113] In a specific embodiment, the wind speed distribution parameters at each point inside the mine are obtained through CFD simulation, and the wind speed field function is obtained. The wind speed field function can be expressed as:

[0114]

[0115] wherein is the wind speed at the coordinate point (x, y, z), with the unit of m / s; v x , v y and v z are the velocity components of the fluid in the x, y, and z directions respectively, with the unit of m / s.

[0116] Step S104: Perform system simulation based on the environment prediction function and the fluid simulation function to obtain device simulation parameters;

[0117] In a specific embodiment, according to the CFD simulation results, the layout of the ventilation duct system, the duct size, and the fan configuration in the mine can be adjusted. By adjusting the duct size, such as adjusting the diameter of the pipeline, the layout of the air duct, and the fan configuration, the energy consumption of the ventilation duct system can be minimized, thereby improving the ventilation efficiency.

[0118] Figure 2 is the schematic flowchart corresponding to the system simulation provided by the embodiment of the present invention. As Figure 2 shown, step S104 may include:

[0119] Step S201: Obtain the resistance parameters corresponding to the ventilation duct system;

[0120] In a specific embodiment, the resistance of each pipeline in the ventilation duct system can be calculated first. The pipeline resistance mainly consists of local resistance and frictional resistance, and the resistance parameter can be the frictional resistance parameter.

[0121] Step S202: Calculate the resistance value to be adjusted in the ventilation duct system based on the resistance parameter, the target device parameter, the fluid flow function, and the wind speed field function;

[0122] In a specific embodiment, the target device parameters further include the pipeline length and the pipeline diameter; the resistance value to be adjusted can be calculated by the following formula:

[0123]

[0124] Among them, is the pressure in the pipeline, with the unit of Pa; f is the resistance parameter, which is related to the pipeline material and flow state; L is the pipeline length, with the unit of m; D is the pipeline diameter, with the unit of m, and ρ is the air density determined based on the fluid flow function, with the unit of kg / m 3 ; v is the wind speed determined based on the wind speed field function, with the unit of m / s.

[0125] Step S203: Input the to-be-adjusted resistance values into the air duct adjustment function and the fan adjustment function respectively to obtain the air duct diameter adjustment information and the fan configuration adjustment information.

[0126] In a specific embodiment, the device simulation parameters include the air duct diameter adjustment information and the fan configuration adjustment information; the energy efficiency of the entire ventilation duct system can be calculated in combination with the resistance corresponding to each pipeline, and analyzed in combination with the performance curve of the fan. Specifically, for pipelines with large resistance, the pipeline diameter can be increased to reduce the flow resistance, and the power configuration and operating parameters of the fan can be adjusted according to the working efficiency of the fan and the system requirements. The fan configuration adjustment information can include the fan power and the wind speed.

[0127] The air duct diameter adjustment information can be determined according to the calculation formula for adjusting the fan diameter. The calculation formula for adjusting the air duct diameter is:

[0128]

[0129] Among them, is the adjusted air duct diameter, with the unit of m; is the pipeline resistance, with the unit of Pa; L is the pipeline length, with the unit of m; ρ is the air density determined based on the fluid flow function, with the unit of kg / m 3 ; v is the wind speed determined based on the wind speed field function, with the unit of m / s.

[0130] The fan configuration adjustment information can be determined according to the calculation formula for adjusting the fan configuration. The calculation formula for adjusting the fan configuration is:

[0131]

[0132] Among them, is the adjusted fan power, with the unit of W; is the fan efficiency, with the unit of dimensionless; A is the cross-sectional area of the air duct, with the unit of ; is the optimized wind speed, with the unit of m / s.

[0133] Further, after obtaining the air duct diameter adjustment information and the fan configuration adjustment information, a re-simulation verification can be carried out to ensure that the overall ventilation efficiency is improved and the energy consumption is reduced. The verification results should include the energy consumption comparison before and after optimization, the system response speed, the temperature and humidity control effect, etc.

[0134] Step S105: Based on the device simulation parameters and the environment prediction function, perform energy consumption prediction to obtain an energy consumption prediction result.

[0135] In a specific embodiment, the air duct diameter adjustment information, the fan configuration adjustment information, and the environment prediction function can be combined, and a fuzzy control algorithm can be applied to adjust the operating states of the fan and the refrigeration unit. Fuzzy control can flexibly adjust the system with fewer preset rules in the face of environmental changes to reduce the system response delay, thereby improving the ventilation efficiency and energy consumption of the central air conditioning system for ventilating the ventilation duct system. Further, according to real-time environmental changes (such as temperature and humidity fluctuations), the fuzzy control algorithm adjusts the device power and ventilation air volume to ensure the stability of the mine environment.

[0136] Figure 3 The flowchart corresponding to the energy consumption prediction provided by the embodiment of the present invention is as Figure 3 shown. The energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result, that is, step S105 may include:

[0137] Step S301: Based on the device simulation parameters and the environment prediction function, determine the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter at the current moment;

[0138] In a specific embodiment, first, the fan diameter adjustment information, the fan configuration adjustment information, and real-time environmental data can be obtained, where the real-time environmental data may include the current temperature parameter T(t) and the change rate ΔT(t), the current humidity parameter H(t) and the change rate ΔH(t), the current wind speed parameter V(t), and the ventilation load parameter P vent (t).

[0139] Establish a fuzzy rule base according to the changes in the real-time environmental data. Usually, the set rules are such as "if the temperature is high and the humidity is low, then increase the fan speed", and so on. Multiple fuzzy rules need to be set according to actual needs to define the behavior of device adjustment.

[0140] Step S302: Perform fuzzy processing on the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter to obtain multiple environmental parameter data sets;

[0141] In a specific embodiment, the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter are used as input variables and are converted into fuzzy sets through a fuzzification function, that is, multiple environmental parameter data sets. Specifically, the current temperature parameter T(t) can be divided into three fuzzy sets: low temperature, medium temperature, and high temperature. The current humidity parameter H(t) can be divided into three fuzzy sets: low humidity, medium humidity, and high humidity. And the current wind speed parameter V(t) can be divided into three fuzzy sets: low wind speed, medium wind speed, and high wind speed.

[0142] Assume that the fuzzification process of the current temperature parameter T(t) can be represented by the following membership function:

[0143]

[0144] Where are the set low temperature threshold and high temperature threshold; is the membership degree corresponding to the temperature T, that is, the fuzzification result of the current temperature parameter, indicating the membership degree of the temperature in the "low temperature" fuzzy set.

[0145] Similarly, the current humidity parameter H(t) and the current wind speed parameter V(t) can be fuzzified.

[0146] Step S303: Perform energy consumption prediction based on the preset energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain the energy consumption prediction result.

[0147] Specifically, the performing energy consumption prediction based on the preset energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain the energy consumption prediction result, that is, step S303 may include:

[0148] Obtain the preset energy consumption adjustment instruction;

[0149] Associate the predicted energy consumption adjustment instruction with the multiple environmental parameter data sets to obtain a fan control instruction and a power control instruction;

[0150] In a specific embodiment, in a specific embodiment, the multiple environmental parameter data sets (that is, the membership degree corresponding to each parameter) , and are used as input data, and the preset energy consumption adjustment instruction for controlling the fan and the refrigeration unit is determined according to the fuzzy rule base. For example, if the current temperature parameter T(t) is "high temperature" and H(t) is "low humidity", then the rotational speed of the fan may need to be increased, and the power of the refrigeration unit also needs to be increased. The fan control instruction and the power control instruction can be obtained by using the fuzzy product method.

[0151] Specifically, the calculation formula for the fan control command is as follows:

[0152]

[0153] The calculation formula for the power control command is as follows:

[0154]

[0155] Based on the fan control command and the power control command, energy consumption prediction is carried out to obtain the energy consumption prediction result.

[0156] Specifically, the fan speed calculated according to the fan control command and the power of the refrigeration unit calculated according to the power control command are used as input data, and defuzzification is performed on them to convert the fan control command and the power control command into actual control signals. For example, using the centroid method for defuzzification, we get:

[0157]

[0158]

[0159] where and are the membership degrees of the fan speed and the power of the refrigeration unit at each control point respectively, and are the possible values of the fan speed and the power of the refrigeration unit respectively.

[0160] Furthermore, the final control signals and are obtained. The unit is RPM (revolutions per minute), representing the target adjustment speed of the fan; The unit is kW, representing the target adjustment power of the refrigeration unit. The target adjustment speed and the target adjustment power are sent to the fan and the command unit respectively through the control system to perform corresponding adjustments.

[0161] Furthermore, first, the fan diameter adjustment information, the fan configuration adjustment information, the target adjustment speed, the target adjustment power, the mine internal environment data such as temperature and humidity, and other structural layouts and air flow distributions of the ventilation duct system are used as input data.

[0162] Then, the definition of the environmental state and the action space is carried out. The environmental state S(t) represents the environmental regulation in the mine, including information such as temperature, humidity, wind speed, and ventilation load. The environmental state vector S(t) can be expressed as:

[0163] S(t)=[T(t),H(t),V(t), P vent (t) ]

[0164] Among them, T(t) is the temperature in °C; H(t) is the humidity in %; V(t) is the wind speed in m / s; is the ventilation load in m 3 / s.

[0165] The action space A(t) represents the operation decision of the control system at a certain moment, such as the adjustment of the fan speed and the power of the refrigeration unit. The action state vector A(t) can be expressed as:

[0166] A(t)=[∆ N fan (t),∆ P cool (t) ]

[0167] Among them, is the adjustment amount of the fan speed in RPM; is the adjustment amount of the power of the refrigeration unit in kW.

[0168] Furthermore, taking the initial state S(0), the action space A(t), and the feedback reward R(t + 1) as input data, through the interactive training between the control system and the environment, the Q-value function is gradually updated. During the training process, the control system selects an action A(t) according to the current environmental state S(t), and adjusts the control strategy according to the feedback of the environment such as the change of temperature and humidity, so as to optimize the load distribution of the cooling system and reduce the situation of overcooling or uneven cooling.

[0169] Furthermore, taking the environmental state S(t) and the action space A(t) as input data, using the Q-learning algorithm or the deep Q-network model in deep reinforcement learning, through the interaction training between the control system and the environment, the optimal control strategy is learned to optimize the load distribution of the central air-conditioning system. The control system selects an action A(t) according to the current environmental state S(t) and adjusts according to the feedback of the environment. The Q-value update formula is as follows:

[0170] Q(S(t),A(t))=Q(S(t),A(t))+α[R(t+1)+γ Q <none / > <none / > <mprescripts / > A' max (s(t+1),A')-Q(S(t),A(t))]

[0171] Among them, $Q(S(t), A(t))$ represents the Q-value corresponding to the state $S(t)$ and the action $A(t)$, indicating the long-term reward for choosing this action in this state; $\alpha$ is the learning rate, used to control the speed of Q-value update; $\gamma$ is the discount factor, representing the importance of future rewards; $R(t + 1)$ represents the immediate reward obtained by the control system after taking an action in the current state; represents the maximum Q-value for choosing the optimal action in the next state $S(t + 1)$.

[0172] Furthermore, taking the initial state $S(0)$, the action space $A(t)$, and the feedback reward $R(t + 1)$ as input data, through the interactive training of the control system and the environment, the Q-value function is gradually updated. During the training process, the control system selects an action $A(t)$ according to the current environmental state $S(t)$, and adjusts the control strategy according to the feedback of the environment such as the change of temperature and humidity, so as to optimize the load distribution of the cooling system and reduce the situation of overcooling or uneven cooling.

[0173] Furthermore, according to the optimal control measurement output by the deep reinforcement learning model, predict the energy consumption of the central air-conditioning system under different environmental regulations. The calculation formula of the energy consumption is as follows:

[0174]

[0175] where, $E_{total}$ is the total energy consumption, with the unit of kWh; $E_{fan}$ is the energy consumption of the fan, with the unit of kWh; $E_{chiller}$ is the energy consumption of the chiller, with the unit of kWh.

[0176] where, The calculation formula of $E_{fan}$ is:

[0177]

[0178] The calculation formula of $E_{chiller}$ is:

[0179]

[0180] where, $P_{fan}(t)$ is the power of the fan at time $t$, with the unit of kW; $P_{chiller}(t)$ is the power of the chiller at time $t$; $\Delta t$ is the time interval, with the unit of hour. After adjusting the equipment power according to the strategy optimized by the deep reinforcement learning model, the energy consumption in the optimal case can be calculated, and the energy efficiency of the system can be evaluated.

[0181] Furthermore, by optimizing the load distribution of the central air-conditioning system through deep reinforcement learning, the finally obtained energy consumption prediction result can be used to guide the dynamic adjustment of the central air-conditioning system to achieve the maximum energy efficiency.

[0182] The energy consumption prediction result may include: the total system energy consumption , in kWh; the energy consumption of the fan at each moment , in kWh, and the energy consumption of the chiller at each moment , in kWh.

[0183] An embodiment of the present invention also provides an energy-saving prediction device for a central air-conditioning system, as Figure 4 shown, the device includes:

[0184] A target parameter acquisition module 410, configured to acquire target air duct parameters, target environmental parameters, and target device parameters corresponding to the ventilation duct system;

[0185] An environmental prediction function construction module 420, configured to construct an environmental prediction function based on the target air duct parameters, the target environmental parameters, and the target device parameters;

[0186] An environmental simulation module 430, configured to simulate the ventilation duct system and the target environment where the ventilation duct system is located to obtain a fluid simulation function;

[0187] A system simulation module 440, configured to perform system simulation based on the environmental prediction function and the fluid simulation function to obtain device simulation parameters;

[0188] An energy consumption prediction module 450, configured to perform energy consumption prediction based on the device simulation parameters and the environmental prediction function to obtain an energy consumption prediction result.

[0189] In some other embodiments, the target parameter acquisition module 410 includes:

[0190] An initial parameter acquisition module, configured to acquire initial air duct parameters, initial environmental parameters, and initial device parameters corresponding to the ventilation duct system in response to a parameter acquisition instruction;

[0191] A data processing module, configured to perform data preprocessing on the initial air duct parameters, the initial environmental parameters, and the initial device parameters to obtain the target air duct parameters, the target environmental parameters, and the target device parameters.

[0192] In some other embodiments, the environmental prediction function construction module 420 includes:

[0193] A first function construction module, configured to respectively construct a temperature change function, a humidity change function, and a wind speed change function based on the target environmental parameters;

[0194] A second function construction module, configured to construct a first device power function and a second device power function based on the target air duct parameters and the target device parameters;

[0195] A third function construction module, configured to construct the environment prediction function based on the temperature change function, the humidity change function, the wind speed change function, the first device power function, and the second device power function.

[0196] In some other embodiments, the environment simulation module 430 includes:

[0197] A fourth function construction module, configured to determine the fluid flow function based on the flow velocity of air in each air duct of the ventilation duct system in the target environment;

[0198] A fifth function construction module, configured to determine the pressure field function based on the fluid flow function and the pressure distribution parameters in the target environment; and,

[0199] A sixth function construction module, configured to determine the wind speed field function based on the wind speed distribution parameters in the target environment.

[0200] In some other embodiments, the system simulation module 440 includes:

[0201] A parameter acquisition module, configured to acquire the resistance parameters corresponding to the ventilation duct system;

[0202] A resistance value calculation module, configured to calculate the resistance value to be adjusted in the ventilation duct system based on the resistance parameters, the target device parameters, the fluid flow function, and the wind speed field function;

[0203] An information determination module, configured to input the resistance value to be adjusted into the air duct adjustment function and the fan adjustment function respectively, to obtain the air duct diameter adjustment information and the fan configuration adjustment information.

[0204] In some other embodiments, the energy consumption prediction module 450 includes:

[0205] A parameter determination module, configured to determine the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter at the current moment based on the device simulation parameters and the environment prediction function;

[0206] A fuzzy processing module, configured to perform fuzzy processing on the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter, to obtain multiple environment parameter data sets;

[0207] A first prediction module, configured to perform energy consumption prediction based on a preset energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain the energy consumption prediction result.

[0208] In another embodiment, the first prediction module further includes:

[0209] An instruction acquisition module, configured to acquire a preset energy consumption adjustment instruction;

[0210] An instruction generation module, configured to associate the predicted energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain a fan control instruction and a power control instruction;

[0211] A second prediction module, configured to perform energy consumption prediction based on the fan control instruction and the power control instruction to obtain the energy consumption prediction result.

[0212] The device in the device embodiment and the method embodiment are based on the same inventive concept and are used to implement the above-mentioned energy-saving prediction method for a central air-conditioning system.

[0213] An embodiment of the present invention further provides an electronic device, where the electronic device includes: a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the energy-saving prediction method for a central air-conditioning system as described in any one of the method embodiments.

[0214] An embodiment of the present invention further provides a computer storage medium, which can be set in a server to store at least one instruction, at least one program, a code set, or an instruction set for implementing the method embodiment. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the energy-saving prediction method for a central air-conditioning system as described in any one of the method embodiments.

[0215] Optionally, in an embodiment of the present invention, the above storage medium may be located in at least one of multiple network servers in a computer network. Optionally, in an embodiment of the present invention, the above storage medium may include, but is not limited to: a USB flash drive, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk, or an optical disc and other media that can store program codes.

[0216] As can be seen from the embodiments provided by the present invention described above, in the present invention, by obtaining the target air duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system, and then based on the target air duct parameters, target environmental parameters, and target equipment parameters, constructing an environmental prediction function and a fluid simulation function, the correlation between environmental changes and system operation can be established to improve the correlation degree between the two; further, based on the environmental prediction function and the fluid simulation function, system simulation is carried out to obtain equipment simulation parameters, and according to the equipment simulation parameters, the structural layout and parameters of the ventilation duct system can be effectively adjusted, so as to effectively adjust the ventilation duct system and improve the operation efficiency of the central air conditioning system; based on the equipment simulation parameters and the environmental prediction function, energy consumption prediction is carried out to obtain the energy consumption prediction result, so that the central air conditioning system can be adjusted to the optimal state in time when there is a sudden temperature change or an increase in ventilation demand, thus avoiding energy waste and discomfort in the working environment of miners.

[0217] It should be noted that: the above has described various embodiments of the present disclosure. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art corresponding to the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the embodiments disclosed herein.

Claims

1. An energy-saving prediction method for a central air-conditioning system based on simulation, characterized in that The method includes: Obtaining target air duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system; Constructing an environment prediction function based on the target air duct parameters, the target environmental parameters, and the target equipment parameters; Simulating the ventilation duct system and the target environment where the ventilation duct system is located to obtain a fluid simulation function; Performing system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters; Performing energy consumption prediction based on the equipment simulation parameters and the environment prediction function to obtain an energy consumption prediction result; The fluid simulation function includes a fluid flow function, a pressure field function, and a wind speed field function; The step of simulating the ventilation duct system and the target environment where the ventilation duct system is located to obtain a fluid simulation function includes: Determining the fluid flow function based on the flow velocity of air in each ventilation duct of the ventilation duct system in the target environment; Determining the pressure field function based on the fluid flow function and the pressure distribution parameters in the target environment; and Determining the wind speed field function based on the wind speed distribution parameters in the target environment; The equipment simulation parameters include air duct diameter adjustment information and fan configuration adjustment information; The step of performing system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters includes: Obtaining the resistance parameters corresponding to the ventilation duct system; Calculating the resistance value to be adjusted in the ventilation duct system based on the resistance parameters, the target equipment parameters, the fluid flow function, and the wind speed field function; Inputting the resistance value to be adjusted into an air duct adjustment function and a fan adjustment function respectively to obtain the air duct diameter adjustment information and the fan configuration adjustment information.

2. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1, wherein The step of obtaining target air duct parameters, target environmental parameters, and target equipment parameters corresponding to the ventilation duct system includes: In response to a parameter acquisition instruction, obtaining initial air duct parameters, initial environmental parameters, and initial equipment parameters corresponding to the ventilation duct system; Performing data preprocessing on the initial air duct parameters, the initial environmental parameters, and the initial equipment parameters to obtain the target air duct parameters, the target environmental parameters, and the target equipment parameters.

3. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1, characterized in that, The step of constructing an environment prediction function based on the target air duct parameters, the target environmental parameters, and the target equipment parameters includes: Respectively constructing a temperature change function, a humidity change function, and a wind speed change function based on the target environmental parameters; Constructing a first equipment power function and a second equipment power function based on the target air duct parameters and the target equipment parameters; Constructing the environment prediction function based on the temperature change function, the humidity change function, the wind speed change function, the first equipment power function, and the second equipment power function.

4. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1, wherein The step of performing energy consumption prediction based on the equipment simulation parameters and the environment prediction function to obtain an energy consumption prediction result includes: Determine the current temperature parameter, current humidity parameter, current wind speed parameter, and ventilation load parameter at the current moment based on the device simulation parameters and the environment prediction function; Perform fuzzy processing on the current temperature parameter, the current humidity parameter, the current wind speed parameter, and the ventilation load parameter to obtain multiple environment parameter data sets; Perform energy consumption prediction based on the preset energy consumption adjustment instruction and the multiple environment parameter data sets to obtain the energy consumption prediction result.

5. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 4, wherein The performing energy consumption prediction based on the preset energy consumption adjustment instruction and the multiple environment parameter data sets to obtain the energy consumption prediction result includes: Obtain the preset energy consumption adjustment instruction; Associate the preset energy consumption adjustment instruction and the multiple environment parameter data sets to obtain a fan control instruction and a power control instruction; Perform energy consumption prediction based on the fan control instruction and the power control instruction to obtain the energy consumption prediction result.

6. An energy-saving prediction device for a central air-conditioning system, which is applied to the method described in any one of claims 1-5, and is characterized in that, The device includes: A target parameter acquisition module, configured to acquire target air duct parameters, target environment parameters, and target device parameters corresponding to the ventilation duct system; An environment prediction function construction module, configured to construct an environment prediction function based on the target air duct parameters, the target environment parameters, and the target device parameters; An environment simulation module, configured to perform environment simulation on the target environment where the ventilation duct system is located to obtain a fluid simulation function corresponding to the target environment; A system simulation module, configured to perform system simulation based on the environment prediction function and the fluid simulation function to obtain device simulation parameters; An energy consumption prediction module, configured to perform energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory. At least one instruction and at least one program segment are stored in the memory. The at least one instruction and the at least one program segment are loaded and executed by the processor to implement the energy-saving prediction method for a central air-conditioning system according to any one of claims 1-5.

8. A computer storage medium, characterized in that, At least one instruction and at least one program segment are stored in the computer storage medium. The at least one instruction and the at least one program segment are loaded and executed by a processor to implement the energy-saving prediction method for a central air-conditioning system according to any one of claims 1-5.

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