Central air-conditioning system energy-saving prediction method based on analogue simulation

Abstract

The invention discloses a central air-conditioning system energy-saving prediction method based on analogue simulation. The method comprises the steps that target air duct parameters, target environment parameters and target equipment parameters corresponding to a ventilation pipeline system are obtained; based on the target air duct parameter, the target environment parameter and the target equipment parameter, constructing an environment prediction function; environment simulation is conducted on a target environment where the ventilation pipeline system is located, and a fluid simulation function corresponding to the target environment is obtained; performing system simulation based on the environment 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 environment prediction function to obtain an energy consumption prediction result. According to the method, energy consumption prediction is realized by combining the environmental parameters and the equipment parameters, the accuracy and the real-time performance of energy consumption prediction can be improved, then relevant parameters of the ventilation pipeline system can be adjusted in time according to environmental changes, and the ventilation quality is improved.

Description

Technical Field

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

[0002] Underground mines, especially deep mines with a depth of more than 1,000 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 system needs to effectively control the temperature and humidity to ensure the safety and comfort of miners. To this end, 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] At present, energy consumption optimization of mine ventilation and refrigeration systems mostly relies on physical models and simulation technologies, which usually establish the relationship between factors such as temperature and humidity, air flow, and equipment power.

[0004] Existing systems often have a lag response problem, especially the slow adjustment speed of traditional fans and refrigeration units, which fail to respond quickly to changes in the underground environment. For example, when there is a sudden temperature change or increased ventilation demand, the system cannot be adjusted to the optimal state in time, resulting in energy waste and discomfort to the miners' working environment. 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] In order to solve at least one of the above-mentioned technical problems, the present invention proposes an energy-saving prediction method for a central air-conditioning system based on simulation.

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

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

[0009] Constructing an environment prediction function based on the target air duct parameters, the target environment parameters, and the target device parameters;

[0010] Simulating the ventilation duct system and the target environment in which the ventilation duct system is located to obtain a fluid simulation function;

[0011] Perform system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters;

[0012] Energy consumption prediction is performed based on the equipment simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

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

[0014] In response to the parameter acquisition instruction, initial air duct parameters, initial environmental parameters and initial equipment parameters corresponding to the ventilation duct system are acquired;

[0015] The initial air duct parameters, the initial environment parameters and the initial equipment parameters are subjected to data preprocessing to obtain the target air duct parameters, the target environment parameters and the target equipment parameters.

[0016] In some possible embodiments, constructing an environment prediction function based on the target environment parameter and the target device parameter includes:

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

[0018] Constructing a first device power function and a second device power function based on the target air duct parameters and the target device parameters;

[0019] The environment prediction function is constructed 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.

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

[0021] 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;

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

[0023] The wind speed field function is determined based on the wind speed distribution parameters in the target environment.

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

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

[0026] Calculating a 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] The resistance value to be adjusted is input 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, performing energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result includes:

[0029] Based on the equipment simulation parameters and the environmental 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] Performing 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;

[0031] Energy consumption prediction is performed based on the preset energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain the energy consumption prediction result.

[0032] In some possible embodiments, 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 includes:

[0033] Obtain preset energy consumption adjustment instructions;

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

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

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

[0037] A target parameter acquisition module is used to acquire target air duct parameters, target environmental parameters and target equipment parameters corresponding to the ventilation duct system;

[0038] An environment prediction function construction module, used to construct an environment prediction function based on the target air duct parameters, the target environment parameters and the target device parameters;

[0039] An environmental simulation module, used to perform environmental simulation on a target environment in which the ventilation duct system is located, and obtain a fluid simulation function corresponding to the target environment;

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

[0041] The energy consumption prediction module is used 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, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program 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, a computer storage medium is provided, in which at least one instruction and at least one program are stored, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the energy-saving prediction method for a central air-conditioning system as described above.

[0044] The implementation of the present invention has the following beneficial effects:

[0045] In the present invention, by acquiring the target air duct parameters, target environmental parameters and target equipment parameters corresponding to the ventilation duct system, and then constructing an environmental prediction function and a fluid simulation function based on the target air duct parameters, target environmental parameters and target equipment parameters, a correlation relationship between environmental changes and system operation can be established to improve the correlation between the two; further, system simulation is performed based on the environmental prediction function and the fluid simulation function to obtain equipment simulation parameters, and the structural layout and parameters of the ventilation duct system can be effectively adjusted according to the equipment simulation parameters, thereby effectively adjusting the ventilation duct system and improving the operating efficiency of the central air-conditioning system; energy consumption prediction is performed based on the equipment simulation parameters and the environmental prediction function to obtain energy consumption prediction results, 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 miners' working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A schematic diagram of a flow chart corresponding to a method for predicting energy saving of a central air-conditioning system based on simulation provided in an embodiment of the present invention;

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

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

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

[0051] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are 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 accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0054] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0055] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0056] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0057] Figure 1 The flowchart corresponding to the energy-saving prediction method for a central air-conditioning system based on simulation provided in the embodiment of the present invention 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, so as to realize the real-time prediction of the energy consumption of the central air-conditioning system by combining the environmental data inside the specific mine and the structural data of the specific ventilation duct system, and then adjust the ventilation of the central air-conditioning system in real time based on the prediction results. Figure 1 As shown, the energy-saving prediction method of the central air-conditioning system based on simulation includes:

[0058] Step S101: acquiring target air duct parameters, target environment parameters, and target equipment parameters corresponding to the ventilation duct system;

[0059] In a specific embodiment, a variety of sensors can be installed to collect real-time environmental data such as temperature, humidity, wind speed, etc. of underground mines, i.e., 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 can be collected, i.e., target duct parameters; and equipment parameters such as fans and refrigeration units in the central air-conditioning system can be collected, i.e., target equipment parameters.

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

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

[0062] Specifically, it is necessary to select appropriate sensors to collect data about the mine environment. Temperature, humidity, wind speed, air pressure, airflow direction and other sensors are commonly used. Depending on the depth of the mine, environmental conditions and equipment requirements, the following types of sensors are selected: temperature sensors to monitor temperature changes in different areas of the mine; humidity sensors to measure relative humidity in the mine; wind speed sensors to detect wind speed and airflow direction; and air pressure sensors to monitor changes in air pressure.

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

[0064] At the same time, it is necessary to collect the basic structural information of each air duct in the ventilation duct system, such as the air duct diameter D, the air duct curvature β, etc. as the 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 equipment. 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 the optimization of the central air-conditioning system. For fans, refrigeration units and other equipment, their power, working status, efficiency and other information need to be collected as initial equipment parameters.

[0065] The initial air duct parameters, the initial environment parameters and the initial equipment parameters are subjected to data preprocessing to obtain the target air duct parameters, the target environment 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 sensor data and device 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: constructing an environment prediction function based on the target air duct parameters, the target environment parameters and the target device parameters;

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

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

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

[0071] In a specific embodiment, the target environment parameters may be considered to include the following parameters used in the function construction process, such as temperature parameters, humidity parameters, and wind speed parameters.

[0072] The temperature change is not only affected by the external air input and equipment cooling, but also closely related to the heat load in the mine (such as mining operations, equipment heating, etc.). Assume that the heat conduction and convection inside the mine can be expressed by a simple thermodynamic model, namely the temperature change function. The temperature change function is:

[0073]

[0074] Among them, 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 input air, in watts; The heat output in the mine, such as the heat removed by the fan, is in W; is the heat load inside the mine, in W.

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

[0076]

[0077] Among them, 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 moisture flowing out of the air, in g / s; The heat released by the condensation of water vapor, unit is W.

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

[0079]

[0080] Among them, V is the wind speed in the mine at time t, in m / s; is the dynamic response parameter of wind speed in the mine, the unit is kg / (m*s); is the energy input to the fan, in W; is the energy of air outflow, in W.

[0081] Constructing a first device power function and a second device power function based on the target air duct parameters and the target device parameters;

[0082] In a specific embodiment, the target device parameters may be considered to include the parameters used in the following function construction process, such as fan efficiency, refrigeration unit efficiency, etc. In the mine ventilation and refrigeration system, 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 refrigeration unit and the energy consumption of the fan.

[0083] The energy transfer function of the system is further established 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. The first equipment power function can be used to describe the relationship between the power output of the fan and factors such as wind speed and air duct diameter. The second equipment power function can be used to describe the relationship between the power of the refrigeration unit and the cooling capacity.

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

[0085]

[0086] in, is the power of the fan at time t, in W; is the efficiency of the fan, and the unit is dimensionless (0-1); is the air density, in kg / m 3 ; is the cross-sectional area of ​​the air duct, in m 2 ; is the wind speed in m / s.

[0087] The second device power function is:

[0088]

[0089] in, is the cooling capacity of the refrigeration unit at time t, in W; is the efficiency of the refrigeration unit, and the unit is dimensionless (0-1); is the power of the refrigeration unit, in W.

[0090] The environment prediction function is constructed 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 an environmental prediction function, which can reflect the temperature and humidity change trend, the time required for the temperature and humidity to adjust to a stable state, and the total energy consumption of the fan and refrigeration unit. The environmental prediction function can be:

[0092]

[0093] in, is the total energy consumption of the ventilation duct system at time t, in J joules; is the power of the fan, in W; is the power of the refrigeration unit, in W.

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

[0095] In a specific embodiment, the ventilation duct system and its target environment are simulated in combination with CFD simulation, and the obtained fluid simulation function is used to calculate the optimized ventilation duct system layout, airflow distribution (wind speed, pressure field) and optimized configuration (duct size, fan power).

[0096] Computational fluid dynamics (CFD) methods are used to simulate the complex ventilation network of underground mines and analyze the air flow path, wind speed distribution, and pipe resistance. Combined with the above environmental prediction functions, the layout of the ventilation ducts, duct size, and fan configuration are optimized to improve the overall ventilation efficiency.

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

[0098] Establish a three-dimensional geometric model: Construct a ventilation duct system model inside the mine in CFD software (such as ANSYS Fluent, COMSOL, etc.). The model includes various air ducts, branch pipes, 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 wind speed, outlet pressure, equipment power, etc.) of each duct area, i.e., the fluid simulation function. The environmental simulation of the target environment in which the ventilation duct system is located is performed to obtain a fluid simulation function corresponding to the target environment, including:

[0100] 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;

[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 in kg / m 3 ; P is air pressure, unit is Pa; R is the gas constant of air, R=287.1J (kg / cdotpK); T is temperature, unit is K Kelvin.

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

[0105]

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

[0107] Boundary conditions include wind speed and pressure of fan and refrigeration unit input, boundary conditions of pipe interface, and outlet pressure of exhaust port.

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

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

[0110]

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

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

[0113] In a specific embodiment, the wind speed distribution parameters of each point inside the mine are obtained through CFD simulation to obtain a wind speed field function, which can be expressed as:

[0114]

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

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

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

[0118] Figure 2 A schematic diagram of a flow chart corresponding to the system simulation provided in an embodiment of the present invention is shown in FIG. Figure 2 As shown, step S104 may include:

[0119] Step S201: obtaining a resistance parameter corresponding to the ventilation duct system;

[0120] In a specific embodiment, the resistance of each pipe in the ventilation pipe system may be calculated first. The pipe resistance is mainly composed of local resistance and friction resistance. The resistance parameter may be a friction resistance parameter.

[0121] Step S202: calculating 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 also include the pipe length and the pipe diameter; the resistance value to be adjusted can be calculated by the following formula:

[0123]

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

[0125] Step S203: inputting 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.

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

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

[0128]

[0129] in, is the adjusted air duct diameter, in m; is the pipeline resistance, in Pa; L is the pipeline length, in m; ρ is the air density determined based on the fluid flow function, in kg / m 3 ; v is the wind speed determined based on the wind speed field function, in 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] in, is the adjusted fan power, in W; is the fan efficiency, unit is dimensionless; A is the cross-sectional area of ​​the air duct, unit is ; is the optimized wind speed, in m / s.

[0133] Furthermore, after obtaining the duct diameter adjustment information and fan configuration adjustment information, simulation verification can be performed again to ensure that the overall ventilation efficiency is improved and energy consumption is reduced. The verification results should include energy consumption comparison before and after optimization, system response speed, temperature and humidity control effects, etc.

[0134] Step S105: performing energy consumption prediction based on the equipment simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

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

[0136] Figure 3 A schematic diagram of a process flow corresponding to energy consumption prediction provided by an embodiment of the present invention, such as Figure 3 As shown, the energy consumption prediction is performed based on the device simulation parameters and the environment prediction function to obtain the energy consumption prediction result, that is, step S105 may include:

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

[0138] In a specific embodiment, the fan diameter adjustment information and the fan configuration adjustment information as well as the real-time environmental data can be first obtained, wherein the real-time environmental data can 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] A fuzzy rule base is established based on changes in real-time environmental data. The rules usually set are such as "if the temperature is high and the humidity is low, increase the fan speed", and so on. Multiple fuzzy rules need to be set according to actual needs to define the behavior of equipment adjustment.

[0140] Step S302: performing 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 converted into fuzzy sets, i.e., multiple environmental parameter data sets, through a fuzzification function. Specifically, the current temperature parameter T(t) can be divided into three fuzzy sets of low temperature, medium temperature and high temperature, the current humidity parameter H(t) can be divided into three fuzzy sets of low humidity, medium humidity and high humidity, and the current wind speed parameter V(t) can be divided into three fuzzy sets of 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] in, The low temperature threshold and high temperature threshold are set; The membership degree corresponding to temperature T is the fuzzy result of the current temperature parameter, which indicates the degree of membership of temperature in the "low temperature" fuzzy set.

[0145] Similarly, the current humidity parameter H(t) and the current wind speed parameter V(t) may be fuzzy processed.

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

[0147] Specifically, the energy consumption prediction is performed 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 preset energy consumption adjustment instructions;

[0149] Associating 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, multiple environmental parameter data sets (i.e., the membership degree corresponding to each parameter) are , as well as As input data, the preset energy consumption adjustment instructions for controlling the fan and refrigeration unit are 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", the fan speed It may be necessary to increase the power of the refrigeration unit It is also necessary to increase that the fuzzy product method can be used to obtain the fan control instructions and the power control instructions.

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

[0152]

[0153] The calculation formula of power control command is:

[0154]

[0155] Energy consumption prediction is performed based on the fan control instruction and the power control instruction to obtain the energy consumption prediction result.

[0156] Specifically, the fan speed calculated according to the fan control instruction And according to the power control command The calculated refrigeration unit power is used as input data, which is defuzzified to convert the fan control command and power control command into actual control signals. For example, the centroid method is used for defuzzification to obtain:

[0157]

[0158]

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

[0160] Then we get the final control signal. and , The unit is RPM revolutions per minute, indicating the target speed of the fan. The unit is kW, which represents the target power of the refrigeration unit. The target speed and target power are sent to the fan and the command unit respectively through the control system to perform corresponding adjustments.

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

[0162] Next, we define the environmental state and action space. The environmental state S(t) represents the environmental adjustment in the mine, including temperature, humidity, wind speed, ventilation load and other information. The environmental state vector S(t) can be expressed as:

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

[0164] Where, T(t) is temperature, unit is ℃; H(t) is humidity, unit is %; V(t) is wind speed, unit is m / s; is the ventilation load, in m 3 / s.

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

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

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

[0168] Furthermore, the initial state S(0), action space A(t) and feedback reward R(t+1) are used as input data, and the Q-value function is gradually updated through interactive training between the control system and the environment. 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 environmental feedback such as changes in temperature and humidity, thereby optimizing the load distribution of the cooling system and reducing overcooling or uneven cooling.

[0169] Furthermore, the environment state S(t) and action space A(t) are used as input data, and the Q learning algorithm or deep Q network model in deep reinforcement learning is used to learn the optimal control strategy by training the interaction between the control system and the environment to optimize the load distribution of the central air-conditioning system. The control system selects an action A(t) according to the current environment state S(t) and adjusts it according to the feedback from 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] in, is the Q value corresponding to the state S(t) and the action A(t), indicating the long-term reward of choosing this action in this state; α is the learning rate, which is used to control the speed of Q value update; γ is the discount factor, indicating 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; It represents the maximum Q value for selecting the optimal action in the next state S(t+1).

[0172] Furthermore, the initial state S(0), action space A(t) and feedback reward R(t+1) are used as input data, and the Q-value function is gradually updated through interactive training between the control system and the environment. 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 environmental feedback such as changes in temperature and humidity, thereby optimizing the load distribution of the cooling system and reducing overcooling or uneven cooling.

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

[0174]

[0175] in, is the total energy consumption in kWh; is the energy consumption of the fan, in kWh; is the energy consumption of the refrigeration unit in kWh.

[0176] in, The calculation formula is:

[0177]

[0178] The calculation formula is:

[0179]

[0180] in, is the power of the fan at time t, in kW; is the power of the refrigeration unit at time t; is the time interval in hours. After adjusting the device power according to the strategy optimized by the deep reinforcement learning model, the energy consumption in the optimal case can be calculated and the system energy efficiency can be evaluated.

[0181] Furthermore, the load distribution of the central air-conditioning system is optimized through deep reinforcement learning, and the energy consumption prediction result is finally obtained. It can be used to guide the dynamic adjustment of central air-conditioning systems to maximize energy efficiency.

[0182] Energy consumption forecast results can include: system total energy consumption , in kWh; fan energy consumption at each moment , in kWh, and the energy consumption of the refrigeration unit at each moment , unit is kWh.

[0183] The embodiment of the present invention also provides a central air conditioning system energy saving prediction device, such as Figure 4 As shown, the device comprises:

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

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

[0186] An environment simulation module 430 is used to simulate the ventilation duct system and the target environment in which the ventilation duct system is located to obtain a fluid simulation function;

[0187] A system simulation module 440 is used to perform system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters;

[0188] The energy consumption prediction module 450 is used to perform energy consumption prediction based on the device simulation parameters and the environment 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, used to obtain initial air duct parameters, initial environmental parameters and initial equipment parameters corresponding to the ventilation duct system in response to a parameter acquisition instruction;

[0191] The data processing module is used to 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.

[0192] In some other embodiments, the environment prediction function building module 420 includes:

[0193] A first function construction module is used to 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, used 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] The third function construction module is used 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 building module, configured to determine the fluid flow function based on a flow velocity of air in each ventilation duct in the ventilation duct system in the target environment;

[0198] a fifth function construction module, for determining the pressure field function based on the fluid flow function and the pressure distribution parameter in the target environment; and

[0199] The sixth function construction module is used 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, used to acquire 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 parameter, the target device parameter, the fluid flow function and the wind speed field function;

[0203] The information determination module is used 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, used to determine a current temperature parameter, a current humidity parameter, a current wind speed parameter and a ventilation load parameter at a current moment based on the device simulation parameter and the environmental prediction function;

[0206] A fuzzy processing module, used for performing 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;

[0207] The first prediction module is used 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, used to acquire preset energy consumption adjustment instructions;

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

[0211] The second prediction module is used 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 and method embodiments in the device embodiment are based on the same inventive concept and are used to implement the above-mentioned central air-conditioning system energy-saving prediction method.

[0213] An embodiment of the present invention also provides an electronic device, comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, 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 an 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 also 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 central air-conditioning system energy-saving prediction method as described in any one of the method embodiments.

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

[0216] It can be seen from the embodiments provided by the present invention that, 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 constructing an environmental prediction function and a fluid simulation function based on the target air duct parameters, target environmental parameters and target equipment parameters, it is possible to establish a correlation between environmental changes and system operation to improve the correlation between the two; further, based on the environmental prediction function and the fluid simulation function, a system simulation is performed 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, thereby effectively adjusting the ventilation duct system and improving the operating efficiency of the central air-conditioning system; based on the equipment simulation parameters and the environmental prediction function, energy consumption prediction is performed to obtain energy consumption prediction results, 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 miners' working environment.

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

Claims

1. A central air conditioning system energy saving prediction method based on simulation, characterized in that: The method comprises: 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 environment parameters, and the target device parameters; Simulating the ventilation duct system and the target environment in which the ventilation duct system is located to obtain a fluid simulation function; Perform system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters; Energy consumption prediction is performed based on the equipment simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

2. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1 is characterized in that: 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 the parameter acquisition instruction, initial air duct parameters, initial environmental parameters and initial equipment parameters corresponding to the ventilation duct system are acquired; The initial air duct parameters, the initial environment parameters and the initial equipment parameters are subjected to data preprocessing to obtain the target air duct parameters, the target environment 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 is characterized in that: The step of constructing an environment prediction function based on the target air duct parameter, the target environment parameter, and the target device parameter includes: Based on the target environmental parameters, a temperature change function, a humidity change function and a wind speed change function are respectively constructed; Constructing a first device power function and a second device power function based on the target air duct parameters and the target device parameters; The environment prediction function is constructed 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.

4. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1 is characterized in that: The fluid simulation function includes a fluid flow function, a pressure field function and a wind speed field function; The simulating of the ventilation duct system and the target environment in which 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, The wind speed field function is determined based on the wind speed distribution parameters in the target environment.

5. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 4 is characterized in that: The equipment simulation parameters include air duct diameter adjustment information and fan configuration adjustment information; The performing of system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters includes: Obtaining resistance parameters corresponding to the ventilation duct system; Calculating a 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; The resistance value to be adjusted is input 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.

6. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 1 is characterized in that: The performing energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result includes: Based on the equipment simulation parameters and the environmental 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; Performing 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; Energy consumption prediction is performed based on the preset energy consumption adjustment instruction and the multiple environmental parameter data sets to obtain the energy consumption prediction result.

7. The energy-saving prediction method for a central air-conditioning system based on simulation according to claim 6 is characterized in that: The energy consumption prediction is performed based on the preset energy consumption adjustment instruction and the plurality of environmental parameter data sets to obtain the energy consumption prediction result, including: Obtaining the preset energy consumption adjustment instruction; Associating the preset energy consumption adjustment instruction with the multiple environmental parameter data sets to obtain a fan control instruction and a power control instruction; Energy consumption prediction is performed based on the fan control instruction and the power control instruction to obtain the energy consumption prediction result.

8. A central air conditioning system energy-saving prediction device, characterized in that: The device comprises: A target parameter acquisition module is used to acquire target air duct parameters, target environmental parameters and target equipment parameters corresponding to the ventilation duct system; An environment prediction function construction module, used to construct an environment prediction function based on the target air duct parameters, the target environment parameters and the target device parameters; An environmental simulation module, used to perform environmental simulation on a target environment in which the ventilation duct system is located, and obtain a fluid simulation function corresponding to the target environment; A system simulation module, used to perform system simulation based on the environment prediction function and the fluid simulation function to obtain equipment simulation parameters; The energy consumption prediction module is used to perform energy consumption prediction based on the device simulation parameters and the environment prediction function to obtain an energy consumption prediction result.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the energy-saving prediction method for the central air-conditioning system as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores at least one instruction and at least one program, and the at least one instruction and the at least one program are 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 claims 1-7.

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