Air source heat pump control system

Abstract

The invention discloses an air source heat pump control system, and particularly relates to the technical field of air source heat pumps, which comprises the following steps: providing an operation parameter intelligent control module, modeling each component of an air source heat pump, outputting a mathematical model of each component, and verifying the reliability; the mathematical models of all the components form an air source heat pump simulation model, and a compressor rotating speed prediction value is output based on the air source heat pump simulation model; a rotating speed control instruction is generated based on the compressor rotating speed prediction value, and the air source heat pump compressor is used for receiving and executing the instruction; the difference between the predicted heat supply coefficient and the actual heat supply coefficient is monitored in real time, and measures are taken; when the difference value between the predicted heat supply coefficient and the actual heat supply coefficient exceeds a threshold value TH1, early warning is carried out; the problems that in the prior art, real-time sensing and self-adaptive adjusting capacity for environment changes are lacked, and the optimal energy-saving effect cannot be achieved are effectively solved. And meanwhile, an evaporator assembly in the air source heat pump is prevented from frosting by providing a frost prevention control module.

Description

Technical Field

[0001] The present invention relates to the technical field of air source heat pumps, and more specifically, to an air source heat pump control system. Background Art

[0002] Air source heat pump is an energy-saving device that allows heat to flow from a low-level heat source to a high-level heat source. It is a form of heat pump. The air source heat pump control system is responsible for controlling the operating state of the heat pump to ensure its efficient and stable operation. By monitoring and adjusting various parameters of the heat pump (such as pressure, temperature, flow, etc.), the intelligent control of the heat pump unit is realized.

[0003] However, in actual use, it still has many shortcomings. For example, the operating environment of the air source heat pump is often changeable, which directly affects the working efficiency of the heat pump; if the heat pump maintains fixed operating parameters and cannot be adjusted in time according to environmental changes, it will lead to unstable heating coefficient, which not only affects user comfort but also reduces energy utilization efficiency; traditional air source heat pump control systems often perform simple on-off control based on preset thresholds, lack real-time perception and adaptive adjustment capabilities of environmental changes, cannot fully tap the potential of the heat pump, and cannot achieve the best energy-saving effect. Summary of the invention

[0004] In order to overcome the above defects of the prior art, the present invention provides an air source heat pump control system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: an air source heat pump control system, comprising:

[0006] The operating parameter intelligent control module uses machine learning to mine and analyze the historical operating data of the air source heat pump, and outputs the predicted value of the compressor speed and the corresponding predicted heating coefficient;

[0007] The operation parameter intelligent control module includes: an air source heat pump simulation model building unit, an operation parameter intelligent prediction unit and an operation parameter execution unit;

[0008] The air source heat pump simulation model building unit is used to model each component of the air source heat pump, output the mathematical model of each component and verify the reliability; the mathematical model of each component constitutes the air source heat pump simulation model, and each component at least includes a compressor, an expansion valve, an evaporator, a condenser, and a load;

[0009] The operating parameter intelligent prediction unit outputs the predicted value of the compressor speed based on the air source heat pump simulation model;

[0010] An operating parameter execution unit generates a speed control instruction based on the predicted value of the compressor speed, and the air source heat pump compressor is used to receive the instruction and execute it;

[0011] The monitoring and early warning module monitors the difference between the predicted heating coefficient and the actual heating coefficient in real time and takes measures; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1, a first-level warning is issued; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1 and the duration exceeds the threshold TH2, a second-level warning is issued.

[0012] Preferably, the air source heat pump simulation model building unit includes:

[0013] According to the principles of thermodynamics, fluid mechanics and the ideal gas state equation, the heat conduction characteristics of the compressor, expansion valve, condenser, evaporator and load of the air source heat pump are analyzed, and the mathematical model of each component is output;

[0014] Connect the mathematical models of each component to construct a simulation mathematical model of the air source heat pump;

[0015] Verify the mathematical model of each component respectively, and obtain the measured data and predicted data of the mathematical model of each component;

[0016] The mathematical model parameters are continuously adjusted and optimized until the predicted results of the air source heat pump simulation mathematical model and the actual situation meet the preset values ​​of the loss function.

[0017] Preferably, the predicted heating coefficient is obtained as follows: based on an air source heat pump simulation model, different compressor speed values ​​are input for simulation operation, and in each simulation operation, the corresponding heating coefficient is calculated and recorded; through machine learning and optimization algorithms, the predicted value of the compressor speed that makes the heating coefficient reach the maximum value is found, and the predicted value of the compressor speed and the corresponding predicted heating coefficient are output.

[0018] Preferably, the actual heating coefficient The calculation model satisfies the following formula:

[0019]

[0020] in, Indicates The value of the air source heat pump heating capacity during the time period, Indicates The value of the air source heat pump's self-consumption during the time period;

[0021] The method for obtaining the heating capacity of the air source heat pump is as follows:

[0022]

[0023] in, Indicates Heating power of air source heat pump during the time period, is the heating time interval;

[0024] The method for obtaining the heating power of the air source heat pump is: obtaining the specific heat capacity of the cooling medium , get the flow rate of cooling medium per unit time , the temperature difference of the cooling medium is recorded as ; Through the formula Calculate the heating power of the air source heat pump .

[0025] Preferably, the operation process of the operating parameter intelligent control module includes the following steps:

[0026] Step S11: constructing a nonlinear prediction model of an air source heat pump based on the established air source heat pump simulation model;

[0027] Step S12: Process the nonlinear prediction model of the air source heat pump through the Taylor formula, linearize the nonlinear prediction model of the air source heat pump near a given working point, and output the linearized prediction model; regularly update the working point to ensure the accuracy of the linearized prediction model;

[0028] Step S13, discretizing the linearized prediction model and outputting the discretized prediction model; embedding the discretized prediction model into the control chip to realize the prediction control of the air source heat pump;

[0029] Step S14: according to the optimization objective function and the constraint conditions, the predicted value of the compressor speed is solved, and the predicted value of the compressor speed is applied to the air source heat pump to realize the predictive control of the air source heat pump.

[0030] Preferably, the actual heating coefficient The calculation model satisfies the following formula:

[0031]

[0032] in, Indicates The value of the air source heat pump heating capacity during the time period, Indicates The value of the air source heat pump's self-consumption during the time period;

[0033] The method for obtaining the heating capacity of the air source heat pump is as follows:

[0034]

[0035] in, Indicates Heating power of air source heat pump during the time period, is the heating time interval;

[0036] The method for obtaining the heating power of the air source heat pump is: obtaining the specific heat capacity of the cooling medium , get the flow rate of cooling medium per unit time , the temperature difference of the cooling medium is recorded as ; Through the formula Calculate the heating power of the air source heat pump .

[0037] Preferably, the air source heat pump control system further includes an anti-frost control module.

[0038] The anti-frost control module is used to prevent the evaporator components in the air source heat pump from frosting and is responsible for controlling the start and stop of the defrosting operation of the air source heat pump;

[0039] When the surface temperature of the outdoor heat exchanger is lower than the dew point and freezing point of the ambient air, the defrost operation is triggered;

[0040] When the surface temperature of the outdoor heat exchanger is not lower than the dew point and freezing point of the ambient air, the environmental parameters are monitored in real time to determine whether there is a frost risk scenario and make corresponding control decisions accordingly, including but not limited to starting the defrost preheating operation in advance, adjusting the heat pump operation mode, and changing the outdoor unit wind speed or direction.

[0041] Preferably, the frost risk coefficient is obtained by real-time monitoring of environmental parameters. When the frost risk coefficient exceeds a preset value, it is determined that the current scene is in a frost risk scenario. The frost risk coefficient is obtained in the following manner:

[0042] Get the ambient temperature, ambient humidity and return water temperature, respectively, , , ;

[0043] Get the dew point temperature and freezing point temperature of the ambient air, denoted as , ;

[0044] The frost risk factor FRI is calculated by the following formula:

[0045] in, Used to adjust the sensitivity of the ambient temperature to the risk of frost. Used to adjust the sensitivity of the ambient humidity to the risk of frost. Used to adjust the sensitivity of the return water temperature to the risk of frost formation; It represents the ambient temperature threshold. When the ambient temperature is lower than the threshold, the risk of frost increases significantly. RH0 represents the ambient humidity threshold. When the ambient humidity is higher than the threshold, the risk of frost increases.

[0046] Preferably, the system further comprises:

[0047] The operation data acquisition module is responsible for collecting various operation data of the air source heat pump, including at least compressor operation data, defrost execution data and heating coefficient time series data;

[0048] The compressor operation data analysis module extracts the compressor speed prediction value and the execution value from the compressor operation data, and uses the difference between the compressor speed prediction value and the execution value to represent the compressor control performance abnormality index;

[0049] The defrost execution data analysis module analyzes the defrost execution data and outputs the defrost control performance abnormality index;

[0050] The heating coefficient time series data analysis module analyzes the heating coefficient time series data and outputs the energy-saving control abnormality index;

[0051] The operation risk judgment and early warning module sets the thresholds of the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index according to the actual situation and operation requirements of the air source heat pump; when the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index exceed the corresponding thresholds, the early warning mechanism is triggered to remind the operator to pay attention and take troubleshooting and maintenance measures.

[0052] Preferably, the operation risk management module combines the compressor control performance abnormality index YKa, the defrost control performance abnormality index YKb and the energy-saving control abnormality index YKc to obtain the operation abnormality description coefficient of the air source heat pump, and allocates monitoring resources for the air source heat pump based on the operation abnormality description coefficient.

[0053] Technical effects and advantages of the present invention:

[0054] (1) The air source heat pump control system provided by the present invention models each component of the air source heat pump, outputs the mathematical model of each component and verifies the reliability; the mathematical model of each component constitutes an air source heat pump simulation model, and the compressor speed prediction value is output based on the air source heat pump simulation model; a speed control instruction is generated based on the compressor speed prediction value, and the air source heat pump compressor is used to receive the instruction and execute it; the difference between the predicted heating coefficient and the actual heating coefficient is monitored in real time, and measures are taken; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold value TH1, an early warning is issued; the problem of lack of real-time perception and adaptive adjustment capabilities of environmental changes and inability to achieve optimal energy-saving effects in the prior art is effectively solved.

[0055] (2) The air source heat pump control system provided by the present invention provides an anti-frost control module to intelligently control the defrosting operation, thereby reducing unnecessary energy consumption and optimizing energy efficiency. By real-time monitoring of the surface temperature of the outdoor heat exchanger and the ambient air parameters, the frost risk can be accurately determined and preventive measures can be taken, thereby effectively avoiding the performance degradation of the evaporator components and system shutdown, and improving the stability and reliability of the system.

[0056] (3) The air source heat pump control system provided by the present invention obtains the performance status of the air source heat pump in terms of compressor control, defrost control and energy-saving control by analyzing the operating data, and takes effective troubleshooting and maintenance measures. Through the intelligent early warning mechanism and accurate abnormality detection capability, it can automatically remind the operator to pay attention to potential risks and guide them to take corresponding troubleshooting and maintenance measures, which is beneficial to improving the work efficiency of the operator and reducing the risk of downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a structural block diagram of the operating parameter intelligent control module of the present invention.

[0058] Figure 2 This is a structural block diagram of the air source heat pump control system of the present invention. DETAILED DESCRIPTION

[0059] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0060] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0061] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0062] Example 1, see Figure 1 The present invention provides a block diagram of the intelligent control module for operating parameters. Figure 1 An air source heat pump control system shown includes:

[0063] The operating parameter intelligent control module uses machine learning to mine and analyze the historical operating data of the air source heat pump, and outputs the predicted value of the compressor speed and the corresponding predicted heating coefficient;

[0064] The operation parameter intelligent control module includes: an air source heat pump simulation model building unit, an operation parameter intelligent prediction unit and an operation parameter execution unit;

[0065] The air source heat pump simulation model building unit is used to model each component of the air source heat pump, output the mathematical model of each component and verify the reliability; the mathematical model of each component constitutes the air source heat pump simulation model, and each component at least includes a compressor, an expansion valve, an evaporator, a condenser, and a load;

[0066] The operating parameter intelligent prediction unit outputs the predicted value of the compressor speed based on the air source heat pump simulation model;

[0067] An operating parameter execution unit generates a speed control instruction based on the predicted value of the compressor speed, and the air source heat pump compressor is used to receive the instruction and execute it;

[0068] The monitoring and early warning module monitors the difference between the predicted heating coefficient and the actual heating coefficient in real time and takes measures; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1, a first-level warning is issued; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1 and the duration exceeds the threshold TH2, a second-level warning is issued.

[0069] It needs to be further explained in the embodiment of the present invention that the air source heat pump simulation model building unit includes:

[0070] According to the principles of thermodynamics, fluid mechanics and the ideal gas state equation, the heat conduction characteristics of the compressor, expansion valve, condenser, evaporator and load of the air source heat pump are analyzed, and the mathematical models of each component are output; the mathematical models of each component are connected to construct the air source heat pump simulation mathematical model;

[0071] The mathematical model of each component is verified separately to obtain the measured data and predicted data of the mathematical model of each component; the mathematical model parameters are continuously adjusted and optimized until the predicted results of the air source heat pump simulation mathematical model and the actual situation meet the preset value of the loss function, thereby ensuring the effectiveness of the mathematical model in subsequent prediction and control.

[0072] What needs to be further explained in the embodiments of the present invention is that the method of obtaining the predicted heating coefficient is: based on the air source heat pump simulation model, different compressor speed values ​​are input for simulation operation, and in each simulation operation, the corresponding heating coefficient is calculated and recorded; through machine learning and optimization algorithms, the predicted value of the compressor speed that makes the heating coefficient reach the maximum value is found, and the predicted value of the compressor speed and the corresponding predicted heating coefficient are output.

[0073] It needs to be further explained in the embodiment of the present invention that the operation process of the operating parameter intelligent control module includes the following steps:

[0074] Step S11: constructing a nonlinear prediction model of an air source heat pump based on the established air source heat pump simulation model;

[0075] Explanation: Based on the load temperature of the air source heat pump , condenser tube wall temperature 、 Evaporator tube wall temperature is the state variable, compressor speed To control the quantity, the condenser inlet temperature , evaporator inlet temperature , Compensation temperature , Ambient temperature , Refrigerant density in compressor , compressor volumetric efficiency The nonlinear prediction model of air source heat pump is constructed as disturbance. The nonlinear prediction model of air source heat pump is the function of the change rate of state variables (such as load temperature, condenser tube wall temperature, evaporator tube wall temperature) over time, with the state variables themselves and control variables as the change rate function as independent variables; among them, the function of the change rate of load temperature over time is recorded as The function of the rate of change of the condenser tube wall temperature over time is recorded as , the function of the rate of change of the evaporator tube wall temperature over time is recorded as ;

[0076] Step S12: Process the nonlinear prediction model of the air source heat pump through the Taylor formula, linearize the nonlinear prediction model of the air source heat pump near a given working point, and output the linearized prediction model; regularly update the working point to ensure the accuracy of the linearized prediction model;

[0077] Explanation: A given operating point refers to the latest collected state variables, disturbances, and control quantities of the air source heat pump. As the air source heat pump operates and the external environment changes, the given operating point will also change. In order to update the operating point, new data needs to be continuously collected to ensure that the nonlinear prediction model of the air source heat pump can accurately reflect the current state of the system.

[0078] Step S13, discretizing the linearized prediction model and outputting the discretized prediction model; embedding the discretized prediction model into the control chip to realize the prediction control of the air source heat pump;

[0079] Step S14: according to the optimization objective function and the constraint conditions, the predicted value of the compressor speed is solved, and the predicted value of the compressor speed is applied to the air source heat pump to realize the predictive control of the air source heat pump.

[0080] Explanation: Constraints usually refer to restrictions on the operating status and parameters of air source heat pumps. Constraints may come from the physical characteristics, safety requirements or operating strategy requirements of the air source heat pump; for example, the compressor speed may be limited by the motor rated power and speed range; the condenser and evaporator temperatures may be limited by the material's temperature resistance and heat exchange efficiency; the load temperature may need to meet specific heating requirements or safety standards.

[0081] It is necessary to further explain in the embodiment of the present invention that the actual heating coefficient The calculation model satisfies the following formula:

[0082]

[0083] in, Indicates The value of the air source heat pump heating capacity during the time period, Indicates The value of the air source heat pump's self-consumption during the time period;

[0084] The method for obtaining the heating capacity of the air source heat pump is as follows:

[0085]

[0086] in, Indicates Heating power of air source heat pump during the time period, is the heating time interval;

[0087] The method for obtaining the heating power of the air source heat pump is: obtaining the specific heat capacity of the cooling medium , get the flow rate of cooling medium per unit time , the temperature difference of the cooling medium is recorded as ; Through the formula Calculate the heating power of the air source heat pump .

[0088] Summary: The operating parameter intelligent control module described in the embodiment of the present invention uses machine learning to mine and analyze historical operating data, and can predict the compressor speed and the corresponding heating coefficient; compared with traditional manual or fixed parameter control, it can more effectively adjust the operating state of the heat pump, thereby improving the energy efficiency of the system; when the actual operation deviates from the predicted value, the monitoring and early warning module can monitor in real time and take measures to ensure that the system can respond quickly and adjust to the optimal state.

[0089] Embodiment 2, the embodiment of the present invention provides an air source heat pump control system, which is different from embodiment 1 in that it also includes an anti-frost control module.

[0090] The anti-frost control module is used to prevent the evaporator components in the air source heat pump from frosting and is responsible for controlling the start and stop of the defrosting operation of the air source heat pump;

[0091] When the surface temperature of the outdoor heat exchanger is lower than the dew point and freezing point of the ambient air, the defrost operation is triggered;

[0092] When the surface temperature of the outdoor heat exchanger is not lower than the dew point and freezing point of the ambient air, the environmental parameters are monitored in real time to determine whether there is a frost risk scenario and make corresponding control decisions accordingly; including but not limited to starting the defrost preheating operation in advance, adjusting the heat pump operation mode, and changing the outdoor unit wind speed or direction.

[0093] Explanation: the defrost preheating operation is started in advance: by heating the evaporator components to increase the surface temperature, thereby effectively preventing the formation of frost, which is achieved by adjusting the temperature of the circulating medium in the heat pump system or introducing an external heat source;

[0094] Adjust the heat pump operation mode: Reduce the possibility of frost by adjusting the heat pump operation mode, including reducing the heating capacity to reduce condensation, or increasing the heating capacity but reducing the operating time of the outdoor unit to reduce the humidity on the condenser surface;

[0095] Change the wind speed or wind direction of the outdoor unit: Reduce the risk of frost by adjusting the wind speed or wind direction of the outdoor unit. Increasing the wind speed can accelerate the air flow on the condenser surface and reduce the accumulation of condensed water; adjusting the wind direction can reduce the residence time of wet air on the condenser surface, thereby reducing the possibility of frost.

[0096] The embodiments of the present invention do not specifically limit the defrost operation, and the defrost operation can be achieved in a variety of ways, such as reverse cycle defrosting and electric defrosting; reverse cycle defrosting is achieved by changing the circulation direction of the refrigerant in the heat pump system to change the evaporator into a condenser, thereby using the heat of the refrigerant to melt the frost layer on the surface of the evaporator; electric defrosting is achieved by heating the surface of the evaporator to melt the frost layer.

[0097] It is necessary to further explain in the embodiment of the present invention that the frost risk coefficient is obtained by real-time monitoring of environmental parameters. When the frost risk coefficient exceeds a preset value, it is determined that the current scene is in a frost risk scenario. The frost risk coefficient is obtained in the following manner:

[0098] Get the ambient temperature, ambient humidity and return water temperature, respectively, , , ;

[0099] Get the dew point temperature and freezing point temperature of the ambient air, denoted as , ;

[0100] The frost risk factor FRI is calculated by the following formula:

[0101] in, Used to adjust the sensitivity of the ambient temperature to the risk of frost. Used to adjust the sensitivity of the ambient humidity to the risk of frost. Used to adjust the sensitivity of the return water temperature to the risk of frost formation; It represents the ambient temperature threshold. When the ambient temperature is lower than the threshold, the risk of frost increases significantly. RH0 represents the ambient humidity threshold. When the ambient humidity is higher than the threshold, the risk of frost increases.

[0102] Summary: The air source heat pump control system provided by the embodiment of the present invention has the advantages of intelligent control, energy saving and consumption reduction, extended equipment life and improved user experience; by real-time monitoring of environmental parameters and judging the risk of frost, precise control of the defrosting operation of the air source heat pump is achieved, providing a strong guarantee for the stable operation of the heat pump system.

[0103] Example 3, see Figure 2 The air source heat pump control system structure block diagram of the present invention, the embodiment of the present invention provides an air source heat pump control system, which is different from the embodiment 2 in that it includes:

[0104] The operation data acquisition module is responsible for collecting various operation data of the air source heat pump, including at least compressor operation data, defrost execution data and heating coefficient time series data;

[0105] The compressor operation data analysis module extracts the compressor speed prediction value and the execution value from the compressor operation data, and uses the difference between the compressor speed prediction value and the execution value to represent the compressor control performance abnormality index;

[0106] The defrost execution data analysis module analyzes the defrost execution data and outputs the defrost control performance abnormality index;

[0107] The heating coefficient time series data analysis module analyzes the heating coefficient time series data and outputs the energy-saving control abnormality index;

[0108] The operation risk judgment and early warning module sets the thresholds of the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index according to the actual situation and operation requirements of the air source heat pump; when the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index exceed the corresponding thresholds, it is considered that there is an abnormality or potential risk in this aspect, and the early warning mechanism is triggered to remind the operator to pay attention and take troubleshooting and maintenance measures; for example, perform troubleshooting and take corresponding repair measures according to the troubleshooting results; replace faulty parts and adjust the control strategy; regularly maintain the air source heat pump, including cleaning the radiator, checking the refrigerant pressure, replacing the filter, etc.

[0109] In a possible embodiment, the compressor control performance abnormality index YKa is obtained as follows:

[0110] Generate a speed control command based on the predicted value of the compressor speed, assuming that there are n speed control commands, and i represents the sequence number of the speed control command;

[0111] The predicted value and the executed value of the i-th speed control command are respectively recorded as, , ; Calculate the difference between the predicted value and the executed value to get the executed value fluctuation coefficient ;

[0112] The compressor control performance abnormality index YKa is calculated by the following formula:

[0113] .

[0114] In a possible embodiment, the defrost control performance abnormality index YKb is obtained as follows:

[0115] Based on the defrost time and defrost energy consumption, the actual defrost energy efficiency is calculated;

[0116] Assume that there are p defrost operations, and use q to represent the order of the defrost operations. The preset defrost energy efficiency and the actual defrost energy efficiency of the qth defrost operation are recorded as, , ;

[0117] Calculate the difference between the actual defrost energy efficiency and the preset defrost energy efficiency to get the defrost energy efficiency fluctuation coefficient ;

[0118] The defrost control performance abnormality index YKb is calculated by the following formula:

[0119] .

[0120] In a possible embodiment, the energy-saving control abnormality index YKc is obtained in the following manner:

[0121] The operation data of the air source heat pump is divided into frosting scenario, frosting risk scenario and non-frosting risk scenario according to the scenario;

[0122] Filter out non-frost risk scenarios from the operating data to obtain data set A;

[0123] Analyze data set A to obtain the average value of heating coefficient in non-frost risk scenarios;

[0124] Calculate the heating coefficient time series data and the heating coefficient average value to obtain the heating coefficient fluctuation coefficient ;

[0125] Assume there are k non-frost risk scenarios, and use s to represent the sequence number of the non-frost risk scenario;

[0126] The average heating coefficient and heating coefficient of the sth non-frost risk scenario are recorded as, , ;

[0127] The energy-saving control abnormality index YKc is calculated by the following formula:

[0128] .

[0129] In a possible embodiment, the system further includes: an operation risk management module,

[0130] When the thresholds of the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index do not exceed the corresponding thresholds, the compressor control performance abnormality index YKa, the defrost control performance abnormality index YKb and the energy-saving control abnormality index YKc are combined to obtain the operation abnormality description coefficient of the air source heat pump, and the monitoring resources of the air source heat pump are allocated based on the operation abnormality description coefficient; for example, the air source heat pumps are prioritized according to the size of the operation abnormality description coefficient, and the air source heat pumps with high values ​​are monitored first.

[0131] In one possible embodiment, by formula The operation abnormality description coefficient Ym is calculated; wherein w1, w2, w3 represent the weight coefficients of each item, and w1+w2+w3=100%; THa, YHb, THc represent the threshold values ​​corresponding to each item;

[0132] Summary: The embodiment of the present invention can obtain the operation abnormality description coefficient of the air source heat pump by combining the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index; based on the coefficient, a series of measures are taken to ensure the safe and stable operation of the heat pump, improve energy utilization efficiency and reduce operating costs.

[0133] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An air source heat pump control system, characterized in that: include: The operating parameter intelligent control module uses machine learning to mine and analyze the historical operating data of the air source heat pump, and outputs the predicted value of the compressor speed and the corresponding predicted heating coefficient; The operation parameter intelligent control module includes: an air source heat pump simulation model building unit, an operation parameter intelligent prediction unit and an operation parameter execution unit; The air source heat pump simulation model building unit is used to model each component of the air source heat pump, output the mathematical model of each component and verify the reliability; the mathematical model of each component constitutes the air source heat pump simulation model, and each component at least includes a compressor, an expansion valve, an evaporator, a condenser, and a load; The operating parameter intelligent prediction unit outputs the predicted value of the compressor speed based on the air source heat pump simulation model; An operating parameter execution unit generates a speed control instruction based on the predicted value of the compressor speed, and the air source heat pump compressor is used to receive the instruction and execute it; The monitoring and early warning module monitors the difference between the predicted heating coefficient and the actual heating coefficient in real time and takes measures; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1, a first-level warning is issued; when the difference between the predicted heating coefficient and the actual heating coefficient exceeds the threshold TH1 and the duration exceeds the threshold TH2, a second-level warning is issued.

2. The air source heat pump control system according to claim 1, characterized in that: The air source heat pump simulation model building unit includes: According to the principles of thermodynamics, fluid mechanics and the ideal gas state equation, the heat conduction characteristics of the compressor, expansion valve, condenser, evaporator and load of the air source heat pump are analyzed, and the mathematical model of each component is output; Connect the mathematical models of each component to construct a simulation mathematical model of the air source heat pump; Verify the mathematical model of each component respectively, and obtain the measured data and predicted data of the mathematical model of each component; The mathematical model parameters are continuously adjusted and optimized until the predicted results of the air source heat pump simulation mathematical model and the actual situation meet the preset values ​​of the loss function.

3. The air source heat pump control system according to claim 1, characterized in that: The method for obtaining the predicted heating coefficient is: based on the air source heat pump simulation model, different compressor speed values ​​are input for simulation operation, and in each simulation operation, the corresponding heating coefficient is calculated and recorded; through machine learning and optimization algorithms, the predicted value of the compressor speed that makes the heating coefficient reach the maximum value is found, and the predicted value of the compressor speed and the corresponding predicted heating coefficient are output.

4. The air source heat pump control system according to claim 1, characterized in that: The actual heating coefficient The calculation model satisfies the following formula: in, Indicates The value of the air source heat pump heating capacity during the time period, Indicates The value of the air source heat pump's self-consumption during the time period; The method for obtaining the heating capacity of the air source heat pump is as follows: in, Indicates Heating power of air source heat pump during the time period, is the heating time interval; The method for obtaining the heating power of the air source heat pump is: obtaining the specific heat capacity of the cooling medium , get the flow rate of cooling medium per unit time , the temperature difference of the cooling medium is recorded as ; Through the formula Calculate the heating power of the air source heat pump .

5. The air source heat pump control system according to claim 1, characterized in that: The operation process of the operating parameter intelligent control module includes the following steps: Step S11: constructing a nonlinear prediction model of an air source heat pump based on the established air source heat pump simulation model; Step S12: Process the air source heat pump nonlinear prediction model through Taylor formula, linearize the air source heat pump nonlinear prediction model near a given working point, and output the linearized prediction model; regularly update the working point to ensure the accuracy of the linearized prediction model; Step S13, discretizing the linearized prediction model and outputting the discretized prediction model; embedding the discretized prediction model into the control chip to realize the prediction control of the air source heat pump; Step S14: according to the optimization objective function and the constraint conditions, the predicted value of the compressor speed is solved, and the predicted value of the compressor speed is applied to the air source heat pump to realize the predictive control of the air source heat pump.

6. The air source heat pump control system according to claim 1, characterized in that: The actual heating coefficient The calculation model satisfies the following formula: in, Indicates The value of the air source heat pump heating capacity during the time period, Indicates The value of the air source heat pump's self-consumption during the time period; The method for obtaining the heating capacity of the air source heat pump is as follows: in, Indicates Heating power of air source heat pump during the time period, is the heating time interval; The method for obtaining the heating power of the air source heat pump is: obtaining the specific heat capacity of the cooling medium , get the flow rate of cooling medium per unit time , the temperature difference of the cooling medium is recorded as ; Through the formula Calculate the heating power of the air source heat pump .

7. The air source heat pump control system according to claim 1, characterized in that: Also includes: The anti-frost control module is used to prevent the evaporator components in the air source heat pump from frosting and is responsible for controlling the start and stop of the defrosting operation of the air source heat pump; When the surface temperature of the outdoor heat exchanger is lower than the dew point and freezing point of the ambient air, the defrost operation is triggered; When the surface temperature of the outdoor heat exchanger is not lower than the dew point and freezing point of the ambient air, the environmental parameters are monitored in real time to determine whether there is a frost risk scenario and make corresponding control decisions accordingly, including but not limited to starting the defrost preheating operation in advance, adjusting the heat pump operation mode, and changing the outdoor unit wind speed or direction.

8. The air source heat pump control system according to claim 7, characterized in that: The frost risk coefficient is obtained by real-time monitoring of environmental parameters. When the frost risk coefficient exceeds a preset value, it is determined that the current scene is in a frost risk scenario. The frost risk coefficient is obtained in the following manner: Get the ambient temperature, ambient humidity and return water temperature, respectively, , , ; Get the dew point temperature and freezing point temperature of the ambient air, denoted as , ; The frost risk factor FRI is calculated by the following formula: in, Used to adjust the sensitivity of the ambient temperature to the risk of frost. Used to adjust the sensitivity of the ambient humidity to the risk of frost. Used to adjust the sensitivity of the return water temperature to the risk of frost formation; It represents the ambient temperature threshold. When the ambient temperature is lower than the threshold, the risk of frost increases significantly. RH0 represents the ambient humidity threshold. When the ambient humidity is higher than the threshold, the risk of frost increases.

9. The air source heat pump control system according to claim 7, characterized in that: Also includes: The operation data acquisition module is responsible for collecting various operation data of the air source heat pump, including at least compressor operation data, defrost execution data and heating coefficient time series data; The compressor operation data analysis module extracts the compressor speed prediction value and the execution value from the compressor operation data, and uses the difference between the compressor speed prediction value and the execution value to represent the compressor control performance abnormality index; The defrost execution data analysis module analyzes the defrost execution data and outputs the defrost control performance abnormality index; The heating coefficient time series data analysis module analyzes the heating coefficient time series data and outputs the energy-saving control abnormality index; The operation risk judgment and early warning module sets the thresholds of the compressor control performance abnormality index, the defrost control performance abnormality index and the energy-saving control abnormality index according to the actual situation and operation requirements of the air source heat pump; When the compressor control performance abnormality index, defrost control performance abnormality index and energy-saving control abnormality index exceed the corresponding thresholds, the early warning mechanism is triggered to remind the operator to pay attention and take troubleshooting and maintenance measures.

10. The air source heat pump control system according to claim 9, characterized in that: Also includes: The operation risk management module combines the compressor control performance abnormality index YKa, the defrost control performance abnormality index YKb and the energy-saving control abnormality index YKc to obtain the operation abnormality description coefficient of the air source heat pump, and allocates the monitoring resources of the air source heat pump based on the operation abnormality description coefficient.

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