Control method and control system of inverter compressor

Abstract

The invention provides a control method and a control system of an inverter compressor, and relates to the technical field of intelligent control. The method comprises the steps that temperature data and operation time of a target inverter compressor are collected; determining a current working condition according to the temperature data and the operation time; and a control strategy is selected according to the current working condition, and the target inverter compressor is controlled. According to the method, the current working condition can be accurately recognized by analyzing the temperature data and the operation time in real time, different control strategies are selected according to different working conditions, accurate control can be achieved, the control mechanism can be dynamically adjusted through the method, and compared with a PID control technology adopting fixed parameters in a traditional method, the control efficiency is improved. The control mode of the embodiment can flexibly cope with different industrial environments, and accurate control under various industrial environments is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly to a control method and a control system for a variable-frequency compressor. Background Art

[0002] In the field of industrial temperature control, variable-frequency compressors are widely used in temperature control equipment such as cabinet air conditioners, and have become an important guarantee for maintaining a stable operating environment for industrial equipment. However, there are still many problems to be solved in the current industrial temperature control technology.

[0003] Traditional temperature control mostly adopts PID control technology with fixed parameters. This control method exposes significant defects when facing complex and changeable industrial environments. In industrial scenarios, the ambient temperature may change suddenly, the equipment load fluctuates frequently, and electromagnetic interference will also affect the accuracy of sensor data. Fixed-parameter PID control is difficult to adapt to these changes. When the temperature changes suddenly, its overshoot can be as high as 15%, and the adjustment time exceeds 300 seconds, making it impossible to achieve fast and accurate temperature control.

[0004] With the continuous improvement of industrial automation and intelligence, higher requirements are put forward for the dynamic adaptability, stability, and reliability of temperature control systems. There is an urgent need to innovate temperature control technologies to solve the problems existing in traditional control methods in order to meet the growing needs of industrial production. Summary of the Invention

[0005] Embodiments of the present invention provide a control method and a control system for a variable-frequency compressor to solve the problem that the current temperature control technology cannot achieve fast and accurate temperature control.

[0006] In a first aspect, embodiments of the present invention provide a control method for a variable-frequency compressor, including:

[0007] Collecting temperature data and running time of a target variable-frequency compressor;

[0008] Determining the current working condition according to the temperature data and the running time;

[0009] Selecting a control strategy according to the current working condition to control the target variable-frequency compressor.

[0010] In a possible implementation manner, determining the current working condition according to the temperature data and the running time includes:

[0011] Calculating a temperature deviation, a temperature change rate, and a temperature change trend correlation coefficient according to the temperature data and the running time;

[0012] Obtaining a current working condition temperature value according to the temperature deviation, the temperature change rate, and the temperature change trend correlation coefficient;

[0013] If the temperature value of the current temperature condition is greater than the temperature threshold of the first preset temperature condition, it is determined that the current condition is a fast response condition;

[0014] If the temperature value of the current temperature condition is less than or equal to the temperature threshold of the first preset temperature condition and greater than the temperature threshold of the second preset temperature condition, it is determined that the current condition is a prediction compensation condition;

[0015] If the temperature value of the current temperature condition is less than or equal to the temperature threshold of the second preset temperature condition, it is determined that the current condition is a stable control condition;

[0016] Among them, the temperature threshold of the first preset temperature condition is greater than the temperature threshold of the second preset temperature condition.

[0017] In a possible implementation manner, alternatively, determining the current condition according to the temperature data and the running time includes:

[0018] Calculating the temperature deviation according to the temperature data;

[0019] Calculating the temperature change trend correlation coefficient according to the temperature data and the running time;

[0020] If the temperature change trend correlation coefficient is greater than the preset correlation coefficient, it is determined that the current condition is a prediction compensation condition;

[0021] If the temperature change trend correlation coefficient is not greater than the preset correlation coefficient and the temperature deviation is greater than the preset temperature deviation, it is determined that the current condition is a fast response condition;

[0022] If the temperature change trend correlation coefficient is not greater than the preset correlation coefficient and the temperature deviation is not greater than the preset temperature deviation, it is determined that the current condition is a stable control condition.

[0023] In a possible implementation manner, calculating the temperature change trend correlation coefficient according to the temperature data and the running time includes:

[0024] Calculating the standard deviation of the temperature data according to the temperature data and taking the standard deviation of the temperature data as the noise standard deviation;

[0025] Filtering and denoising the temperature data according to the noise standard deviation to obtain the denoised temperature data;

[0026] Corresponding the denoised temperature data with the running time to obtain the time-series temperature data;

[0027] Calculating the autocorrelation and partial autocorrelation of the time-series temperature data;

[0028] Determining the temperature change trend correlation coefficient according to the autocorrelation and partial autocorrelation.

[0029] In a possible implementation, the temperature data includes temperature signals; the temperature data is the temperature data collected by multiple temperature sensors; or, determining the current operating condition according to the temperature data and the running time includes:

[0030] Calculating the change rate of the temperature signal according to the temperature signal;

[0031] Calculating the acquisition error between any two temperature sensors based on the temperature data collected by multiple temperature sensors;

[0032] Obtaining a temperature prediction value according to the temperature data and the running time;

[0033] When the change rate of the temperature signal is greater than the preset change rate, it is determined that a fault exists;

[0034] When the acquisition error is greater than the preset acquisition error, it is determined that a fault exists;

[0035] When the prediction error between the temperature prediction value and the actual temperature is greater than the preset prediction error and the confidence level is the preset confidence level, it is determined that a fault exists;

[0036] If no fault exists, it is determined that the current operating condition is a fast response operating condition;

[0037] If a fault exists and the fault type is one kind, it is determined that the current operating condition is a stable control operating condition;

[0038] If a fault exists and the number of fault types is greater than or equal to two kinds, it is determined that the current operating condition is a prediction compensation operating condition.

[0039] In a possible implementation, selecting a control strategy according to the current operating condition to control the target variable-frequency compressor includes:

[0040] If the current operating condition is a fast response operating condition, then take dual-loop control as the control strategy and control the target variable-frequency compressor based on the dual-loop control; wherein, the dual-loop control includes temperature outer-loop control and speed inner-loop control;

[0041] If the current operating condition is a stable control operating condition, then take smooth control as the control strategy and control the target variable-frequency compressor based on the smooth control;

[0042] If the current operating condition is a prediction compensation operating condition, then take fuzzy control as the control strategy and control the target variable-frequency compressor based on the fuzzy control.

[0043] In a possible implementation, controlling the target variable-frequency compressor based on the dual-loop control includes:

[0044] Collecting the speed data of the target variable-frequency compressor;

[0045] Input the temperature deviation, temperature change rate, correlation coefficient of temperature change trend, and current rotational speed into the temperature outer loop, and obtain the target rotational speed based on the pre-determined outer loop control parameters;

[0046] Obtain the inner loop control signal according to the target rotational speed, current rotational speed, and pre-determined inner loop control parameters;

[0047] Control the rotational speed of the target variable frequency compressor according to the inner loop control signal.

[0048] In a possible implementation, control the target variable frequency compressor based on smooth control, including:

[0049] Predict the current rotational speed of the target variable frequency compressor to obtain rotational speed prediction data;

[0050] Adopt a smooth algorithm to obtain the rotational speed adjustment amount according to the current rotational speed and rotational speed prediction data;

[0051] Control the target variable frequency compressor according to the rotational speed adjustment amount.

[0052] In a possible implementation, control the target variable frequency compressor based on fuzzy control, including:

[0053] Calculate the temperature deviation and temperature change rate according to the temperature data and running time;

[0054] Adopt fuzzy control to determine the corresponding membership degree according to the temperature deviation and temperature change rate;

[0055] Control the rotational speed of the target variable frequency compressor according to the membership degree corresponding to the temperature deviation and temperature change rate.

[0056] In a second aspect, an embodiment of the present invention provides a control system for a variable frequency compressor, including a variable frequency compressor and an electronic device. Among them, the electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method in the above first aspect or any possible implementation manner of the first aspect is implemented.

[0057] In the embodiment of the present invention, by analyzing temperature data and running time in real time, the current working condition can be accurately identified. Different control strategies are selected according to different working conditions, and precise control can be achieved. Through this method, the control mechanism can be dynamically adjusted. Compared with the traditional PID control technology with fixed parameters, this control method in this embodiment can flexibly cope with different industrial environments and achieve precise control in multiple industrial environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart of the implementation of the control method for a variable frequency compressor provided by an embodiment of the present invention;

[0059] Figure 2 It is a schematic structural diagram of a control device for a variable-frequency compressor provided by an embodiment of the present invention. Detailed implementation manners

[0060] Next, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0061] Figure 1 It is a flowchart for implementing a control method for a variable-frequency compressor provided by an embodiment of the present invention. As Figure 1 shown, the method includes:

[0062] Step 110: Collect temperature data and running time of a target variable-frequency compressor.

[0063] In this embodiment, the temperature data may be a temperature signal. The running time is used to represent the time accumulated since the system starts, and can be used to judge which stage the system is in during operation. Different stages may have different control requirements.

[0064] In traditional control methods, temperature detection is often performed by a single temperature sensor. When the temperature sensor fails, such as short circuit, open circuit, or signal drift, etc., the control system often cannot respond in time; after the temperature sensor fails, the recovery time is too long, for example, more than five minutes. In this case, due to the lack of real-time monitoring and analysis of temperature, the normal operation of the system will be seriously affected.

[0065] Based on this problem, multiple temperature sensors can be deployed in this embodiment for temperature data collection. On the one hand, it can prevent the inability to collect temperature data in time when any one of the temperature sensors is damaged; on the other hand, through comprehensive analysis of the temperature data collected by multiple temperature sensors, the accuracy of analysis can be improved.

[0066] Step 120: Determine the current working condition according to the temperature data and the running time.

[0067] In this embodiment, corresponding indexes can be determined according to the temperature data and the running time, and the current working condition can be determined according to the obtained indexes; or, comprehensive evaluation can be performed according to the temperature data and the running time, and the current working condition can be determined according to the evaluation result; or, through predictive analysis, according to the relevant data obtained by prediction, the change situation of the current working condition at the next moment can be determined to adjust the corresponding strategy in advance.

[0068] Step 130: Select a control strategy according to the current working condition and control the target variable-frequency compressor.

[0069] In this embodiment, according to the current working condition, a corresponding control strategy is flexibly selected, and based on the selected control strategy, the target variable-frequency compressor is controlled, which can achieve precise control. By this method, the control mechanism can be dynamically adjusted. Compared with the PID control technology with fixed parameters in the traditional method, this control method in this embodiment can flexibly cope with different industrial environments and achieve precise control in various industrial environments.

[0070] In an alternative embodiment, determining the current working condition according to the temperature data and the running time in step 120 may include:

[0071] According to the temperature data and the running time, calculate the temperature deviation, the temperature change rate, and the correlation coefficient of the temperature change trend.

[0072] Obtain the current working condition temperature value based on the temperature deviation, the temperature change rate, and the correlation coefficient of the temperature change trend.

[0073] If the current working condition temperature value is greater than the first preset working condition temperature threshold, it is determined that the current working condition is a fast response working condition.

[0074] If the current working condition temperature value is less than or equal to the first preset working condition temperature threshold and greater than the second preset working condition temperature threshold, it is determined that the current working condition is a prediction compensation working condition.

[0075] If the current working condition temperature value is less than or equal to the second preset working condition temperature threshold, it is determined that the current working condition is a stable control working condition.

[0076] Wherein, the first preset working condition temperature threshold is greater than the second preset working condition temperature threshold.

[0077] In this embodiment, the temperature deviation represents the difference between the currently collected temperature data and the preset temperature, which reflects the degree of temperature deviation from the preset temperature, that is, the target temperature.

[0078] The temperature change rate is used to measure the speed of temperature change, which is obtained by the difference between adjacent temperature deviations and the corresponding time interval, and the corresponding time interval is determined based on the running time.

[0079] The correlation coefficient of the temperature change trend is a statistical index that measures the trend of temperature data changing with time. It is obtained by analyzing the obtained temperature data based on the time series analysis algorithm. Its value range is usually between -1 and 1. A positive value indicates that the temperature is on the rise, a negative value indicates a downward trend, and the closer the absolute value is to 1, the more obvious the trend.

[0080] According to the temperature deviation, the temperature change rate, and the correlation coefficient of the temperature change trend, the current working condition temperature value is obtained through the following formula.

[0081] Among them, the formula is as follows:

[0082]

[0083] In the formula, Mode is the temperature value of the current working condition; ΔT is the temperature deviation; is the temperature change rate; ρ trend is the correlation coefficient related to the temperature change trend.

[0084] According to the historical data and the working conditions corresponding to the historical stage, set the temperature threshold of the first preset temperature working condition and the temperature threshold of the second preset temperature working condition. Exemplarily, the temperature threshold of the first preset temperature working condition can be 2, and the temperature threshold of the second preset temperature working condition can be 0.5.

[0085] If the temperature value of the current temperature working condition is greater than the temperature threshold of the first preset temperature working condition, it is determined that the current working condition is a fast response working condition. In this current working condition, it indicates that there is a large deviation between the current ambient temperature and the set temperature, and the compressor needs to quickly adjust the refrigeration or heating power to make the temperature approach the set value as soon as possible to meet the user's demand for rapid temperature adjustment.

[0086] If the temperature value of the current temperature working condition is less than or equal to the temperature threshold of the first preset temperature working condition and greater than the temperature threshold of the second preset temperature working condition, it is determined that the current working condition is a prediction compensation working condition. In this current working condition, controlling according to the current temperature data may lead to control lag, affecting the accuracy and comfort of temperature control. By predicting the temperature change trend and taking corresponding compensation control measures in advance, it is possible to better cope with future possible temperature changes and improve the control performance of the system.

[0087] If the temperature value of the current temperature working condition is less than or equal to the temperature threshold of the second preset temperature working condition, it is determined that the current working condition is a stable control working condition. In this current working condition, it shows that the current temperature is already close to the set temperature. At this time, the main goal is to maintain the stability of the temperature, avoid large temperature fluctuations, and at the same time reduce the energy consumption of the compressor and extend the service life of the equipment.

[0088] In addition to the method provided in the above embodiments, this embodiment also provides another method for determining the current working condition, and this method is as shown in the following embodiments:

[0089] In an alternative embodiment, or, in step 120, determining the current working condition according to the temperature data and the running time may include:

[0090] Calculate the temperature deviation according to the temperature data.

[0091] Calculate the correlation coefficient related to the temperature change trend according to the temperature data and the running time.

[0092] If the correlation coefficient of the temperature change trend is greater than the preset correlation coefficient, it is determined that the current working condition is the prediction compensation working condition.

[0093] If the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient, and the temperature deviation is greater than the preset temperature deviation, it is determined that the current working condition is the fast response working condition.

[0094] If the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient, and the temperature deviation is not greater than the preset temperature deviation, it is determined that the current working condition is the stable control working condition.

[0095] In this embodiment, the preset correlation coefficient can be 0.8, and the preset temperature deviation can be set to 1, 1.1, 1.2, etc. Among them, the comparative analysis mentioned here is based on the absolute values of the relevant data. For example, if the correlation coefficient of the temperature change trend is greater than the preset correlation coefficient, it means that the absolute value of the correlation coefficient of the temperature change trend is greater than the preset correlation coefficient, and the same applies to the temperature deviation.

[0096] When the correlation coefficient of the temperature change trend is greater than the preset correlation coefficient, it indicates that the temperature change trend is obvious. In the current situation, controlling based on the current temperature data may lead to control lag, affecting the accuracy and comfort of temperature control. Therefore, it is necessary to predict the temperature change trend and take corresponding compensation control measures in advance, that is, the prediction compensation working condition.

[0097] When the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient, it indicates that the temperature change trend is relatively stable. At this time, it is necessary to combine the temperature deviation to determine the deviation degree between the current temperature and the set temperature. When the temperature deviation is greater than the preset temperature deviation, it indicates that there is a large deviation between the current ambient temperature and the set temperature, and the compressor needs to quickly adjust the refrigeration or heating power, that is, it is determined that the current working condition is the fast response working condition.

[0098] When the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient, and when the temperature deviation is not greater than the preset temperature deviation, it indicates that the deviation between the current ambient temperature and the set temperature is small, and the current temperature is already close to the set temperature. At this time, the main goal is to maintain the stability of the temperature, that is, the stable control working condition.

[0099] In an alternative embodiment, calculating the correlation coefficient of the temperature change trend according to the temperature data and the running time may include:

[0100] According to the temperature data, calculate the standard deviation of the temperature data, and use the standard deviation of the temperature data as the noise standard deviation.

[0101] According to the noise standard deviation, filter and denoise the temperature data to obtain the denoised temperature data.

[0102] Correlate the denoised temperature data with the running time to obtain the time-series temperature data.

[0103] Calculate the autocorrelation and partial autocorrelation of the time-series temperature data.

[0104] Determine the correlation coefficient related to the temperature change trend based on the autocorrelation and partial autocorrelation.

[0105] In this embodiment, the standard deviation of the temperature data can be calculated in the following manner:

[0106] Sum up the temperature data at each acquisition point and take the average to obtain the average temperature, that is:

[0107]

[0108] Where is the average temperature; n is the number of acquisition points; T i is the temperature data at the i-th acquisition point.

[0109] Subtract the temperature data at each acquisition point from the average temperature to obtain the deviation at each acquisition point, that is:

[0110]

[0111] Where ΔT i is the deviation at the i-th acquisition point.

[0112] Square the deviation at each acquisition point and sum them up to obtain the sum of squared deviations, that is:

[0113] ∑i = 1n(ΔT i ) 2

[0114] Calculate the variance based on the sum of squared deviations, that is:

[0115]

[0116] Based on the variance, obtain the standard deviation of the temperature data. Assume that the fluctuating part in the temperature signal is mainly caused by noise. Therefore, the calculated standard deviation of the temperature data can be approximately regarded as the noise standard deviation, that is, use the standard deviation of the temperature data as the noise standard deviation.

[0117] The traditional method has limited ability to suppress noise, with a noise sensitivity of ±1.2°C, which cannot meet the requirements of high-precision temperature control. In this embodiment, the standard deviation of the temperature data calculated is used as the noise standard deviation to filter and denoise the temperature data to obtain the denoised temperature data.

[0118] After denoising, correlate the denoised temperature data with the running time to obtain the time-series temperature data.

[0119] The autocorrelation function and partial autocorrelation function are respectively used to calculate the autocorrelation and partial autocorrelation of the time-series temperature data.

[0120] According to the characteristics of the autocorrelation and partial autocorrelation, a time-series model is selected, where the time-series regression model includes the autoregressive model, moving average model, autoregressive moving average model, etc.

[0121] According to the selected time-series model, the correlation coefficient of the temperature change trend is calculated.

[0122] In addition to the two methods provided in the above embodiments, this embodiment also provides a third method for determining the current working condition, and this method is shown in the following embodiments:

[0123] In an alternative embodiment, or, determining the current working condition according to the temperature data and running time in step 120 may include:

[0124] According to the temperature signal, the change rate of the temperature signal is calculated.

[0125] Based on the temperature data collected by multiple temperature sensors, the acquisition error between any two temperature sensors is calculated.

[0126] According to the temperature data and running time, a temperature prediction value is obtained.

[0127] When the change rate of the temperature signal is greater than the preset change rate, it is determined that a fault exists.

[0128] When the acquisition error is greater than the preset acquisition error, it is determined that a fault exists.

[0129] When the prediction error between the temperature prediction value and the actual temperature is greater than the preset prediction error and the confidence level is the preset confidence level, it is determined that a fault exists.

[0130] If no fault exists, it is determined that the current working condition is a fast response working condition.

[0131] If a fault exists and the type of the fault is one, it is determined that the current working condition is a stable control working condition.

[0132] If a fault exists and the number of fault types is greater than or equal to two, it is determined that the current working condition is a prediction compensation working condition.

[0133] Calculating the change rate of the temperature signal has been described in the above related embodiments, so it will not be elaborated here.

[0134] Through the set multiple temperature sensors, temperature data is collected simultaneously, and at each preset time, the temperature data collected by different sensors is compared to obtain the acquisition error between any two temperature sensors.

[0135] Train a temperature prediction model based on historical data, such as a long short-term memory network model. Obtain time-series temperature data based on the temperature data and the running time, and input it into the pre-trained temperature prediction model to obtain a temperature prediction value.

[0136] Comprehensively judge faults based on three indicators: the change rate of the temperature signal, the acquisition error, and the temperature prediction value.

[0137] When the change rate of the temperature signal is greater than the preset change rate, it indicates that the signal changes suddenly at this time, and it is determined that there is a fault. Among them, the preset change rate can be 4.5℃ / s, 5℃ / s, 5.5℃ / s, etc.

[0138] When the acquisition error is greater than the preset acquisition error, there may be a sensor fault, and it is determined that there is a fault at this time. Among them, the acquisition error can be ±0.6℃, ±0.5℃, ±0.4℃, etc.

[0139] Calculate the prediction error between the temperature prediction value and the actual temperature, and the corresponding confidence level. If the calculated prediction error is too large, for example, exceeding 2℃, and the confidence level reaches 95%, it is determined that there is a fault at this time.

[0140] Judge whether there is a fault according to the above fault criteria.

[0141] If there is no fault, it is determined that the current working condition is a fast response working condition.

[0142] If there is a fault and the number of faults is one, it is determined that the current working condition is a stable control working condition.

[0143] If there is a fault and the number of faults is two or more, it is determined that the current working condition is a prediction compensation working condition.

[0144] In an optional implementation manner, step 130 of selecting a control strategy according to the current working condition and controlling the target variable-frequency compressor may include:

[0145] If the current working condition is a fast response working condition, then use double-loop control as the control strategy and control the target variable-frequency compressor based on double-loop control; among them, double-loop control includes temperature outer-loop control and speed inner-loop control.

[0146] If the current working condition is a stable control working condition, then use smooth control as the control strategy and control the target variable-frequency compressor based on smooth control.

[0147] If the current working condition is a prediction compensation working condition, then use fuzzy control as the control strategy and control the target variable-frequency compressor based on fuzzy control.

[0148] Based on the determined current working condition, corresponding control strategies are selected to control the target variable-frequency compressor according to the characteristics of various working conditions.

[0149] Exemplarily, in dual-loop control, the outer loop generates a target speed based on the temperature deviation, and the inner loop realizes fast tracking of the compressor speed through current / voltage control. This control strategy has a fast dynamic response speed, strong anti-interference ability, and significant overshoot suppression effect, and is suitable for control adjustment under fast-response working conditions.

[0150] The smooth control strategy can maintain the temperature fluctuating slightly around the target value. It has extremely high temperature stability, significant energy-saving effect, and can achieve low-noise operation, and is suitable for control adjustment under stable control working conditions.

[0151] The fuzzy control strategy can respond to the temperature change trend in advance, and has the advantages of strong adaptability to nonlinear systems, parameter-free tuning, excellent robustness, and no need for an accurate mathematical model, and is suitable for control adjustment under predictive compensation working conditions.

[0152] In an alternative embodiment, controlling the target variable-frequency compressor based on dual-loop control includes:

[0153] Collect the speed data of the target variable-frequency compressor.

[0154] Input the temperature deviation, temperature change rate, temperature change trend correlation coefficient, and the current speed into the temperature outer loop, and based on the pre-determined outer-loop control parameters, obtain the target speed.

[0155] According to the target speed, the current speed, and the pre-determined inner-loop control parameters, obtain the inner-loop control signal.

[0156] Control the speed of the target variable-frequency compressor according to the inner-loop control signal.

[0157] In this embodiment, the dual-loop control can be implemented based on PID. Among them, the pre-determined outer-loop control parameters are obtained through the following method:

[0158] Adopt an optimization algorithm, such as a genetic algorithm, to dynamically optimize the PID control parameters, that is, the proportional coefficient kp, the integral coefficient ki, and the differential coefficient kd:

[0159] Initialize the population: Randomly generate a certain number (such as 100 groups) of PID parameter combinations. The value range of kp in each group of parameters is [0, 20], the value range of ki is [0, 20], and the value range of kd is [0, 20]. These initial parameter combinations constitute the initial population of the genetic algorithm.

[0160] Calculate the fitness: For each group of parameters, substitute them into the fitness function

[0161] def fitness function(params): kp, ki, kd = params;

[0162] It is calculated in return 1 / (J + 0.1 * S).

[0163] Among them, J is the integral error, and S is the output smoothness index.

[0164] The integral error is the integral of the temperature error over a period of time and is used to measure the accuracy of temperature control. The temperature error is the difference between the set temperature and the actual measured temperature. For example, within 10 seconds, the temperature is measured every 1 second, and the errors from the set temperature are 0.1°C, 0.2°C, -0.1°C, 0.3°C, 0.2°C, -0.2°C, 0.1°C, 0.3°C, -0.1°C, 0.2°C respectively;

[0165] Then J = ∫(0.1 + 0.2 - 0.1 + 0.3 + 0.2 - 0.2 + 0.1 + 0.3 - 0.1 + 0.2)dt; where t ranges from 0 to 10.

[0166] The output smoothness index is measured by calculating the root mean square error of the compressor speed change. Suppose within a period of time, the speeds of the target compressor are 5, 5.2, 5.5, 5.3, 5.4 in sequence. Taking 5 as the initial value, the root mean square error of the speed change is calculated to reflect the smoothness of the compressor speed output.

[0167] The roulette wheel selection method is adopted, and the selection probability of each group of parameters is calculated according to their fitness values. The higher the fitness, the greater the selection probability. For example, if the total fitness value is 100 and the fitness value of a certain group of parameters is 5, then its selection probability is 5%. Through random selection, some parameters are selected to enter the next generation.

[0168] Crossover operation: The selected parameters are grouped in pairs of two and perform single - point crossover. For example, there are two groups of parameters [10, 5, 8] and [12, 6, 9]. Randomly select the crossover point as 2, then the two new groups of parameters [10, 6, 9] and [12, 5, 8] are generated after crossover.

[0169] The parameters are mutated at a certain mutation rate (such as 0.1). For example, for a certain group of parameters [10, 5, 8], when mutating, each parameter has a probability of 0.1 for random change. If the first parameter is mutated and the random change value is -1, then the mutated parameter becomes [9, 5, 8].

[0170] After multiple rounds of selection, crossover, and mutation operations, through continuous iteration, finally a group of PID parameters with the highest fitness is obtained as the control parameters for temperature control under the current working conditions.

[0171] The pre-determined inner-loop control parameters are PI control parameters, and the way to determine them is the same as that of the outer-loop control parameters.

[0172] Input the rotational speed data, temperature deviation, temperature change rate, and temperature change trend correlation coefficient of the target variable-frequency compressor obtained by real-time acquisition into the temperature outer-loop. Based on the pre-determined outer-loop control parameters, obtain the target rotational speed. Among them, in the temperature outer-loop, according to the deviation between the set temperature and the actual temperature, combined with the current working condition mode decision, determine the target rotational speed. In the fast response mode, if the temperature deviation is large, the adjustment range of the target rotational speed will be increased to quickly make the temperature approach the set value; in the stable control mode, the adjustment range of the target rotational speed is small to maintain the temperature stable.

[0173] Input the target rotational speed and the current rotational speed into the rotational speed inner-loop. Based on the pre-determined inner-loop control parameters, perform proportional and integral operations on the deviation between the target rotational speed and the actual rotational speed, and output the inner-loop control signal to the drive circuit of the target variable-frequency compressor to control the rotational speed of the target variable-frequency compressor.

[0174] Among them, in the rotational speed inner-loop, according to the magnitude of the rotational speed deviation, output the control signal proportionally. For example, the proportional coefficient is 0.5, and the rotational speed deviation is 0.5 rps, then the proportional control output is 0.5×0.5 = 0.25; perform integral operation on the rotational speed deviation to eliminate the steady-state error. The integral coefficient determines the influence degree of the integral term on the control signal. For example, the integral coefficient is 0.1, and the integral value of the rotational speed deviation within a period of time is 1, then the integral control output is 0.1×1 = 0.1. Add the outputs of the proportional control and the integral control to obtain the final control signal. In the above example, the PI control output is 0.25 + 0.1 = 0.35, and this signal is input into the drive circuit of the target variable-frequency compressor to drive the variable-frequency compressor to work, adjust the rotational speed of the compressor, and thus achieve the control of the temperature.

[0175] In an alternative embodiment, controlling the target variable-frequency compressor based on smooth control includes:

[0176] Predict the current rotational speed of the target variable-frequency compressor to obtain rotational speed prediction data.

[0177] Adopt a smooth algorithm to obtain the rotational speed adjustment amount according to the current rotational speed and the rotational speed prediction data.

[0178] Control the target variable-frequency compressor according to the rotational speed adjustment amount.

[0179] In this embodiment, to achieve smooth control, first, analyze and predict the collected rotational speed data by methods such as linear regression or time series analysis. Predict the rotational speed change trend in the next 3 - 5 cycles.

[0180] According to the predicted trend, adjust the rotational speed according to the formula n k = n k-1 + Δn × α.

[0181] Wherein, n k is the current rotational speed; n k-1 is the rotational speed at the previous moment; Δn is the theoretical rotational speed adjustment amount obtained according to trend analysis; α is a smoothing coefficient, the value range of which is 0 - 1, and its magnitude is dynamically adjusted according to the stability and change range of the trend. When the trend is relatively stable, the value of α is close to 1, making the rotational speed adjustment closer to the theoretical value; when the trend fluctuates greatly, the value of α decreases, reducing the rotational speed adjustment amplitude to ensure smooth rotational speed change.

[0182] Exemplarily, according to the prediction, the rotational speed at the next moment should increase by 0.3 rps, then Δn = 0.3, the current rotational speed n k-1 is 5 rps, Δn is 0.3 rps, if the current trend is relatively stable and the value of α is 0.9, then the adjusted rotational speed n k = 5 + 0.3 × 0.9 = 5.27 rps.

[0183] In an alternative embodiment, control the target variable-frequency compressor based on fuzzy control, including:

[0184] Calculate the temperature deviation and the temperature change rate according to the temperature data and the running time.

[0185] Adopt fuzzy control, and determine the corresponding membership degrees according to the temperature deviation and the temperature change rate.

[0186] Control the rotational speed of the target variable-frequency compressor according to the membership degrees corresponding to the temperature deviation and the temperature change rate.

[0187] Fuzzy control is implemented based on fuzzy rules. Fuzzy rules are a kind of rules based on fuzzy logic, used to describe the behavior of the system in a fuzzy state. It allows the system to still make reasonable decisions when the input information is inaccurate or there is uncertainty. Fuzzy rules are usually expressed in the form of "if... then...", and are used to map input variables (such as temperature deviation, temperature change rate, etc.) to output variables (such as the compressor rotational speed adjustment amount).

[0188] The specific implementation process of fuzzy control is as follows:

[0189] 1. Fuzzification:

[0190] Fuzzification is the process of converting the input precise numerical value into a fuzzy linguistic variable. This step is achieved through the membership function, and the membership function defines the degree to which the input value belongs to a certain fuzzy set.

[0191] Input variables:

[0192] The temperature deviation and the temperature change rate are used as input variables.

[0193] The fuzzy linguistic variables are defined as follows:

[0194] Temperature deviation (ΔT): It can be divided into "large", "medium", and "small".

[0195] Temperature change rate It can be divided into "fast", "medium", and "slow".

[0196] The membership functions are defined as follows:

[0197] Temperature deviation (ΔT):

[0198] Large: When ΔT > 1°C, the membership degree is 1; when ΔT < 0.5°C, the membership degree is 0.

[0199] Medium: When ΔT is between 0.5°C and 1°C, the membership degree is 1.

[0200] Small: When ΔT < 0.5°C, the membership degree is 1.

[0201] Temperature change rate

[0202] Fast: When the membership degree is 1; when the membership degree is 0.

[0203] Medium: When is between 0.2°C / s and 0.5°C / s, the membership degree is 1.

[0204] Slow: When the membership degree is 1.

[0205] 2. The fuzzy rule base is defined as follows:

[0206] The fuzzy rule base contains a set of fuzzy rules that describe the relationship between the input variables and the output variable. The following are some example rules:

[0207] If ΔT is large and fast, then increase the compressor speed.

[0208] If ΔT is small and slow, then decrease the compressor speed.

[0209] If ΔT is medium and medium, then maintain the current speed.

[0210] 3. Fuzzy inference:

[0211] Fuzzy inference is a process of inferring to obtain the fuzzy set of the output variable based on the fuzzification results of the input variables and the fuzzy rule base.

[0212] Inference method: The "MIN-MAX" inference method in fuzzy logic is adopted.

[0213] For each rule, calculate the membership degree of the input variable.

[0214] According to the "if" part of the rule, take the minimum value of the membership degrees of the input variables as the activation degree of the rule.

[0215] According to the "then" part of the rule, map the activation degree to the fuzzy set of the output variable.

[0216] 4. Defuzzification

[0217] Defuzzification is the process of converting the fuzzy output set obtained from fuzzy inference into an exact numerical value. Common methods include the centroid method and the maximum membership degree method.

[0218] Among them, the centroid method is as follows:

[0219] Calculate the centroid of the fuzzy output set to obtain the specific rotational speed adjustment amount.

[0220] Formula: Δn = ∑ membership degree ∑ (membership degree × output value).

[0221] Example:

[0222] Suppose the fuzzy output set is "increase", and its membership function is a triangle with the vertex at Δn = 0.5.

[0223] By calculating the centroid of the membership function, the specific rotational speed adjustment amount Δn = 0.4 rps is obtained.

[0224] 5. Application example of fuzzy rules

[0225] When the system detects a serious fault (fault level 2), the specific application of the fuzzy rules is as follows:

[0226] Input parameters:

[0227] Temperature deviation ΔT = 1.2 °C (large)

[0228] Temperature change rate (fast)

[0229] Fuzzification:

[0230] The membership degree of ΔT is "large" (membership degree 1).

[0231] The membership degree of is "fast" (membership degree 1).

[0232] Fuzzy inference:

[0233] According to the rule "If ΔT is large and is fast, then increase the compressor speed", the output fuzzy set is "increase".

[0234] Defuzzification:

[0235] Through the centroid method calculation, the specific rotational speed adjustment amount Δn = 0.4 rps is obtained.

[0236] Control signal output:

[0237] The current compressor speed is 5 rps, and the adjusted speed is 5 + 0.4 = 5.4 rps.

[0238] In summary, by real-time analyzing the temperature data and running time, the current working condition can be accurately identified. According to different working conditions, different control strategies are selected, and precise control can be achieved. Through this method, the control mechanism can be dynamically adjusted. Compared with the PID control technology with fixed parameters in the traditional method, this control method in this embodiment can flexibly cope with different industrial environments and achieve precise control under various industrial environments.

[0239] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0240] The following is the device embodiment of the present invention. For the details not described in detail, reference can be made to the corresponding method embodiment above.

[0241] Figure 2 The structural schematic diagram of the control device of the variable-frequency compressor provided by the embodiment of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown and are described in detail as follows:

[0242] As Figure 2 shown, the control device 2 of the variable-frequency compressor includes:

[0243] An acquisition module 21, configured to acquire the temperature data and running time of the target variable-frequency compressor;

[0244] A determination module 22, configured to determine the current working condition according to the temperature data and running time;

[0245] A control module 23, configured to select a control strategy according to the current working condition and control the target variable-frequency compressor.

[0246] In a possible implementation manner, the determination module 22 is specifically configured to:

[0247] Calculate the temperature deviation, the temperature change rate, and the correlation coefficient of the temperature change trend based on the temperature data and the running time;

[0248] Obtain the current working condition temperature value based on the temperature deviation, the temperature change rate, and the correlation coefficient of the temperature change trend;

[0249] If the current working condition temperature value is greater than the first preset temperature working condition temperature threshold, determine that the current working condition is a fast response working condition;

[0250] If the current working condition temperature value is less than or equal to the first preset temperature working condition temperature threshold and greater than the second preset temperature working condition temperature threshold, determine that the current working condition is a prediction compensation working condition;

[0251] If the current working condition temperature value is less than or equal to the second preset temperature working condition temperature threshold, determine that the current working condition is a stable control working condition;

[0252] Wherein, the first preset temperature working condition temperature threshold is greater than the second preset temperature working condition temperature threshold.

[0253] In a possible implementation manner, or, the determining module 22 is specifically configured to:

[0254] Calculate the temperature deviation according to the temperature data;

[0255] Calculate the correlation coefficient of the temperature change trend according to the temperature data and the running time;

[0256] If the correlation coefficient of the temperature change trend is greater than the preset correlation coefficient, determine that the current working condition is a prediction compensation working condition;

[0257] If the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient and the temperature deviation is greater than the preset temperature deviation, determine that the current working condition is a fast response working condition;

[0258] If the correlation coefficient of the temperature change trend is not greater than the preset correlation coefficient and the temperature deviation is not greater than the preset temperature deviation, determine that the current working condition is a stable control working condition.

[0259] In a possible implementation manner, the determining module 22 is specifically configured to:

[0260] Calculate the standard deviation of the temperature data according to the temperature data, and use the standard deviation of the temperature data as the noise standard deviation;

[0261] Filter and denoise the temperature data according to the noise standard deviation to obtain the denoised temperature data;

[0262] Correspond the denoised temperature data with the running time to obtain the time-series temperature data;

[0263] Calculate the autocorrelation and partial autocorrelation of the time-series temperature data;

[0264] Determine the correlation coefficient of the temperature change trend based on autocorrelation and partial autocorrelation.

[0265] In a possible implementation, the temperature data includes a temperature signal; the temperature data is the temperature data collected by multiple temperature sensors; or, the determination module 22 is specifically configured to:

[0266] Calculate the change rate of the temperature signal according to the temperature signal;

[0267] Calculate the acquisition error between any two temperature sensors based on the temperature data collected by multiple temperature sensors;

[0268] Obtain the temperature prediction value according to the temperature data and the running time;

[0269] When the change rate of the temperature signal is greater than the preset change rate, it is determined that there is a fault;

[0270] When the acquisition error is greater than the preset acquisition error, it is determined that there is a fault;

[0271] When the prediction error between the temperature prediction value and the actual temperature is greater than the preset prediction error and the confidence level is the preset confidence level, it is determined that there is a fault;

[0272] If there is no fault, it is determined that the current working condition is a fast response working condition;

[0273] If there is a fault and the type of the fault is one kind, it is determined that the current working condition is a stable control working condition;

[0274] If there is a fault and the type of the fault is greater than or equal to two kinds, it is determined that the current working condition is a prediction compensation working condition.

[0275] In a possible implementation, the control module 23 is specifically configured to:

[0276] If the current working condition is a fast response working condition, the double-loop control is used as the control strategy and the target variable-frequency compressor is controlled based on the double-loop control; wherein, the double-loop control includes a temperature outer loop control and a speed inner loop control;

[0277] If the current working condition is a stable control working condition, the smooth control is used as the control strategy and the target variable-frequency compressor is controlled based on the smooth control;

[0278] If the current working condition is a prediction compensation working condition, the fuzzy control is used as the control strategy and the target variable-frequency compressor is controlled based on the fuzzy control.

[0279] In a possible implementation, the control module 23 is specifically configured to:

[0280] Collect the speed data of the target variable-frequency compressor;

[0281] Input the temperature deviation, temperature change rate, correlation coefficient of temperature change trend, and the current rotational speed into the outer temperature loop, and based on the pre-determined outer loop control parameters, obtain the target rotational speed;

[0282] According to the target rotational speed, the current rotational speed, and the pre-determined inner loop control parameters, obtain the inner loop control signal;

[0283] Control the rotational speed of the target variable-frequency compressor according to the inner loop control signal.

[0284] In a possible implementation manner, the control module 23 is specifically configured to:

[0285] Predict the current rotational speed of the target variable-frequency compressor to obtain rotational speed prediction data;

[0286] Adopt a smoothing algorithm to obtain a rotational speed adjustment amount according to the current rotational speed and the rotational speed prediction data;

[0287] Control the target variable-frequency compressor according to the rotational speed adjustment amount.

[0288] In a possible implementation manner, the control module 23 is specifically configured to:

[0289] Calculate the temperature deviation and the temperature change rate according to the temperature data and the running time;

[0290] Adopt fuzzy control to determine the corresponding membership degrees according to the temperature deviation and the temperature change rate;

[0291] Control the rotational speed of the target variable-frequency compressor according to the membership degrees corresponding to the temperature deviation and the temperature change rate.

[0292] An embodiment of the present invention further provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the methods in the above method embodiments are implemented.

[0293] An embodiment of the present invention provides a control system for a variable-frequency compressor, including a variable-frequency compressor and an electronic device.

[0294] In the above embodiments, the descriptions of each embodiment have their own focuses. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. If there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be mutually referred to. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0295] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A control method for a variable frequency compressor, characterized in that, Including: Collecting the temperature data and running time of the target variable-frequency compressor; Determining the current working condition according to the temperature data and the running time; Selecting a control strategy according to the current working condition to control the target variable-frequency compressor.

2. The control method of the variable frequency compressor according to claim 1, characterized in that, The determining of the current working condition according to the temperature data and the running time includes: Calculating the temperature deviation, the temperature change rate and the temperature change trend correlation coefficient according to the temperature data and the running time; Obtaining the current working condition temperature value according to the temperature deviation, the temperature change rate and the temperature change trend correlation coefficient; If the current working condition temperature value is greater than the first preset temperature working condition temperature threshold, it is determined that the current working condition is a fast response working condition; If the current working condition temperature value is less than or equal to the first preset temperature working condition temperature threshold and greater than the second preset temperature working condition temperature threshold, it is determined that the current working condition is a prediction compensation working condition; If the current working condition temperature value is less than or equal to the second preset temperature working condition temperature threshold, it is determined that the current working condition is a stable control working condition; Wherein, the first preset temperature working condition temperature threshold is greater than the second preset temperature working condition temperature threshold.

3. The control method of the variable-frequency compressor according to claim 2, wherein Alternatively, the determining of the current working condition according to the temperature data and the running time includes: Calculating the temperature deviation according to the temperature data; Calculating the temperature change trend correlation coefficient according to the temperature data and the running time; If the temperature change trend correlation coefficient is greater than the preset correlation coefficient, it is determined that the current working condition is a prediction compensation working condition; If the temperature change trend correlation coefficient is not greater than the preset correlation coefficient and the temperature deviation is greater than the preset temperature deviation, it is determined that the current working condition is a fast response working condition; If the temperature change trend correlation coefficient is not greater than the preset correlation coefficient and the temperature deviation is not greater than the preset temperature deviation, it is determined that the current working condition is a stable control working condition.

4. The control method of the variable-frequency compressor according to claim 3, characterized in that, The calculating of the temperature change trend correlation coefficient according to the temperature data and the running time includes: Calculating the standard deviation of the temperature data according to the temperature data and taking the standard deviation of the temperature data as the noise standard deviation; Filtering and denoising the temperature data according to the noise standard deviation to obtain the denoised temperature data; Corresponding the denoised temperature data with the running time to obtain the time-series temperature data; Calculating the autocorrelation and partial autocorrelation of the time-series temperature data; Determining the temperature change trend correlation coefficient according to the autocorrelation and the partial autocorrelation.

5. The control method of the variable frequency compressor according to claim 1, wherein, The temperature data includes temperature signals; the temperature data is the temperature data collected by multiple temperature sensors; or, the determining of the current working condition according to the temperature data and the running time includes: Calculating the change rate of the temperature signal according to the temperature signal; Calculating the acquisition error between any two temperature sensors based on the temperature data collected by multiple temperature sensors; Obtaining the temperature prediction value according to the temperature data and the running time; When the change rate of the temperature signal is greater than the preset change rate, it is determined that there is a fault; When the acquisition error is greater than the preset acquisition error, it is determined that there is a fault; When the prediction error between the temperature prediction value and the actual temperature is greater than the preset prediction error and the confidence level is the preset confidence level, it is determined that a fault exists; If no fault exists, it is determined that the current working condition is a fast response working condition; If a fault exists and the fault type is one kind, it is determined that the current working condition is a stable control working condition; If a fault exists and the number of fault types is greater than or equal to two kinds, it is determined that the current working condition is a prediction compensation working condition.

6. The control method of the variable frequency compressor according to any one of claims 2 to 5, characterized in that, Selecting a control strategy according to the current working condition and controlling the target variable-frequency compressor includes: If the current working condition is a fast response working condition, double-loop control is used as the control strategy, and the target variable-frequency compressor is controlled based on the double-loop control; wherein, the double-loop control includes temperature outer-loop control and speed inner-loop control; If the current working condition is a stable control working condition, smooth control is used as the control strategy, and the target variable-frequency compressor is controlled based on the smooth control; If the current working condition is a prediction compensation working condition, fuzzy control is used as the control strategy, and the target variable-frequency compressor is controlled based on the fuzzy control.

7. The control method of the variable-frequency compressor according to claim 6, characterized in that, Controlling the target variable-frequency compressor based on the double-loop control includes: Collecting the speed data of the target variable-frequency compressor; Inputting the temperature deviation, temperature change rate, temperature change trend correlation coefficient, and the current speed into the temperature outer loop, and obtaining the target speed based on the pre-determined outer-loop control parameters; According to the target speed, the current speed, and the pre-determined inner-loop control parameters, obtaining an inner-loop control signal; Controlling the speed of the target variable-frequency compressor according to the inner-loop control signal.

8. The control method of the variable-frequency compressor according to claim 6, wherein, Controlling the target variable-frequency compressor based on the smooth control includes: Predicting the current speed of the target variable-frequency compressor to obtain speed prediction data; Using a smooth algorithm, obtaining a speed adjustment amount according to the current speed and the speed prediction data; Controlling the target variable-frequency compressor according to the speed adjustment amount.

9. The control method of the variable-frequency compressor according to claim 6, characterized in that, Controlling the target variable-frequency compressor based on the fuzzy control includes: Calculating the temperature deviation and temperature change rate according to the temperature data and the running time; Using fuzzy control to determine the corresponding membership degrees according to the temperature deviation and the temperature change rate; Controlling the speed of the target variable-frequency compressor according to the membership degrees corresponding to the temperature deviation and the temperature change rate.

10. A control system for a variable-frequency compressor, characterized in that, It includes a variable-frequency compressor and an electronic device, wherein the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method described in any one of claims 1 to 9 is implemented.

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