Motor energy-saving optimization and intelligent regulation and control platform

Abstract

The invention relates to the technical field of motor system energy saving, and discloses a motor energy-saving optimization and intelligent regulation and control platform, which comprises a motor data analysis module, a motor system simulation module, a characteristic parameter extraction module and a control strategy generation module, and is characterized in that performance analysis is performed on acquired operation data to obtain performance data; constructing a dynamic simulation model by using the obtained motor system parameters; calculating dynamic characteristics of the motor system according to the performance data and the dynamic simulation model, and performing characteristic parameter extraction on the operation data based on the dynamic characteristics to obtain mode characteristic parameters; and performing control parameter optimization iteration on the dynamic simulation model based on the mode characteristic parameters, performing strategy evaluation on an iteration result, and screening out an optimal control parameter from the candidate parameter set according to a strategy evaluation result. According to the invention, the problem that the existing motor control system is still insufficient in control accuracy in motor energy-saving optimization can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor system energy saving, and in particular to a motor energy saving optimization and intelligent control platform. Background Art

[0002] As one of the most important power equipment in industrial production, the energy efficiency level of the motor directly affects the energy consumption and carbon emissions of the entire industrial system. Therefore, improving the energy efficiency of the motor system is of great significance to achieving the goal of energy conservation and emission reduction.

[0003] At present, most motor control systems still use traditional control strategies, such as fixed-parameter PID controllers. Although this type of control method is simple and easy to use, it often fails to achieve the optimal control effect when facing a complex and changeable working environment, especially under nonlinear and high-uncertainty working conditions. Problems such as over-adjustment, under-adjustment or oscillation are prone to occur, resulting in reduced energy efficiency and energy waste. In addition, traditional control strategies lack adaptability and learning capabilities, making it difficult to make real-time adjustments based on the actual operating status of the motor, and unable to fully utilize the data resources brought by modern information technology. In summary, the current motor control system still has the problem of insufficient control accuracy in motor energy-saving optimization. Summary of the invention

[0004] The present invention provides a motor energy-saving optimization and intelligent control platform, the main purpose of which is to solve the problem that the current motor control system still has insufficient control accuracy in motor energy-saving optimization.

[0005] To achieve the above objectives, the present invention provides a motor energy-saving optimization and intelligent control platform, comprising: Motor data analysis module, motor system simulation module, characteristic parameter extraction module, control strategy generation module, specifically: The motor data analysis module is used to obtain historical motor operation data, perform pattern recognition on the operation data to obtain pattern probability, and calculate performance data of the operation data according to the pattern probability, wherein the pattern recognition is performed using the following formula: ; in, Indicates running data The corresponding mode probability is is the first The weight of the mode, is the total number of weights in the weight matrix, For the The mean probability corresponding to each mode is For the The covariance matrix of the probabilities corresponding to the modes, For the given data Belong to The probability of a pattern; The motor system simulation module is used to build a dynamic simulation model using the collected motor system parameters; A characteristic parameter extraction module, used to calculate dynamic characteristics according to the performance data and the dynamic simulation model, and extract pattern characteristic parameters of the operation data based on the dynamic characteristics; The control strategy generation module is used to optimize the control parameters of the simulation model based on the pattern characteristic parameters, generate a candidate parameter set, and perform parameter screening on the candidate parameter set to obtain the optimal control parameters.

[0006] Optionally, when executing the function of building a dynamic simulation model using the collected motor system parameters, the motor system simulation module is specifically used to: Fitting a preset electromagnetic model according to the motor system parameters to obtain a motor electromagnetic model; Fitting a preset mechanical model according to the motor system parameters to obtain a motor mechanical model; Fitting a preset control model according to the motor system parameters to obtain a motor control model; A simulation environment is built according to the motor control model, the motor mechanical model and the motor electromagnetic model to obtain a dynamic simulation model.

[0007] Optionally, when the characteristic parameter extraction module performs the function of calculating the dynamic characteristics according to the performance data and the dynamic simulation model, it is specifically used to: constructing a state space equation according to the dynamic simulation model and the performance data; Discretizing the state space equation to obtain a discrete time matrix; Using the performance data and the discrete time matrix to perform matrix parameter estimation to obtain a characteristic matrix; Perform eigenvalue decomposition on the characteristic matrix to obtain dynamic characteristics.

[0008] Optionally, when executing the function of constructing a state space equation according to the dynamic simulation model and the performance data, the characteristic parameter extraction module is specifically used to: Extracting input variables and output variables of the motor system from the dynamic simulation model; Acquire the relationship between the input variable and the output variable of the motor system based on the performance data; Defining state variables of a motor system using the dynamic simulation model and the performance data; A state space equation is constructed according to the relationship between the state variable and the input variable and output variable of the motor system.

[0009] Optionally, when the characteristic parameter extraction module performs the function of discretizing the state space equation to obtain a discrete time matrix, it is specifically used to: Determine the time step of the state space equation based on the motor system parameters; Discretizing the state space equation using a time step to generate a discretized continuous time equation; A discrete time matrix is ​​calculated from the discretized continuous time equations.

[0010] Optionally, when the feature parameter extraction module performs the function of extracting the pattern feature parameters of the operating data based on the dynamic characteristics, it is specifically used to: Using the dynamic characteristics to segment the operation data, obtaining time series data of different stages; Calculating a stability index of a motor system using the time series data and the dynamic characteristics; Calculate the efficiency interval critical point of the time series data; Calculating the dynamic response time of the time series data; The dynamic response time, the efficiency range critical point, the stability index, and the performance data corresponding to the operation data are combined to obtain mode characteristic parameters.

[0011] Optionally, when the feature parameter extraction module performs the function of segmenting the operation data using the dynamic characteristics to obtain time series data of different stages, it is specifically used to: Constructing a state evaluation function according to the dynamic characteristics; The state evaluation function is used to detect the change point of the operation data to obtain the state change probability value. The change point detection can be performed using the following formula: ; in, For time point The corresponding state change probability value, is the base of natural logarithms, is the preset constant, For time point The amount of change in running data, Run the data for a preset significance threshold; Constructing a dynamic window adjustment function based on the state change probability value; The operating data is segmented using the dynamic window adjustment function and a preset initialization window to obtain time series data.

[0012] Optionally, when the control strategy generation module performs the function of optimizing the simulation model control parameters based on the pattern characteristic parameters and generating the candidate parameter set, it is specifically used to: Filtering key control parameters from the operation data using the mode characteristic parameters; Generate simulation results using the key control parameters and the dynamic simulation model; Calculating performance indicators of the simulation results; Using the performance index to adjust the key control parameters to obtain candidate control parameters; Return to the step of generating simulation results using the key control parameters and the dynamic simulation model until the number of parameter adjustments reaches a preset threshold, thereby obtaining a candidate parameter set.

[0013] Optionally, when the control strategy generation module performs the function of screening out key control parameters from the operation data using the mode characteristic parameters, it is specifically used to: calculating a correlation score between the pattern feature parameter and the operation data; Filtering out preliminary relevant data from the operation data using the relevance score and a preset threshold; Performing multiple correlation verifications on the preliminary correlation data to obtain verification results; Based on the verification results, key control parameters are screened out from the preliminary relevant data.

[0014] Optionally, when the control strategy generation module performs the function of screening the candidate parameter set to obtain the optimal control parameter, it is specifically used to: Perform motor simulation operation based on the candidate parameter set and calculate the corresponding energy saving effect; Evaluate the performance indicators of each group of candidate control parameters on the stability, response speed and energy consumption of the motor system based on the energy-saving effect; Score each performance indicator according to a preset weight formula to obtain a scoring result; According to the scoring results, the candidate control parameters with the highest comprehensive scores are selected as the optimal control parameters.

[0015] The present invention constructs a highly realistic motor system model through detailed motor system parameters and advanced simulation tools to ensure the accuracy and reliability of the model; by using input performance data and dynamic simulation models, the dynamic characteristics are calculated using the matrix expression method to extract key characteristic parameters that have a significant impact on motor performance, providing accurate data support for the optimization of control strategies. Therefore, the motor energy-saving optimization and intelligent control platform proposed by the present invention can solve the problem of insufficient control accuracy in motor energy-saving optimization in current motor control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A functional module diagram of a motor energy-saving optimization and intelligent control platform provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process flow of pattern recognition provided by an embodiment of the present invention; Figure 3 A schematic diagram of a process for generating dynamic characteristics provided by an embodiment of the present invention; Figure 4 A schematic flow chart of a motor energy-saving optimization and intelligent control method provided by one embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] Reference Figure 1 As shown, it is a functional module diagram of a motor energy-saving optimization and intelligent control platform provided by one embodiment of the present invention. In this embodiment, the motor energy-saving optimization and intelligent control platform 100 can be installed in an electronic device. According to the functions implemented, the motor energy-saving optimization and intelligent control platform 100 may include a motor data analysis module 101, a state calculation module 102, a fault detection module 103, and an automatic adjustment module 104. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0020] In the embodiment of the present invention, the motor data analysis module 101 includes acquiring historical motor operation data, performing pattern recognition on the operation data to obtain pattern probability, and calculating performance data of the operation data according to the pattern probability; In the embodiment of the present invention, the motor system simulation module 102 includes constructing a dynamic simulation model using the collected motor system parameters; In the embodiment of the present invention, the characteristic parameter extraction module 103 comprises calculating dynamic characteristics according to the performance data and the dynamic simulation model, and extracting the pattern characteristic parameters of the operation data based on the dynamic characteristics; In the embodiment of the present invention, the control strategy generation module 104 includes optimizing the simulation model control parameters based on the pattern characteristic parameters, generating a candidate parameter set, and performing parameter screening on the candidate parameter set to obtain the optimal control parameters.

[0021] In detail, the modules described in the motor energy-saving optimization and intelligent control platform 100 described in the embodiment of the present invention adopt the same technical means as the motor energy-saving optimization and intelligent control platform described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0022] In combination with specific embodiments, the various components and specific workflows of the motor energy-saving optimization and intelligent control platform are described below: The motor data analysis module 101 is used to obtain historical motor operation data, perform pattern recognition on the operation data to obtain pattern probability, and calculate performance data of the operation data according to the pattern probability.

[0023] In the embodiment of the present invention, by acquiring operating data, it helps to analyze the long-term performance trend and state changes of the motor, identify the fluctuations and potential problems of the motor performance, and provide support for subsequent optimization.

[0024] In the embodiment of the present invention, the operating data refers to data recording the actual operating conditions of the motor in the past period of time, including operating time, number of starts, motor load data, motor temperature data, voltage and current data, fault and alarm data, etc.

[0025] See also Figure 2 As shown, in the embodiment of the present invention, when the motor data analysis module performs the function of performing performance analysis on the operating data according to the pattern probability to obtain performance data, it is specifically used to: S21, dividing the operation data into modes according to the mode probability to obtain a data mode; S22, performing regression analysis on the data pattern to obtain a performance change trend; S23. Perform structured processing on the operation data based on the performance change trend to obtain performance data.

[0026] In the embodiment of the present invention, the following formula is used for pattern recognition: ; in, Indicates running data The corresponding mode probability is is the first The weight of the mode, is the total number of weights in the weight matrix, For the The mean probability corresponding to each mode is For the The covariance matrix of the probabilities corresponding to the modes, For the given data Belong to The probability of a mode.

[0027] In detail, a multivariable linear regression equation is used to fit the data pattern and the operating data, and the regression coefficient analysis is performed on the fitted multivariable linear regression equation to obtain the performance change trend. Since the positive and negative values ​​of the regression coefficient indicate the direction of the influence of the independent variable on the dependent variable, and the absolute value of the coefficient indicates the intensity of the influence, the regression coefficient is analyzed to obtain the performance change trend.

[0028] In an embodiment of the present invention, the data is reclassified based on the performance change trend. Specifically, the originally scattered and messy motor operation data are grouped according to different performance trends (such as "normal operation", "overload operation", "abnormal state", etc.), and each classified data is organized according to certain rules (such as time, load, speed, etc.) to form a structured table or database, including summarizing the motor operation data of different time periods to form records with fields such as "date-load-temperature-speed-power", etc. After reclassification and structural processing, a unified and representative performance data set, i.e., performance data, is formed.

[0029] In the embodiment of the present invention, the pattern recognition can calculate the probability of each operating data point belonging to a different pattern, identify the complex distribution and multiple patterns in the motor operating data, provide probability distribution information of the data points, and provide a basis for subsequent pattern division.

[0030] In an embodiment of the present invention, based on pattern probability, the cleaned data is divided into different patterns by assigning each data point to the pattern with the highest probability, so that each pattern corresponds to an operating state of the motor (such as normal, overloaded, inefficient, etc.), so that the data is divided into meaningful operating states, which is convenient for subsequent analysis, provides classification results of the motor operating state, and supports state monitoring and fault diagnosis.

[0031] In the embodiments of the present invention, by discovering motor performance degradation or potential failure in advance, the risk of failure can be reduced and the stability and reliability of the motor system can be improved.

[0032] The motor system simulation module 102 is used to construct a dynamic simulation model using the collected motor system parameters.

[0033] In the embodiment of the present invention, an accurate dynamic simulation model is constructed through motor system parameters to simulate the performance of the motor under different operating conditions, avoid high-cost and high-risk actual tests, and perform multiple simulation verifications.

[0034] In an embodiment of the present invention, motor system parameters refer to various key parameters and structural information used to describe the motor and its related systems. This information is the basis for building a dynamic simulation model and determines the accuracy and functional performance of the model, including motor body information, control system motor system parameters, control parameters, motor system environment and load information, etc.

[0035] In the embodiment of the present invention, when the motor system simulation module executes the function of constructing a dynamic simulation model using the collected motor system parameters, it is specifically used to: Fitting a preset electromagnetic model according to the motor system parameters to obtain a motor electromagnetic model; Fitting a preset mechanical model according to the motor system parameters to obtain a motor mechanical model; Fitting a preset control model according to the motor system parameters to obtain a motor control model; A simulation environment is built according to the motor control model, the motor mechanical model and the motor electromagnetic model to obtain a dynamic simulation model.

[0036] In the embodiment of the present invention, the mathematical expression of the electromagnetic model is: ; in, is the stator voltage in the motor system parameters, is the stator resistance in the motor system parameters, is the stator current in the motor system parameters, is the stator inductance in the motor system parameters, is the preset back EMF, The time rate of change of the stator current, is the differential symbol.

[0037] In the embodiment of the present invention, the mathematical expression of the mechanical model is: ; in, is the moment of inertia in the motor system parameters, is the angular velocity obtained by fitting, is the angular acceleration, which describes the rate of change of angular velocity with time, represents a small change in angular velocity, is the electromagnetic torque in the motor system parameters, is the load torque in the motor system parameters, is the preset friction coefficient.

[0038] In the embodiment of the present invention, the mathematical expression of the mathematical model is: ; in, is the preset error result, is the first parameter of the motor system The control signal at each moment, is the proportional gain in the motor system parameters, is the integral gain in the motor system parameters, is the differential gain in the motor system parameters.

[0039] In the embodiment of the present invention, the physical model and the mathematical model are integrated to simulate the real-time behavior of the motor system and output the changes of key variables (such as current, torque, and speed).

[0040] In the embodiment of the present invention, by fitting these models, a more accurate dynamic simulation model can be established based on the actual motor system parameters (such as motor parameters, electrical characteristics, mechanical load, etc.). For example, the electromagnetic model of the motor can accurately describe the relationship between current and voltage, the mechanical model can describe the influence of factors such as rotational inertia and friction on the movement of the motor, and the control model can predict the response of the control system, so that the motor can still maintain stable operation when the load changes.

[0041] The characteristic parameter extraction module 103 is used to calculate dynamic characteristics according to the performance data and the dynamic simulation model, and extract the mode characteristic parameters of the operation data based on the dynamic characteristics.

[0042] In the embodiment of the present invention, the key characteristic parameters of the motor are extracted through calculation based on performance data and a dynamic simulation model to accurately reflect the dynamic characteristics of the motor.

[0043] Ginseng Figure 3 As shown, when the characteristic parameter extraction module performs the function of calculating the dynamic characteristics according to the performance data and the dynamic simulation model, it is specifically used to: S31, constructing a state space equation according to the dynamic simulation model and the performance data; S32, discretizing the state space equation to obtain a discrete time matrix; S33, using the performance data and the discrete time matrix to perform matrix parameter estimation to obtain a characteristic matrix; S34, performing eigenvalue decomposition on the characteristic matrix to obtain dynamic characteristics.

[0044] In the embodiment of the present invention, when the characteristic parameter extraction module performs the function of constructing the state space equation according to the dynamic simulation model and the performance data, it is specifically used to: Extracting input variables and output variables of the motor system from the dynamic simulation model; Acquire the relationship between the input variable and the output variable of the motor system based on the performance data; Defining state variables of a motor system using the dynamic simulation model and the performance data; A state space equation is constructed according to the relationship between the state variable and the input variable and output variable of the motor system.

[0045] In detail, according to the definition in the dynamic simulation model, input variables (such as voltage, current), output variables (such as speed, torque), and state variables (such as rotor position, speed) are automatically identified, and the identified variables are stored in a list or dictionary.

[0046] In detail, the performance data is matched with the input variables and output variables, and statistical methods (such as correlation analysis, regression analysis, etc.) are used to find out the relationship between the input variables and the output variables, and how these relationships change over time.

[0047] In detail, based on the data expression of the dynamic simulation model, state variables, such as rotor position, speed, etc., are preliminarily selected, and the validity of the state variables is verified using the performance data to obtain the final state variables.

[0048] In detail, the state space equation is: ; ; in, is the derivative of the state variable, is the state variable, is the output variable, is the input variable, is the system matrix, which represents the relationship between states.

[0049] In detail, by extracting the input and output variables of the motor system from the dynamic simulation model and combining the performance data to determine the relationship between the two, the dynamic behavior of the motor system can be described more accurately. By constructing the state space equation, using the extracted state variables and their relationship with the input and output variables, a more comprehensive state space equation can be constructed, which enhances the control ability of the motor system in terms of energy efficiency optimization.

[0050] In the embodiment of the present invention, when the characteristic parameter extraction module performs the function of discretizing the state space equation to obtain a discrete time matrix, it is specifically used to: Determine the time step of the state space equation based on the motor system parameters; Discretizing the state space equation using a time step to generate a discretized continuous time equation; A discrete time matrix is ​​calculated from the discretized continuous time equations.

[0051] In detail, according to specific motor system parameters of the motor system, one tenth of the time constant of the fastest dynamic change in the motor system parameters is taken as the time step.

[0052] In detail, the state space equation is discretized using a zero order holder (ZOH) or other discretization methods (such as bilinear transformation), and the discretized continuous time equation is obtained by transforming the system matrix in the state space equation using a bilinear transformation formula.

[0053] In detail, the matrix index is calculated according to the discretized continuous-time equation and the state-space equation by the Taylor series expansion method, and then the matrix integral is calculated by a numerical integration method, such as the trapezoidal rule or the Simpson's rule. The calculated matrix index and matrix integral are recombined with the system matrix to obtain a discrete-time matrix.

[0054] In detail, by discretizing the continuous-time state-space equations, the dynamic behavior of the motor system can be more accurately represented in the digital control system, and the discretized equations are suitable for computer control algorithms, which helps to improve the control accuracy of the system and ensure the precise match between the control signal and the motor output at discrete time steps, avoiding system instability or inaccurate control caused by discretization errors.

[0055] In an embodiment of the present invention, an error function is constructed according to the discrete time matrix, and the error function is optimized using the least squares method or the recursive least squares method based on the configuration data and the performance data to ensure that the discrete time model can reflect the dynamic laws in the performance data, and finally obtain the characteristic matrix.

[0056] In the embodiment of the present invention, the following formula is used for eigenvalue decomposition: ; in, is the feature matrix, is the corresponding diagonal matrix of the eigenvalues, is the eigenvector matrix of the eigenvector matrix decomposition.

[0057] In the embodiment of the present invention, when the feature parameter extraction module performs the function of extracting the pattern feature parameters of the operation data based on the dynamic characteristics, it is specifically used to: Using the dynamic characteristics to segment the operation data, obtaining time series data of different stages; Calculating a stability index of a motor system using the time series data and the dynamic characteristics; Calculate the efficiency interval critical point of the time series data; Calculating the dynamic response time of the time series data; The dynamic response time, the efficiency range critical point, the stability index, and the performance data corresponding to the operation data are combined to obtain mode characteristic parameters.

[0058] In an embodiment of the present invention, historical data is divided into different time periods according to the dynamic characteristics of the motor system. Since the dynamic characteristics can help identify key events or state change points in the data, the operating data is segmented through a time window of variable length (the window length is adaptively adjusted according to the dynamic characteristics) to obtain time series data.

[0059] In the embodiment of the present invention, by segmenting the operating data and calculating the stability index, efficiency interval critical point and dynamic response time in combination with the dynamic characteristics, the performance of the motor system under different operating conditions can be accurately identified. By calculating the stability index, the stability of the motor system at each operating stage can be analyzed to avoid problems such as motor oscillation, overheating, and stalling, and a comprehensive understanding of the working state of the motor at each stage can be obtained, thereby providing key data support for system optimization, fault diagnosis, performance prediction, etc.

[0060] In the embodiment of the present invention, when the characteristic parameter extraction module performs the function of segmenting the operation data using the dynamic characteristics to obtain time series data of different stages, it is specifically used to: Constructing a state evaluation function according to the dynamic characteristics; Using the state evaluation function to perform change point detection on the operating data to obtain a state change probability value; Constructing a dynamic window adjustment function based on the state change probability value; The operating data is segmented using the dynamic window adjustment function and a preset initialization window to obtain time series data.

[0061] In detail, the state evaluation function is used to describe the operating state of the motor system at different time points. These states are usually evaluated based on some characteristic values ​​(such as speed, load, current, etc.), and the state evaluation function is finally obtained by weighted calculation of the state characteristic data. In detail, change point detection refers to identifying significant changes in the operation of the motor (such as load mutation, speed regulation process, etc.), and calculating the "state change probability value" at each time point through the state evaluation function, that is, reflecting the degree of state change between the current time point and the previous time point.

[0062] In detail, by constructing a state evaluation function and combining it with change point detection, the state changes (such as load changes, temperature fluctuations, etc.) that occur during the operation of the motor system can be accurately identified. This dynamic segmentation processing can segment the system's operating data into multiple meaningful time windows, thereby providing detailed data support for further analysis (such as performance optimization, fault prediction, etc.).

[0063] In detail, the following formula can be used for change point detection: ; in, For time point The corresponding state change probability value, is the base of natural logarithms, is the preset constant, For time point The amount of change in running data, Run the data for a preset significance threshold.

[0064] In detail, a piecewise function is constructed based on the calculated state change probability value and a preset threshold value to obtain a dynamic window adjustment function.

[0065] In detail, the use of the dynamic window adjustment function and the preset initialization window to segment the operating data is through the initialization window, calculating S(t) and the state change probability value and the current dynamic characteristics at each moment, and accumulating data in the initialization window using the dynamic window adjustment function. When the accumulated data reaches the last data, a time series segment is generated, and overlap control is performed, that is, if adjacent segments overlap by >15%, they are merged into a single segment to finally obtain time series data.

[0066] In the embodiment of the present invention, time series analysis is performed on the data of each time period to calculate the autocorrelation coefficient.

[0067] In the embodiment of the present invention, the system efficiency in each time period is calculated, which is usually defined as the ratio of output power to input power. Then, a sliding window is used to calculate the average efficiency in each time period to detect the mutation point of efficiency change.

[0068] In an embodiment of the present invention, a step input (such as a sudden increase in load) is applied to the motor system, the time required for the system to change from an initial state to a stable state is recorded, the rise time, peak time and adjustment time are calculated, and the calculated time is stored in the form of a vector to obtain the dynamic response time.

[0069] In an embodiment of the present invention, the dynamic response time, the critical point of the efficiency range, the stability index and the performance data corresponding to the operating data (such as energy consumption, temperature, etc.) are integrated together, and each characteristic parameter is obtained as a dimension by combining all characteristic parameters into a characteristic vector.

[0070] In the embodiment of the present invention, accurate motor status monitoring can be achieved through these characteristic parameters, providing data support for early fault diagnosis and maintenance.

[0071] The control strategy generation module 104 is used to optimize the simulation model control parameters based on the mode characteristic parameters, generate a candidate parameter set, and perform parameter screening on the candidate parameter set to obtain optimal control parameters.

[0072] In the embodiment of the present invention, the effect of the control strategy is improved by optimizing the extracted characteristic parameters, adjusting the dynamic simulation model and generating a candidate parameter set.

[0073] In the embodiment of the present invention, when the control strategy generation module performs the function of optimizing the simulation model control parameters based on the pattern characteristic parameters and generating the candidate parameter set, it is specifically used to: Filtering key control parameters from the operation data using the mode characteristic parameters; Generate simulation results using the key control parameters and the dynamic simulation model; Calculating performance indicators of the simulation results; Using the performance index to adjust the key control parameters to obtain candidate control parameters; Return to the step of generating simulation results using the key control parameters and the dynamic simulation model until the number of parameter adjustments reaches a preset threshold, thereby obtaining a candidate parameter set.

[0074] In an embodiment of the present invention, a feature selection method is used to select control parameters that are highly correlated with pattern characteristic parameters from historical data. By calculating the correlation coefficient between each control parameter and the pattern characteristic parameter, parameters with a correlation coefficient greater than a preset correlation threshold are selected to obtain key control parameters.

[0075] In the embodiment of the present invention, when the control strategy generation module performs the function of filtering out key control parameters from the operation data using the mode characteristic parameters, it is specifically used to: calculating a correlation score between the pattern feature parameter and the operation data; Filtering out preliminary relevant data from the operation data using the relevance score and a preset threshold; Performing multiple correlation verifications on the preliminary correlation data to obtain verification results; Based on the verification results, key control parameters are screened out from the preliminary relevant data.

[0076] In detail, the pattern feature parameters and the operating data are aligned along the time axis, the covariance of the pattern feature parameters and the operating data, the standard deviation of the pattern feature parameters and the standard deviation of the operating data are calculated respectively, the product of the standard deviation of the pattern feature parameters and the standard deviation of the operating data is calculated, and the covariance is divided by the product of the standard deviation to obtain the relevant score.

[0077] In detail, the marginal probability distribution and the joint probability distribution of the pattern feature parameters and the preliminary correlation data are calculated, the univariate entropy and the joint entropy of the pattern feature parameters and the preliminary correlation data are calculated respectively according to the marginal probability distribution and the joint probability distribution, the mutual information entropy of the pattern feature parameters and the preliminary correlation data is calculated according to the univariate entropy and the joint entropy, the trend mapping rules of the pattern feature parameters and the preliminary correlation data are established according to the mutual information entropy, the preliminary correlation data are matched according to the trend mapping rules and the mutual information entropy, and the verification result is obtained.

[0078] In the embodiment of the present invention, the key control parameters are input into the dynamic simulation model to obtain simulation results. In the embodiment of the present invention, for each simulation result, a series of performance indicators are calculated to evaluate the system performance under the setting. These performance indicators may include but are not limited to energy efficiency ratio, response time, stability and energy consumption.

[0079] In an embodiment of the present invention, each control parameter is applied to a dynamic simulation model, the performance index corresponding to each control parameter is recorded, the fitness value of each control parameter is calculated according to the performance index, a part of the control parameters are selected as parameters to be processed according to the fitness value, the parameters to be processed are randomly paired, a new control parameter combination is generated through a crossover operation, and the control parameter combination is used as a candidate control parameter.

[0080] In the embodiment of the present invention, through the optimization and iteration of control parameters, the system can not only improve energy efficiency, but also maintain the stability of the motor system under variable working environments. The optimized control strategy can effectively cope with the influence of different load fluctuations, temperature changes, etc., avoid motor failure due to overload or imbalance, and thus improve the overall reliability of the system.

[0081] For example, in the historical operation data of the motor system, the pattern characteristic parameters can help screen out the most critical control parameters, such as the motor's speed, torque, current, etc. These parameters directly affect the motor's power consumption and efficiency. By adjusting the speed and torque control methods, the simulation system can predict the motor's response and energy efficiency under load changes. The simulation results are evaluated through a series of performance indicators (such as efficiency, power factor, energy consumption, etc.), that is, the motor performance under each set of candidate control parameters is calculated to find the optimal operating range. According to the energy efficiency calculation results of the motor, the current and speed control strategies in the control system are adjusted to perform energy-saving optimization. Through continuous iteration and adjustment, a candidate parameter set is generated. After several optimization iterations, a set of adjusted and verified candidate parameter sets is finally obtained. The candidate parameter set can maximize the energy efficiency of the motor and ensure the stability and efficiency of the motor system in actual operation.

[0082] In the embodiment of the present invention, when the control strategy generation module performs the function of screening the candidate parameter set to obtain the optimal control parameter, it is specifically used to: Perform motor simulation operation based on the candidate parameter set and calculate the corresponding energy saving effect; Evaluate the performance indicators of each group of candidate control parameters on the stability, response speed and energy consumption of the motor system based on the energy-saving effect; Score each performance indicator according to a preset weight formula to obtain a scoring result; According to the scoring results, the candidate control parameters with the highest comprehensive scores are selected as the optimal control parameters.

[0083] In an embodiment of the present invention, a set of candidate control parameters are first selected, and then a motor simulation model is used to simulate the performance of these parameters in actual operation to obtain operation data, and the energy saving ratio corresponding to each operation data is calculated, and the energy saving ratio is used as the energy saving effect.

[0084] In an embodiment of the present invention, the influence of each set of candidate control parameters on the stability, response speed and energy consumption of the motor system is evaluated. The response speed refers to the reaction time of the motor simulation model simulation system after receiving the input signal, the electric energy consumed when performing the task, and whether different loads can work normally, so as to obtain the comprehensive performance.

[0085] In the embodiment of the present invention, the comprehensive performance is weighted and calculated to obtain a scoring result.

[0086] In the embodiment of the present invention, after obtaining the comprehensive score of each group of candidate control parameters, they can be sorted according to the score. The control parameter group with the highest comprehensive score will be selected as the optimal control parameter, which means that it has achieved the best balance in multiple dimensions and can provide the best performance (such as energy saving, response speed, stability, etc.).

[0087] In the embodiment of the present invention, by screening the optimal control parameters, it is ensured that the motor system operates efficiently and stably under different working conditions, thereby extending the service life of the motor and reducing the maintenance cost.

[0088] like Figure 4 FIG. 1 is a flow chart of a motor energy-saving optimization and intelligent control method provided by an embodiment of the present invention. In an embodiment of the present invention, the motor energy-saving optimization and intelligent control method includes: S401, acquiring historical motor operation data, performing pattern recognition on the operation data to obtain pattern probability, and calculating performance data of the operation data according to the pattern probability, wherein pattern recognition is performed using the following formula: ; in, Indicates running data The corresponding mode probability is is the first The weight of the mode, is the total number of weights in the weight matrix, For the The mean probability corresponding to each mode is For the The covariance matrix of the probabilities corresponding to the modes, For the given data Belong to The probability of a pattern; S402, constructing a dynamic simulation model using the collected motor system parameters; S403, calculating dynamic characteristics according to the performance data and the dynamic simulation model, and extracting pattern characteristic parameters of the operation data based on the dynamic characteristics; S404, optimizing the control parameters of the simulation model based on the pattern characteristic parameters, generating a candidate parameter set, and performing parameter screening on the candidate parameter set to obtain optimal control parameters.

[0089] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0090] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0091] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in the system can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A motor energy-saving optimization and intelligent control platform, characterized in that: The platform includes: a motor data analysis module, a motor system simulation module, a characteristic parameter extraction module, and a control strategy generation module. Specifically: The motor data analysis module is used to obtain historical motor operation data, perform pattern recognition on the operation data to obtain pattern probability, and calculate performance data of the operation data according to the pattern probability, wherein the pattern recognition is performed using the following formula: ； in, Indicates running data The corresponding mode probability is is the first The weight of the mode, is the total number of weights in the weight matrix, For the The mean probability corresponding to each mode is For the The covariance matrix of the probabilities corresponding to the modes, For the given data Belong to The probability of a pattern; The motor system simulation module is used to build a dynamic simulation model using the collected motor system parameters; A characteristic parameter extraction module, used to calculate dynamic characteristics according to the performance data and the dynamic simulation model, and extract pattern characteristic parameters of the operation data based on the dynamic characteristics; The control strategy generation module is used to optimize the control parameters of the simulation model based on the pattern characteristic parameters, generate a candidate parameter set, and perform parameter screening on the candidate parameter set to obtain the optimal control parameters.

2. A motor energy-saving optimization and intelligent control platform as claimed in claim 1, characterized in that: When executing the function of building a dynamic simulation model using the collected motor system parameters, the motor system simulation module is specifically used to: Fitting a preset electromagnetic model according to the motor system parameters to obtain a motor electromagnetic model; Fitting a preset mechanical model according to the motor system parameters to obtain a motor mechanical model; Fitting a preset control model according to the motor system parameters to obtain a motor control model; A simulation environment is built according to the motor control model, the motor mechanical model and the motor electromagnetic model to obtain a dynamic simulation model.

3. A motor energy-saving optimization and intelligent control platform as claimed in claim 1, characterized in that: When the characteristic parameter extraction module performs the function of calculating the dynamic characteristics according to the performance data and the dynamic simulation model, it is specifically used to: constructing a state space equation according to the dynamic simulation model and the performance data; Discretizing the state space equation to obtain a discrete time matrix; Using the performance data and the discrete time matrix to perform matrix parameter estimation to obtain a characteristic matrix; Perform eigenvalue decomposition on the characteristic matrix to obtain dynamic characteristics.

4. A motor energy-saving optimization and intelligent control platform as claimed in claim 3, characterized in that: When executing the function of constructing the state space equation according to the dynamic simulation model and the performance data, the characteristic parameter extraction module is specifically used to: Extracting input variables and output variables of the motor system from the dynamic simulation model; Acquire the relationship between the input variable and the output variable of the motor system based on the performance data; Defining state variables of a motor system using the dynamic simulation model and the performance data; A state space equation is constructed according to the relationship between the state variable and the input variable and output variable of the motor system.

5. A motor energy-saving optimization and intelligent control platform as claimed in claim 3, characterized in that: When the characteristic parameter extraction module performs the function of discretizing the state space equation to obtain a discrete time matrix, it is specifically used to: Determine the time step of the state space equation based on the motor system parameters; Discretizing the state space equation using a time step to generate a discretized continuous time equation; A discrete time matrix is ​​calculated from the discretized continuous time equations.

6. The motor energy-saving optimization and intelligent control platform according to claim 1, characterized in that: When the feature parameter extraction module performs the function of extracting the pattern feature parameters of the operation data based on the dynamic characteristics, it is specifically used to: Using the dynamic characteristics to segment the operation data, obtaining time series data of different stages; Calculating a stability index of a motor system using the time series data and the dynamic characteristics; Calculate the efficiency interval critical point of the time series data; Calculating the dynamic response time of the time series data; The dynamic response time, the efficiency range critical point, the stability index, and the performance data corresponding to the operation data are combined to obtain mode characteristic parameters.

7. A motor energy-saving optimization and intelligent control platform as claimed in claim 6, characterized in that: When the characteristic parameter extraction module performs the function of segmenting the operation data by using the dynamic characteristics to obtain time series data of different stages, it is specifically used to: Constructing a state evaluation function according to the dynamic characteristics; The state evaluation function is used to detect the change point of the operation data to obtain the state change probability value. The change point detection can be performed using the following formula: ； in, For time point The corresponding state change probability value, is the base of natural logarithms, is the preset constant, For time point The amount of change in running data, is the preset significance threshold; Constructing a dynamic window adjustment function based on the state change probability value; The operating data is segmented using the dynamic window adjustment function and a preset initialization window to obtain time series data.

8. The motor energy-saving optimization and intelligent control platform according to claim 1, characterized in that: When the control strategy generation module performs the function of optimizing the simulation model control parameters based on the pattern characteristic parameters and generating the candidate parameter set, it is specifically used to: Filtering key control parameters from the operation data using the mode characteristic parameters; Generate simulation results using the key control parameters and the dynamic simulation model; Calculating performance indicators of the simulation results; Using the performance index to adjust the key control parameters to obtain candidate control parameters; Return to the step of generating simulation results using the key control parameters and the dynamic simulation model until the number of parameter adjustments reaches a preset threshold, thereby obtaining a candidate parameter set.

9. A motor energy-saving optimization and intelligent control platform as claimed in claim 8, characterized in that: When the control strategy generation module performs the function of screening out key control parameters from the operation data using the mode characteristic parameters, it is specifically used to: calculating a correlation score between the pattern feature parameter and the operation data; Filtering out preliminary relevant data from the operation data using the relevance score and a preset threshold; Performing multiple correlation verifications on the preliminary correlation data to obtain verification results; Based on the verification results, key control parameters are screened out from the preliminary relevant data.

10. The motor energy-saving optimization and intelligent control platform according to claim 1, characterized in that: When the control strategy generation module performs the function of screening the candidate parameter set to obtain the optimal control parameter, it is specifically used to: Perform motor simulation operation based on the candidate parameter set and calculate the corresponding energy saving effect; Evaluate the performance indicators of each group of candidate control parameters on the stability, response speed and energy consumption of the motor system based on the energy-saving effect; Score each performance indicator according to a preset weight formula to obtain a scoring result; According to the scoring results, the candidate control parameters with the highest comprehensive scores are selected as the optimal control parameters.

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