A permanent magnet motor parameter operation regulation test system and method based on cloud services

Abstract

The present invention discloses a permanent magnet motor parameter operation regulation and control test system and method based on cloud services, which relates to the technical field of motor control. The present invention includes a data acquisition module, a data analysis module, a control node identification module, an optimization strategy generation module, and a real-time monitoring and feedback module; the data acquisition module uses sensors to obtain the historical and real-time operation data of the permanent magnet synchronous motor; the data analysis module conducts a comprehensive validity score on the acquired data and screens the qualified data; the control node identification module sets control nodes based on the regulation and control scheme, extracts relevant parameters and timestamps from the integrated dataset, and analyzes and judges the deviation coefficient to identify key control nodes; the optimization strategy generation module uses a linear regression model to analyze the correlation between the regulation and control scheme and the motor operation state and establish an optimized control strategy; the real-time monitoring and feedback module monitors the real-time operation data, judges faults and outputs alarms in a timely manner, and adjusts the regulation and control scheme based on the monitoring results.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and particularly to a permanent magnet motor parameter operation regulation test system and method based on cloud service. Background Technique

[0002] With the rapid development of industrial automation, permanent magnet synchronous motors have been widely used in many fields such as aerospace, electric vehicles, wind power generation, and industrial automation due to their high efficiency, high power density, and excellent control performance. Especially in drive systems with low power, high precision, high reliability, and wide speed regulation range, the successful application of permanent magnet synchronous motors has attracted great attention in the industry. Moreover, the design, manufacturing, and operation quality of permanent magnet synchronous motors directly affect the performance and reliability of the entire system. Therefore, it is particularly important to conduct comprehensive and accurate tests and detections on them.

[0003] However, in practical applications, the operation parameter regulation of permanent magnet synchronous motors faces many challenges. The traditional permanent magnet motor parameter operation regulation test system is limited by local computing power and cannot process and analyze the massive data generated during the operation of permanent magnet synchronous motors in real time, resulting in inaccurate judgment of the operation state of permanent magnet synchronous motors and limited optimization space for regulation strategies; in the scenarios of distributed or remote control, it is difficult for the traditional permanent magnet motor parameter operation regulation test system to achieve remote monitoring and real-time fault diagnosis of permanent magnet synchronous motors, affecting the efficiency and accuracy of fault troubleshooting; most of the traditional permanent magnet motor parameter operation regulation test systems are based on fixed control strategies and lack the ability to perform intelligent regulation according to the real-time operation state and environmental changes of permanent magnet synchronous motors, thus unable to fully exert the best performance of permanent magnet synchronous motors. Summary of the Invention

[0004] The purpose of the present invention is to provide a permanent magnet motor parameter operation regulation test system and method based on cloud service to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] A permanent magnet motor parameter operation regulation test method based on cloud service, the method includes the following steps:

[0007] Step S100. Obtain the historical operation data of the permanent magnet synchronous motor, analyze the historical operation data, evaluate the validity of the historical operation data, and perform corresponding screening processing on the historical operation data according to the validity; obtain the screened historical operation data, and correspond the screened historical operation data with the regulation scheme of the permanent magnet synchronous motor;

[0008] Step S200. Analyze based on the regulation scheme of the permanent magnet synchronous motor in combination with the corresponding historical operation data, so as to obtain the control node data set corresponding to the regulation scheme of the permanent magnet synchronous motor; analyze the deviation coefficient between the historical operation data and the ideal operation data of the control node data set, and obtain the key control nodes corresponding to the regulation scheme of the permanent magnet synchronous motor based on the deviation coefficient.

[0009] Step S300. Obtain the key control nodes and the regulation scheme of the permanent magnet synchronous motor, analyze the corresponding change trend of the operation state of the permanent magnet synchronous motor, so as to establish the correlation between the regulation scheme of the permanent magnet synchronous motor and the change trend of the operation state, and obtain the corresponding optimal control strategy based on the correlation.

[0010] Step S400. Obtain the real-time operation data of the permanent magnet synchronous motor and identify the real-time key control nodes; analyze the real-time operation data corresponding to the real-time key control nodes to determine whether the current permanent magnet synchronous motor fails; if no failure occurs, adjust the regulation scheme of the permanent magnet synchronous motor accordingly according to the optimal control strategy.

[0011] Further, step S100 includes:

[0012] S101. Use the sensor device to obtain the historical operation data of the permanent magnet synchronous motor within the selected time period, and obtain the reasonable physical boundaries of each operation parameter of the permanent magnet synchronous motor, and the reasonable physical boundaries of each operation parameter of the permanent magnet synchronous motor are all expressed as: Li = [l_min, l_max]i, where Li represents the reasonable physical boundary of the i-th operation parameter of the permanent magnet synchronous motor, and l_min and l_max in [l_min, l_max]i respectively represent the lower limit and the upper limit of the reasonable physical boundary of the i-th operation parameter of the permanent magnet synchronous motor; perform physical validity judgment according to the reasonable physical boundaries of each operation parameter of the permanent magnet synchronous motor, and the physical validity judgment rule is: if the measured value of a certain parameter Li falls within its reasonable physical boundary, then the parameter is valid, and Wi = 1 is assigned; if the measured value of a certain parameter Li is not within its reasonable physical boundary, then the parameter is invalid, and Wi = 0 is assigned.

[0013] S102. Perform time series analysis on the historical operation data of the permanent magnet synchronous motor, and the resulting time series is represented as: D(t)=(t1,P1),(t2,P2),...,(tn,Pn), where D(t) represents the time series data, t1 in (t1,P1) represents the first timestamp, and P1 represents the operation parameter value at time t1; t2 in (t2,P2) represents the second timestamp, and P2 represents the operation parameter value at time t2; and so on, tn in (tn,Pn) represents the nth timestamp, and Pn represents the operation parameter value at time tn, and n is a positive integer; for each pair of adjacent data points Pi and Pi+1, calculate the change rate Ri between them, and Ri = |Pi+1 - Pi|; compare Ri with the corresponding change rate threshold R, if Ri ≤ R, then assign Bi = 1, otherwise assign Bi = 0;

[0014] S103. Use the Z-score method to identify outliers in the historical operation data of the permanent magnet synchronous motor, calculate the mean μ and standard deviation σ of all operation parameters; for each operation parameter Pi, calculate the Z-score, and the calculation formula is: Zi = (Pi - μ) / σ, compare the Zi corresponding to each operation parameter Pi with the preset threshold Z, if |Zi| > Z, then determine that this data point is an outlier and assign Ji = 1, otherwise assign Ji = 0;

[0015] S104. Synthesize Wi, Bi, and Ji to calculate a comprehensive effectiveness score Si for each data point, and Si = α×Wi + β×Bi + γ×Ji, where α, β, and γ are all weight coefficients and α + β + γ = 1; compare the comprehensive effectiveness score Si with the corresponding screening threshold S, and screen the data points that satisfy Si ≥ S; and store the screened data points in the historical operation data set D1, obtain the regulation scheme of the permanent magnet synchronous motor, including information such as control strategies, operation modes, parameter settings, etc., match the data points in the historical operation data set D1 with the control strategies in the corresponding time periods in the regulation scheme through timestamps, and map each operation parameter Pi to the corresponding regulation parameter, so as to form an integrated data set Dc, which contains each screened operation data point and its corresponding regulation scheme information, and the data structure is represented as: Dc={(ti,Pi,Cj)|ti is the timestamp, Pi is the operation parameter, and Cj is the corresponding regulation scheme}.

[0016] Further, step S200 includes:

[0017] S201. Extract all operating parameters and their corresponding timestamps from the integrated dataset Dc, set control nodes based on the regulation scheme of the permanent magnet synchronous motor, divide the operating parameters and their corresponding timestamps extracted from the integrated dataset Dc into time windows to form the dataset Dw, and perform time matching on the dataset Dw and the control nodes in the regulation scheme to form the control node dataset K, where K = {k1, k2,..., km}, k1 represents the first control node data, k2 represents the second control node data, and so on, km represents the mth control node data;

[0018] S202. Based on the control node dataset K, obtain the ideal operating data of the corresponding control nodes to form the ideal dataset G, where G = {g1, g2,..., gm}, g1 represents the ideal operating data corresponding to the first control node, g2 represents the ideal operating data corresponding to the second control node, and so on, gm represents the ideal operating data corresponding to the mth control node. Each control node in the ideal dataset G corresponds one-to-one with the control nodes in the control node dataset K; for each data point in the control node dataset K, calculate the deviation coefficient between the historical operating data and the ideal operating data, and the specific calculation formula is:

[0019] ,

[0020] where Hx(t) represents the deviation coefficient between the historical operating data and the ideal operating data of control node x at time t, kx(t) represents the historical operating data of control node x at time t, gx(t) represents the ideal operating data of control node x at time t, b represents the parameter factor, a represents the weight coefficient, and Vx(t) represents the change rate of the historical operating data of control node x at time t, and Vx(t) = |kx(t) - kx(t - 1)|;

[0021] S203. For each control node in the control node dataset K, calculate the corresponding deviation coefficient according to the deviation coefficient calculation formula in S202; obtain the corresponding fault log, and according to the timestamps of the fault events recorded in the fault log, take the control nodes with the same timestamp as the fault nodes, and summarize the average value of the deviation coefficients corresponding to all fault nodes to obtain the fault threshold H0; for the control nodes in the control node dataset K except the fault nodes, compare the deviation coefficients of the remaining control nodes with the preset deviation threshold H, and take the control nodes greater than or equal to the deviation threshold H as the key control nodes, so as to obtain the key control nodes corresponding to the regulation scheme of the permanent magnet synchronous motor, and the deviation threshold H < the fault threshold H0.

[0022] Further, step S300 includes:

[0023] S301. Extract the key control node dataset K and the corresponding permanent magnet synchronous motor regulation scheme, analyze the time series of the operating parameters of the key control nodes, and define the trend function: T(k) = (1 / c)Σt∈[1,c],k(t), where c is the time window length and k(t) represents the operating parameter value of the key control node at time t; Use a linear regression model to analyze the correlation between the permanent magnet synchronous motor regulation scheme and the change in the operating state of the permanent magnet synchronous motor, and establish the model as: U = w0 + w1·C + d, where U represents the change in the operating state of the permanent magnet synchronous motor, C represents the permanent magnet synchronous motor regulation scheme parameter, w0 and w1 represent regression coefficients, and d represents the error term;

[0024] S302. Calculate the response index Ex of each key control node, and Ex = Δkx / ΔCx, where Δkx represents the change in the operating parameter value of the key control node x, and ΔCx represents the change in the regulation scheme parameter of the key control node x; According to the correlation analysis results, obtain the optimized control strategy E1: E1 = E + λ·(U - T(k)), where λ is the adjustment coefficient. The new strategy E1 is adjusted based on the original control strategy E according to the deviation between the current operating state and the expected trend. By adding the adjustment amount λ·(U - T(k)), the control strategy can be dynamically corrected to make the actual operating state of the motor closer to the ideal state, thereby improving the control effect and system performance.

[0025] Further, step S400 includes:

[0026] S301. Extract the key control node dataset K and the corresponding permanent magnet synchronous motor regulation scheme, analyze the time series of the operating parameters of the key control nodes, and define the trend function: T(k) = (1 / c)Σt∈[1,c],k(t), where c is the time window length and k(t) represents the operating parameter value of the key control node at time t; Use a linear regression model to analyze the correlation between the permanent magnet synchronous motor regulation scheme and the change in the operating state of the permanent magnet synchronous motor, and establish the model as: U = w0 + w1·C + d, where U represents the change in the operating state of the permanent magnet synchronous motor, C represents the permanent magnet synchronous motor regulation scheme parameter, w0 and w1 represent regression coefficients, and d represents the error term;

[0027] S302. Calculate the response index Ex of each key control node, where Ex = Δkx / ΔCx. Here, Δkx represents the change in the operating parameter value of the key control node x, and ΔCx represents the change in the regulation scheme parameter of the key control node x. According to the correlation analysis result, obtain the optimized control strategy E1: E1 = E + λ·(U - T(k)), where λ is the adjustment coefficient. The new strategy E1 is adjusted based on the original control strategy E according to the deviation between the current operating state and the desired trend. By adding the adjustment amount λ·(U - T(k)), the control strategy can be dynamically corrected, making the actual operating state of the motor closer to the ideal state, thereby improving the control effect and system performance.

[0028] Further, step S400 includes:

[0029] S401. Obtain the real-time operating data of the permanent magnet synchronous motor, perform the same analysis on the real-time operating data as the historical operating data, thereby obtaining several real-time control nodes, and calculate the deviation coefficient H2 between the real-time operating data corresponding to the real-time control nodes and the ideal operating data. Compare the deviation coefficient H2 with the fault threshold H0. If the deviation coefficient H2 < the fault threshold H0, then go to S402; if the deviation coefficient H2 ≥ the fault threshold H0, determine that the current permanent magnet synchronous motor has a fault, and output the corresponding prompt information to relevant personnel;

[0030] S402. Compare the deviation coefficient H2 with the preset deviation threshold H. Take the control nodes greater than or equal to the deviation threshold H as key real-time control nodes, calculate the similarity between the real-time operating data of the key real-time control nodes and the historical operating data, select the key control node corresponding to the historical operating data with the maximum similarity as the matching node, and take the optimized control strategy corresponding to the matching node as the matching strategy. Adjust the regulation scheme of the permanent magnet synchronous motor according to the matching strategy.

[0031] A permanent magnet motor parameter operation regulation and test system based on cloud service. The system includes: a data acquisition module, a data analysis module, a control node identification module, an optimized strategy generation module, and a real-time monitoring and feedback module;

[0032] The data acquisition module uses sensor devices to obtain the historical operation data and real-time operation data of the permanent magnet synchronous motor; the data analysis module conducts physical validity judgment, outlier detection, and time series analysis on the collected historical operation data and real-time operation data, generates a comprehensive validity score, filters out the qualified operation data according to the validity score to form an integrated dataset, analyzes the deviation between the historical operation data or real-time operation data and the ideal operation data, calculates the deviation coefficient, and identifies the key control nodes; the control node identification module sets the control nodes based on the regulation scheme, extracts relevant operation parameters and timestamps from the integrated dataset, analyzes the control node dataset, and judges the deviation coefficient to identify the key control nodes; the optimization strategy generation module uses a linear regression model to analyze the correlation between the regulation scheme and the change of the motor operation state, and establishes an optimal control strategy; the real-time monitoring and feedback module monitors and analyzes the real-time operation data of the motor, calculates the deviation coefficient of the real-time control nodes, judges whether the motor fails, if a failure is detected, it timely outputs an alarm message to relevant personnel, and adjusts the regulation scheme based on the real-time monitoring results.

[0033] Furthermore, the data acquisition module includes a sensor data acquisition unit and a data preprocessing unit;

[0034] The sensor data acquisition unit obtains the historical operation data and real-time operation data of the permanent magnet synchronous motor; the data preprocessing unit conducts cleaning, denoising, and formatting processing on the collected historical operation data and real-time operation data;

[0035] The data analysis module includes a validity evaluation unit, a time series analysis unit, and a deviation calculation unit;

[0036] The validity evaluation unit conducts validity judgment on the historical operation data and real-time operation data, generates a validity score, and filters out valid data points; the time series analysis unit conducts time series analysis on the filtered historical operation data and real-time operation data, calculates the data change rate, and identifies outliers; the deviation calculation unit calculates the deviation coefficient between the historical operation data or real-time operation data and the ideal operation data.

[0037] Furthermore, the control node identification module includes a control node setting unit and a key node analysis unit;

[0038] The control node setting unit sets the control nodes according to the regulation scheme, conducts time matching on the dataset, and extracts key control node data; the key node analysis unit identifies the key control nodes and calculates their deviation coefficients.

[0039] The optimization strategy generation module includes a correlation analysis unit and an optimal control strategy generation unit;

[0040] The correlation analysis unit uses a linear regression model to analyze the correlation between the control scheme and the change in the operating state, and establishes a corresponding model; the optimal control strategy generation unit generates an optimal control strategy for the key control nodes according to the correlation analysis results.

[0041] Furthermore, the real-time monitoring and feedback module includes a real-time data analysis unit, a fault monitoring unit, and a control adjustment unit;

[0042] The real-time data analysis unit obtains the real-time operating data of the permanent magnet synchronous motor, compares and analyzes it with the historical data, so as to obtain the corresponding matching information; the fault monitoring unit judges whether the motor has a fault based on the real-time deviation coefficient and issues a fault prompt message in time; the control adjustment unit adjusts the control scheme of the permanent magnet synchronous motor according to the matching information.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows: by introducing a comprehensive effectiveness scoring mechanism (Si), considering factors such as physical effectiveness, time series change rate, and outliers, the present invention effectively screens out high-quality historical operating data, accurately matches it with the control scheme, and constructs an integrated data set Dc, providing a reliable data basis for subsequent analysis. By analyzing the deviation coefficient between the control node data set and the ideal operating data, the present invention can accurately identify the key control nodes of the permanent magnet synchronous motor, establish the correlation between the control scheme and the change trend of the operating state based on these key nodes, and then formulate an optimal control strategy. The operating state of the permanent magnet synchronous motor is monitored in real time. By calculating the deviation coefficient of the real-time control node and comparing it with the fault threshold, early warning and real-time adjustment of the motor fault are realized, reducing the downtime and production losses caused by the fault. By analyzing the historical operating data and real-time operating state of the permanent magnet synchronous motor, the present invention can dynamically adjust the control strategy based on intelligent algorithms (such as linear regression models) to adapt to the changes in the motor operating state and external environment, so as to give full play to the best performance of the permanent magnet synchronous motor. Brief Description of the Drawings

[0044] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0045] Figure 1 It is a schematic diagram of the module of a permanent magnet motor parameter operation regulation and control test system based on cloud service of the present invention. Detailed Embodiments

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figure 1 , the present invention provides a technical solution:

[0048] A permanent magnet motor parameter operation regulation and control test system based on cloud service, the system includes: a data acquisition module, a data analysis module, a control node identification module, an optimization strategy generation module, and a real-time monitoring and feedback module;

[0049] The data acquisition module uses sensor devices to obtain the historical operation data and real-time operation data of the permanent magnet synchronous motor; the data analysis module conducts physical validity judgment, outlier detection and time series analysis on the collected historical operation data and real-time operation data, generates a comprehensive validity score, filters out the qualified operation data according to the validity score to form an integrated data set, analyzes the deviation between the historical operation data or real-time operation data and the ideal operation data, calculates the deviation coefficient, and identifies the key control nodes; the control node identification module sets the control nodes based on the regulation and control plan, extracts the relevant operation parameters and timestamps from the integrated data set, analyzes the control node data set, and judges the deviation coefficient to identify the key control nodes; the optimization strategy generation module uses a linear regression model to analyze the correlation between the regulation and control plan and the change of the motor operation state, and establishes an optimized control strategy; the real-time monitoring and feedback module monitors and analyzes the real-time operation data of the motor, calculates the deviation coefficient of the real-time control node, judges whether the motor fails, if a failure is detected, timely outputs an alarm message to relevant personnel, and adjusts the regulation and control plan based on the real-time monitoring results.

[0050] The data acquisition module includes a sensor data acquisition unit and a data preprocessing unit;

[0051] The sensor data acquisition unit obtains the historical operation data and real-time operation data of the permanent magnet synchronous motor; the data preprocessing unit conducts cleaning, denoising and formatting processing on the collected historical operation data and real-time operation data;

[0052] The data analysis module includes a validity evaluation unit, a time series analysis unit, and a deviation calculation unit;

[0053] The validity evaluation unit judges the validity of historical operation data and real-time operation data, generates a validity score, and filters valid data points; the time series analysis unit performs time series analysis on the filtered historical operation data and real-time operation data, calculates the data change rate, and identifies outliers; the deviation calculation unit calculates the deviation coefficient between the historical operation data or real-time operation data and the ideal operation data.

[0054] The control node identification module includes a control node setting unit and a key node analysis unit;

[0055] The control node setting unit sets control nodes according to the regulation and control scheme, performs time matching on the data set, and extracts key control node data; the key node analysis unit identifies key control nodes and calculates their deviation coefficients.

[0056] The optimization strategy generation module includes a correlation analysis unit and an optimization control strategy generation unit;

[0057] The correlation analysis unit uses a linear regression model to analyze the correlation between the regulation and control scheme and the change of the operation state, and establishes a corresponding model; the optimization control strategy generation unit generates an optimization control strategy for key control nodes according to the correlation analysis results.

[0058] The real-time monitoring and feedback module includes a real-time data analysis unit, a fault monitoring unit, and a control adjustment unit;

[0059] The real-time data analysis unit obtains the real-time operation data of the permanent magnet synchronous motor, and compares and analyzes it with the historical data to obtain corresponding matching information; the fault monitoring unit judges whether the motor fails based on the real-time deviation coefficient and issues a fault prompt message in time; the control adjustment unit adjusts the regulation and control scheme of the permanent magnet synchronous motor according to the matching information.

[0060] A method for regulating and controlling the operation of permanent magnet motor parameters based on cloud service, the method includes the following steps:

[0061] Step S100. Obtain the historical operation data of the permanent magnet synchronous motor, analyze the historical operation data, evaluate the validity of the historical operation data, and perform corresponding screening processing on the historical operation data according to the validity; obtain the filtered historical operation data, and correspond the filtered historical operation data with the regulation and control scheme of the permanent magnet synchronous motor;

[0062] Step S200. Based on the regulation and control scheme of the permanent magnet synchronous motor, analyze it in combination with the corresponding historical operation data, so as to obtain the control node data set corresponding to the regulation and control scheme of the permanent magnet synchronous motor; analyze the deviation coefficient between the historical operation data of the control node data set and the ideal operation data, and obtain the key control nodes corresponding to the regulation and control scheme of the permanent magnet synchronous motor based on the deviation coefficient;

[0063] Step S300. Obtain the key control nodes and the regulation scheme of the permanent magnet synchronous motor, analyze the corresponding change trend of the operating state of the permanent magnet synchronous motor, thereby establish the correlation between the regulation scheme of the permanent magnet synchronous motor and the change trend of the operating state, and obtain the corresponding optimal control strategy based on the correlation;

[0064] Step S400. Obtain the real-time operating data of the permanent magnet synchronous motor, and identify the real-time key control nodes; analyze the real-time operating data corresponding to the real-time key control nodes to determine whether the permanent magnet synchronous motor has a fault at present; if no fault occurs, adjust the regulation scheme of the permanent magnet synchronous motor accordingly according to the optimal control strategy.

[0065] Step S100 includes:

[0066] S101. Use the sensor device to obtain the historical operating data of the permanent magnet synchronous motor within the selected time period, and obtain the reasonable physical limits of each operating parameter of the permanent magnet synchronous motor, and the reasonable physical limits of each operating parameter of the permanent magnet synchronous motor are all expressed as: Li = [l_min, l_max]i, where Li represents the reasonable physical limit of the i-th operating parameter of the permanent magnet synchronous motor, and l_min and l_max in [l_min, l_max]i respectively represent the lower limit and the upper limit of the reasonable physical limit of the i-th operating parameter of the permanent magnet synchronous motor; perform physical validity judgment according to the reasonable physical limits of each operating parameter of the permanent magnet synchronous motor, and the physical validity judgment rule is: if the measured value of a certain parameter Li falls within its reasonable physical limit, then the parameter is valid and is assigned Wi = 1; if the measured value of a certain parameter Li is not within its reasonable physical limit, then the parameter is invalid and is assigned Wi = 0;

[0067] In this embodiment, the reasonable physical limits of each operating parameter of the permanent magnet synchronous motor are expressed as:

[0068] 1. Rotational speed (N):

[0069] Reasonable range: Usually depends on the rated speed and design specifications of the motor;

[0070] Expressed as: N_min ≤ N ≤ N_max, for example: 0, rpm ≤ N ≤ 3000, rp;

[0071] 2. Current (I):

[0072] Reasonable range: Set according to the rated current and overload capacity of the motor.

[0073] Expressed as: I_min ≤ I ≤ I_max, for example: 0, A ≤ I ≤ 10, A;

[0074] 3. Torque (T):

[0075] Reasonable range: Based on the rated torque and maximum output torque of the motor.

[0076] Expressed as: T_min ≤ T ≤ T_max, for example: 0, Nm ≤ T ≤ 15, Nm;

[0077] 3. Temperature (T_temp):

[0078] Reasonable range: According to the operating temperature range of the motor, usually based on the environment and insulation materials.

[0079] Expressed as: T_min,temp ≤ T_temp ≤ T_max,temp, for example: -20, °C ≤ T_temp ≤ 80, °C;

[0080] 4. Efficiency (η):

[0081] Reasonable range: According to the designed efficiency of the motor, usually maintaining high efficiency under high load.

[0082] Expressed as: η_min ≤ η ≤ η_max, for example: 80% ≤ η ≤ 95%.

[0083] S102. Perform time series analysis on the historical operation data of the permanent magnet synchronous motor, and the resulting time series is expressed as: D(t) = (t1, P1), (t2, P2),..., (tn, Pn), where D(t) represents the time series data, t1 in (t1, P1) represents the first timestamp, and P1 represents the value of the operation parameter at time t1; t2 in (t2, P2) represents the second timestamp, and P2 represents the value of the operation parameter at time t2; and so on, tn in (tn, Pn) represents the nth timestamp, and Pn represents the value of the operation parameter at time tn, and n is a positive integer; for each pair of adjacent data points Pi and Pi+1, calculate the change rate Ri between them, and Ri = |Pi+1 - Pi|; compare Ri with the corresponding change rate threshold R, if Ri ≤ R, then assign Bi = 1, otherwise assign Bi = 0;

[0084] S103. Use the Z-score method to identify outliers in the historical operation data of the permanent magnet synchronous motor, calculate the mean μ and standard deviation σ of all operation parameters; for each operation parameter Pi, calculate the Z-score, and the calculation formula is: Zi = (Pi - μ) / σ, compare the Zi corresponding to each operation parameter Pi with the preset threshold Z, if |Zi| > Z, then determine that this data point is an outlier and assign Ji = 1, otherwise assign Ji = 0;

[0085] S104. Combine Wi, Bi, and Ji to calculate a comprehensive effectiveness score Si for each data point, where Si = α × Wi + β × Bi + γ × Ji, and α, β, and γ are all weight coefficients with α + β + γ = 1; compare the comprehensive effectiveness score Si with the corresponding screening threshold S, and screen the data points that satisfy Si ≥ S; and store the screened data points in the historical operation dataset D1, obtain the regulation scheme of the permanent magnet synchronous motor, including information such as control strategies, operation modes, parameter settings, etc., match the data points in the historical operation dataset D1 with the control strategies in the corresponding time periods in the regulation scheme through timestamps, and map each operation parameter Pi to the corresponding regulation parameter, thereby forming an integrated dataset Dc, which contains each screened operation data point and its corresponding regulation scheme information, and the data structure is represented as: Dc = {(ti, Pi, Cj)|ti is the timestamp, Pi is the operation parameter, and Cj is the corresponding regulation scheme}.

[0086] Step S200 includes:

[0087] S201. Extract all operation parameters and their corresponding timestamps from the integrated dataset Dc, set control nodes based on the regulation scheme of the permanent magnet synchronous motor, divide the operation parameters and their corresponding timestamps extracted from the integrated dataset Dc into time windows, thereby forming a dataset Dw, and perform time matching on the dataset Dw and the control nodes in the regulation scheme to form a control node dataset K, and K = {k1, k2,..., km}, where k1 represents the first control node data, k2 represents the second control node data, and so on, and km represents the mth control node data;

[0088] S202. Based on the control node dataset K, obtain the ideal operation data of the corresponding control nodes, thereby forming an ideal dataset G, and G = {g1, g2,..., gm}, where g1 represents the ideal operation data corresponding to the first control node, g2 represents the ideal operation data corresponding to the second control node, and so on, and gm represents the ideal operation data corresponding to the mth control node. Each control node in the ideal dataset G corresponds one-to-one with the control nodes in the control node dataset K; for each data point in the control node dataset K, calculate the deviation coefficient between the historical operation data and the ideal operation data, and the specific calculation formula is:

[0089] ,

[0090] Among them, Hx(t) represents the deviation coefficient between the historical operation data and the ideal operation data of control node x at time t, kx(t) represents the historical operation data of control node x at time t, gx(t) represents the ideal operation data of control node x at time t, b represents the parameter factor, a represents the weight coefficient, Vx(t) represents the change rate of the historical operation data of control node x at time t, and Vx(t)=|kx(t)-kx(t - 1)|;

[0091] S203. For each control node in the control node dataset K, calculate the corresponding deviation coefficient according to the deviation coefficient calculation formula in S202; obtain the corresponding fault log, and according to the time stamp corresponding to the fault event recorded in the fault log, take the control nodes with the same time stamp as the fault nodes, and summarize the average value of the deviation coefficients corresponding to all fault nodes to obtain the fault threshold H0; for the control nodes in the control node dataset K except the fault nodes, compare the deviation coefficients of the remaining control nodes with the preset deviation threshold H, and take the control nodes greater than or equal to the deviation threshold H as the key control nodes, so as to obtain the key control nodes corresponding to the regulation scheme of the permanent magnet synchronous motor, and the deviation threshold H < fault threshold H0.

[0092] Step S300 includes:

[0093] S301. Extract the key control node dataset K and the corresponding permanent magnet synchronous motor regulation scheme, analyze the time series of the operating parameters of the key control nodes, and define the trend function: T(k)=(1 / c)Σt∈[1,c],k(t), where c is the time window length, and k(t) represents the operating parameter value of the key control node at time t; use the linear regression model to analyze the correlation between the permanent magnet synchronous motor regulation scheme and the change of the permanent magnet synchronous motor operating state, and establish the model as: U = w0 + w1·C + d, where U represents the change of the permanent magnet synchronous motor operating state, C represents the permanent magnet synchronous motor regulation scheme parameter, w0 and w1 represent the regression coefficients, and d represents the error term;

[0094] S302. Calculate the response index Ex of each key control node, and Ex = Δkx / ΔCx, where Δkx represents the change amount of the operating parameter value of key control node x, and ΔCx represents the change amount of the regulation scheme parameter of key control node x; according to the correlation analysis result, obtain the optimized control strategy E1: E1 = E + λ·(U - T(k)), where λ is the adjustment coefficient. The new strategy E1 is adjusted based on the original control strategy E according to the deviation between the current operating state and the expected trend. By adding the adjustment amount λ·(U - T(k)), the control strategy can be dynamically corrected to make the actual operating state of the motor closer to the ideal state, thereby improving the control effect and system performance.

[0095] Step S400 includes:

[0096] S401. Obtain the real-time operation data of the permanent magnet synchronous motor, perform the same analysis on the real-time operation data as the historical operation data, so as to obtain several real-time control nodes, calculate the deviation coefficient H2 between the real-time operation data corresponding to the real-time control nodes and the ideal operation data, compare the deviation coefficient H2 with the fault threshold H0. If the deviation coefficient H2 < the fault threshold H0, then go to S402; if the deviation coefficient H2 ≥ the fault threshold H0, it is determined that the current permanent magnet synchronous motor has a fault, and corresponding prompt information is output to relevant personnel;

[0097] S402. Compare the deviation coefficient H2 with the preset deviation threshold H, regard the control nodes greater than or equal to the deviation threshold H as key real-time control nodes, calculate the similarity between the real-time operation data of the key real-time control nodes and the historical operation data, select the key control node corresponding to the historical operation data with the largest similarity as the matching node, and regard the optimization control strategy corresponding to the matching node as the matching strategy, and make corresponding adjustments to the regulation scheme of the permanent magnet synchronous motor according to the matching strategy.

[0098] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0099] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A cloud service-based permanent magnet motor parameter operation control test method, characterized in that: The method comprises the following steps: Step S100. Acquire historical operation data of the permanent magnet synchronous motor, analyze the historical operation data, evaluate the validity of the historical operation data, and perform corresponding screening processing on the historical operation data according to the validity; acquire the screened historical operation data, and match the screened historical operation data with the control scheme of the permanent magnet synchronous motor; Step S200. Based on the control scheme of the permanent magnet synchronous motor, the corresponding historical operation data is analyzed to obtain a control node data set corresponding to the control scheme of the permanent magnet synchronous motor; the deviation coefficient between the historical operation data and the ideal operation data of the control node data set is analyzed, and the key control nodes corresponding to the control scheme of the permanent magnet synchronous motor are obtained based on the deviation coefficient; wherein the analysis process of the key control nodes is as follows: Obtaining historical operation data and ideal operation data corresponding to each control node in the control node data set; calculating a deviation coefficient for each control node, where the deviation coefficient is the degree of deviation between the historical operation data and the ideal operation data of the control node; comparing the calculated deviation coefficient with a preset deviation threshold, selecting a control node whose deviation coefficient is greater than or equal to the deviation threshold as a key control node; Step S300. Acquire the key control nodes and the control scheme of the permanent magnet synchronous motor, analyze the corresponding operating state change trend of the permanent magnet synchronous motor, thereby establishing a correlation between the control scheme of the permanent magnet synchronous motor and the operating state change trend, and obtain a corresponding optimization control strategy based on the correlation; Step S400. Acquire the real-time operation data of the permanent magnet synchronous motor and identify the real-time key control nodes; analyze the real-time operation data corresponding to the real-time key control nodes to determine whether the current permanent magnet synchronous motor has a fault; if no fault occurs, adjust the control scheme of the permanent magnet synchronous motor accordingly according to the optimization control strategy.

2. According to a cloud service-based permanent magnet motor parameter operation control test method according to claim 1, it is characterized in that: The step S100 includes: S101. Use sensor equipment to obtain historical operating data of the permanent magnet synchronous motor in a selected period, and obtain reasonable physical limits of various operating parameters of the permanent magnet synchronous motor, and the reasonable physical limits of various operating parameters of the permanent magnet synchronous motor are expressed as: Li=[l_min,l_max]i, where Li represents the reasonable physical limit of the operating parameter of the ith permanent magnet synchronous motor, and l_min and l_max in [l_min,l_max]i represent the lower limit and upper limit of the reasonable physical limit of the operating parameter of the ith permanent magnet synchronous motor, respectively; physical validity judgment is performed according to the reasonable physical limits of various operating parameters of the permanent magnet synchronous motor, and the physical validity judgment rule is: if the measured value of a certain parameter Li falls within its reasonable physical limit, the parameter is valid, and the value Wi=1 is assigned; if the measured value of a certain parameter Li is not within its reasonable physical limit, the parameter is invalid, and the value Wi=0 is assigned; S102. Perform time series analysis on the historical operating data of the permanent magnet synchronous motor, and the obtained time series is expressed as: D(t)=(t1,P1),(t2,P2),...,(tn,Pn), where D(t) represents the time series data, t1 in (t1,P1) represents the first timestamp, and P1 represents the operating parameter value at time t1; t2 in (t2,P2) represents the second timestamp, and P2 represents the operating parameter value at time t2; and so on, tn in (tn,Pn) represents the nth timestamp, Pn represents the operating parameter value at time tn, and n is a positive integer; for each pair of adjacent data points Pi and Pi+1, calculate the change rate Ri between the two, and Ri=|Pi+1-Pi|; compare Ri with the corresponding change rate threshold R, if Ri≤R, assign Bi=1, otherwise assign Bi=0; S103. Use the Z-score method to identify abnormal values ​​in the historical operating data of the permanent magnet synchronous motor, calculate the mean μ and standard deviation σ of all operating parameters; for each operating parameter Pi, calculate the Z-score, and the calculation formula is: Zi=(Pi-μ) / σ, compare the Zi corresponding to each operating parameter Pi with the preset threshold Z, if |Zi|>Z, then judge that the data point is an abnormal value, assign Ji=1, otherwise assign Ji=0; S104. Comprehensively consider Wi, Bi and Ji, and calculate a comprehensive effectiveness score Si for each data point, where Si=α×Wi+β×Bi+γ×Ji, and α, β and γ are all weight coefficients, α+β+γ=1; compare the comprehensive effectiveness score Si with the corresponding screening threshold S, and screen the data points that satisfy Si≥S; store the screened data points in the historical operation data set D1, obtain the control scheme of the permanent magnet synchronous motor, match the data points in the historical operation data set D1 with the control strategy of the corresponding time period in the control scheme through the timestamp, and map each operating parameter Pi with the corresponding control parameter, thereby forming an integrated data set Dc, which contains each screened operating data point and its corresponding control scheme information, and the data structure is expressed as: Dc={(ti,Pi,Cj)|ti is the timestamp, Pi is the operating parameter, and Cj is the corresponding control scheme}.

3. A cloud service-based permanent magnet motor parameter operation control test method according to claim 2, characterized in that: The step S200 includes: S201. Extract all operating parameters and their corresponding timestamps from the integrated data set Dc, set the control nodes based on the control scheme of the permanent magnet synchronous motor, divide the operating parameters extracted from the integrated data set Dc and their corresponding timestamps into time windows, thereby forming a data set Dw, perform time matching on the data set Dw and the control nodes in the control scheme, and form a control node data set K, where K={k1,k2,...,km}, where k1 represents the first control node data, k2 represents the second control node data, and so on, km represents the mth control node data; S202. Based on the control node data set K, the ideal operation data of the corresponding control node is obtained to form an ideal data set G, and G={g1,g2,...,gm}, wherein g1 represents the ideal operation data corresponding to the first control node, g2 represents the ideal operation data corresponding to the second control node, and so on, gm represents the ideal operation data corresponding to the mth control node, and each control node in the ideal data set G corresponds one-to-one to the control node in the control node data set K; for each data point in the control node data set K, the deviation coefficient between the historical operation data and the ideal operation data is calculated, and the specific calculation formula is: ， Wherein, Hx(t) represents the deviation coefficient between the historical operation data of the control node x at time t and the ideal operation data, kx(t) represents the historical operation data of the control node x at time t, gx(t) represents the ideal operation data of the control node x at time t, b represents the parameter factor, a represents the weight coefficient, Vx(t) represents the change rate of the historical operation data of the control node x at time t, and Vx(t)=|kx(t)-kx(t-1)|; S203. For each control node in the control node data set K, calculate the corresponding deviation coefficient according to the calculation formula of the deviation coefficient in S202; obtain the corresponding fault log, and according to the timestamp corresponding to the fault event recorded in the fault log, take the control node corresponding to the same stamp as the fault node, summarize the deviation coefficients corresponding to all fault nodes and calculate the average value, so as to obtain the fault threshold H0; for the control nodes other than the fault node in the control node data set K, compare the deviation coefficients of the remaining control nodes with the preset deviation threshold H, and take the control nodes greater than or equal to the deviation threshold H as the key control nodes, so as to obtain the key control nodes corresponding to the control scheme of the permanent magnet synchronous motor, and the deviation threshold H is less than the fault threshold H0.

4. A cloud service-based permanent magnet motor parameter operation control test method according to claim 3, characterized in that: The step S300 includes: S301. Extract the key control node data set K and the corresponding permanent magnet synchronous motor control scheme, analyze the time series of the operating parameters of the key control nodes, and define the trend function: T(k)=(1 / c)Σt∈[1,c],k(t), where c is the time window length, and k(t) represents the operating parameter value of the key control node at time t; use the linear regression model to analyze the correlation between the permanent magnet synchronous motor control scheme and the change of the operating state of the permanent magnet synchronous motor, and establish the model: U=w0+w1·C+d, where U represents the change of the operating state of the permanent magnet synchronous motor, C represents the parameters of the permanent magnet synchronous motor control scheme, w0 and w1 represent regression coefficients, and d represents the error term; S302. Calculate the response index Ex of each key control node, and Ex=Δkx / ΔCx, where Δkx represents the change in the operating parameter value of the key control node x, and ΔCx represents the change in the control scheme parameters of the key control node x; according to the correlation analysis results, the optimized control strategy E1 is obtained: E1=E+λ·(UT(k)), where λ is the adjustment coefficient.

5. A cloud service-based permanent magnet motor parameter operation control test method according to claim 4, characterized in that: The step S400 includes: S401. Acquire the real-time operation data of the permanent magnet synchronous motor, perform the same analysis on the real-time operation data as the historical operation data, thereby obtaining several real-time control nodes, and calculate the deviation coefficient H2 between the real-time operation data corresponding to the real-time control node and the ideal operation data, and compare the deviation coefficient H2 with the fault threshold H0. If the deviation coefficient H2 is less than the fault threshold H0, go to S402; if the deviation coefficient H2 is greater than or equal to the fault threshold H0, determine that the current permanent magnet synchronous motor has a fault, and output corresponding prompt information to relevant personnel; S402. Compare the deviation coefficient H2 with the preset deviation threshold H, and take the control nodes that are greater than or equal to the deviation threshold H as the key real-time control nodes, and calculate the similarity between the real-time operation data of the key real-time control nodes and the historical operation data, select the key control node corresponding to the historical operation data with the greatest similarity as the matching node, and take the optimization control strategy corresponding to the matching node as the matching strategy, and make corresponding adjustments to the control scheme of the permanent magnet synchronous motor according to the matching strategy.

6. A cloud-based permanent magnet motor parameter operation control test system, applied to a cloud-based permanent magnet motor parameter operation control test method according to any one of claims 1 to 5, characterized in that: The system includes: a data acquisition module, a data analysis module, a control node identification module, an optimization strategy generation module and a real-time monitoring and feedback module; The data acquisition module uses sensor equipment to obtain historical operating data and real-time operating data of the permanent magnet synchronous motor; the data analysis module performs physical validity judgment, outlier detection and time series analysis on the collected historical operating data and real-time operating data, generates a comprehensive validity score, screens out qualified operating data according to the validity score, forms an integrated data set, analyzes the deviation between the historical operating data or the real-time operating data and the ideal operating data, calculates the deviation coefficient, and identifies the key control nodes; the control node identification module sets the control node based on the control scheme, extracts relevant operating parameters and timestamps from the integrated data set, analyzes the control node data set, and determines the deviation coefficient to identify the key control node; the optimization strategy generation module uses a linear regression model to analyze the correlation between the control scheme and the change of the motor operating state, and establishes an optimization control strategy; the real-time monitoring and feedback module monitors and analyzes the real-time operating data of the motor, calculates the deviation coefficient of the real-time control node, determines whether the motor has a fault, and if a fault is detected, promptly outputs an alarm message to relevant personnel, and adjusts the control scheme based on the real-time monitoring results.

7. The cloud-based permanent magnet motor parameter operation control test system according to claim 6 is characterized by: The data acquisition module includes a sensor data acquisition unit and a data preprocessing unit; The sensor data acquisition unit acquires historical operation data and real-time operation data of the permanent magnet synchronous motor; the data preprocessing unit cleans, removes noise and formats the collected historical operation data and real-time operation data; The data analysis module includes a validity assessment unit, a time series analysis unit and a deviation calculation unit; The effectiveness evaluation unit performs effectiveness judgment on the historical operation data and the real-time operation data, generates effectiveness scores, and screens valid data points; the time series analysis unit performs time series analysis on the screened historical operation data and the real-time operation data, calculates the data change rate, and identifies abnormal values; The deviation calculation unit calculates a deviation coefficient between the historical operation data or the real-time operation data and the ideal operation data.

8. The cloud-based permanent magnet motor parameter operation control test system according to claim 6 is characterized by: The control node identification module includes a control node setting unit and a key node analysis unit; The control node setting unit sets the control node according to the control scheme, performs time matching on the data set, and extracts key control node data; The key node analysis unit identifies key control nodes and calculates their deviation coefficients.

9. The cloud-based permanent magnet motor parameter operation control test system according to claim 6 is characterized by: The optimization strategy generation module includes a correlation analysis unit and an optimization control strategy generation unit; The correlation analysis unit uses a linear regression model to analyze the correlation between the control scheme and the change of the operating state, and establishes a corresponding model; the optimization control strategy generation unit generates an optimization control strategy for the key control node according to the correlation analysis result.

10. The cloud service-based permanent magnet motor parameter operation control test system according to claim 6, characterized in that: The real-time monitoring and feedback module includes a real-time data analysis unit, a fault monitoring unit and a control adjustment unit; The real-time data analysis unit obtains the real-time operation data of the permanent magnet synchronous motor and compares and analyzes it with the historical data to obtain corresponding matching information; the fault monitoring unit determines whether the motor has a fault based on the real-time deviation coefficient and issues a fault prompt information in a timely manner; The control adjustment unit adjusts the control scheme of the permanent magnet synchronous motor according to the matching information.

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