Motor adaptive energy-saving control method and system fusing environment perception

Abstract

The application relates to the technical field of motor control, and discloses a motor adaptive energy-saving control method and system fusing environment perception, which comprises the following steps: collecting real-time electrical parameters and operation state parameters of a motor to construct real-time data of the motor, and collecting environment multi-dimensional physical quantities of the motor in a working environment; determining an environment influence factor of the motor load according to a coupling relationship between the environment multi-dimensional physical quantities and the motor load; fusing and analyzing the environment influence factor and a preset energy efficiency optimization target to generate a preliminary regulation strategy of the motor; dynamically adjusting key parameters in the preliminary regulation strategy according to the real-time data to obtain an optimized control parameter combination of the motor; and adjusting the motor in real time according to the optimized control parameter combination; and the application can improve the efficiency of the motor adaptive energy-saving control fusing environment perception.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to a motor adaptive energy-saving control method and system that integrates environmental perception. Background Technology

[0002] Existing motor energy-saving control methods often focus solely on regulating the motor's electrical and operational parameters during the design process, failing to fully consider the coupling relationship between multidimensional physical quantities in the working environment and the motor load. This results in the control logic lacking the ability to adapt to dynamic environmental changes. When environmental conditions fluctuate, existing control strategies cannot accurately capture the real-time impact of environmental factors on the motor load, causing a deviation between the regulation direction and actual needs, making it difficult to maximize motor energy efficiency.

[0003] Meanwhile, existing technologies mostly use fixed values ​​for control parameters based on historical data, lacking a dynamic adjustment mechanism based on the motor's real-time operating response and environmental influencing factors. In complex and ever-changing working scenarios, fixed parameter control modes cannot adapt to the dynamic changes in motor load in a timely manner, causing the motor to operate in a suboptimal state for a long time. This not only limits energy-saving effects but may also reduce the motor's lifespan due to continuous mismatched operation, making it difficult to meet the core requirements of efficient energy saving and stable operation of motors in industrial production and other scenarios. Summary of the Invention

[0004] This invention provides a motor adaptive energy-saving control method and system that integrates environmental perception to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a motor adaptive energy-saving control method integrating environmental perception, comprising: S1: Collect real-time electrical parameters and operating status parameters of the motor to construct real-time data of the motor, and collect multi-dimensional physical quantities of the motor in the working environment. S2: Determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load; S3: Perform a fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor; S4: Dynamically adjust the key parameters in the preliminary control strategy based on the real-time data to obtain the optimal control parameter combination for the motor; S5: Adjust the motor in real time based on the optimized control parameter set.

[0006] In a preferred embodiment, the real-time electrical parameters and operating status parameters of the motor are collected to construct real-time data of the motor, and multi-dimensional physical quantities of the motor's environment in the working environment are collected, including: The three-phase stator current, stator voltage, and speed of the motor are collected to form the first electrical data set of the motor; The winding temperature and bearing vibration intensity of the motor are collected to form the first state dataset of the motor; The air temperature, relative humidity, and air velocity in the working environment of the motor are collected to form the first raw environmental dataset of the working environment; The first electrical dataset, the first state dataset, and the first environmental dataset are time-aligned based on a unified timestamp to obtain the original data set of the motor. The data in the original dataset are preprocessed with dimension normalization to obtain the real-time data of the motor and the multidimensional physical quantities of the environment.

[0007] In a preferred embodiment, determining the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load includes: From the real-time data, extract load characteristic parameters representing the motor load level; Establish a sliding time window correlation dataset between the environmental multidimensional physical quantities and the load characteristic parameters; On the associated dataset within the sliding time window, the causal contribution between environmental parameters and load characteristic parameters is evaluated to obtain the real-time influence coefficient of each dimension of environmental parameter on the current motor load.

[0008] In a preferred embodiment, after evaluating the causal contribution between environmental parameters and load characteristic parameters on the sliding time window associated dataset to obtain the real-time influence coefficient of each dimension of environmental parameter on the current motor load, the method further includes: The influence intensity is dynamically corrected based on the instantaneous values ​​and historical trends of the environmental multidimensional physical quantities; The influence coefficients after dynamic correction are fused; Based on feedback from historical synthesis effects, the coefficients after fusion are adjusted online to obtain the environmental impact factor of motor load.

[0009] In a preferred embodiment, the environmental impact factor is calculated using the following formula: ; In the formula, For the first Environmental impact factors within a time window For the first Real-time impact coefficients of each dimension The real-time impact coefficient is obtained based on experience. For time window The first one obtained inside Real-time dynamic correlation coefficient between environmental parameters and motor load The change sensitivity weighting coefficient is obtained based on experience. For the first Historical trends of environmental parameters For the first The measured values ​​of each environmental parameter, For the first The mean and standard deviation of an environmental parameter in long-term historical data. For the first The standard deviation of an environmental parameter in long-term historical data.

[0010] In a preferred embodiment, the step of fusing and analyzing the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor includes: Based on the historical operating data and energy consumption records of the motor, a benchmark energy efficiency curve of the motor under different load ranges is established. The preset energy efficiency optimization target is analyzed as the optimization direction and constraint boundary for the benchmark energy efficiency curve; The environmental impact factors are used as dynamic correction variables and mapped onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factors. Based on the deformation characteristics, the optimization direction, and the constraint boundary, search the set of optimal operating points that satisfy the current conditions in the control parameter space; The optimal operating point set is converted into a corresponding control command sequence to obtain the initial control strategy of the motor.

[0011] In a preferred embodiment, the step of mapping the environmental impact factor as a dynamic correction variable onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factor includes: Extract the key feature parameters of the benchmark energy efficiency curve; Establish the nonlinear influence relationship between the environmental impact factors and each of the key characteristic parameters; Based on the nonlinear influence relationship and the specific values ​​of the current environmental impact factors, the key characteristic parameters are adjusted in real time to obtain a dynamically adjusted energy efficiency curve. The shape change of the energy efficiency curve is output as a deformation feature.

[0012] In a preferred embodiment, the step of dynamically adjusting the key parameters in the preliminary control strategy based on the real-time data to obtain the optimized control parameter combination for the motor includes: Based on the motor operating status parameters in the real-time data, the actual response of the motor during the execution of the initial control strategy is continuously monitored. The actual response is compared with the target response of the preliminary control strategy to determine the response deviation of the motor; Based on the direction and magnitude of the response deviation, determine the adjustment direction and magnitude of the key parameters in the preliminary control strategy; According to the adjustment direction and adjustment range, the key parameters are fine-tuned in real time; By collecting key parameters that have been fine-tuned in real time, the optimal combination of control parameters for the motor is obtained.

[0013] In a preferred embodiment, adjusting the motor in real time according to the optimized control parameter set includes: Each parameter in the optimized control parameter set is converted into a control command that the motor driver can recognize; The control commands are sent sequentially to the motor driver; The motor driver updates the key variables used to generate the drive signal internally according to the received control command, and generates an updated motor drive signal. The updated motor drive signal is used to drive the motor to change its operating state accordingly.

[0014] To address the above problems, the present invention also provides a motor adaptive energy-saving control system that integrates environmental perception, the system comprising: The multi-source data acquisition module is used to collect the real-time electrical parameters and operating status parameters of the motor to construct the real-time data of the motor, and to collect the multi-dimensional physical quantities of the motor in the working environment. The environmental coupling analysis module is used to determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load. The fusion decision module is used to perform fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor. The parameter dynamic optimization module is used to dynamically adjust the key parameters in the preliminary control strategy based on the real-time data to obtain the optimized control parameter combination of the motor. The real-time control execution module is used to adjust the motor in real time based on the optimized control parameter set.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention comprehensively collects real-time electrical parameters, operating status parameters, and multidimensional physical quantities in the working environment of the motor. After time-series alignment and dimensional normalization, the data is integrated to accurately quantify the coupling relationship between the environment and the motor load. The generated environmental impact factor truly reflects the dynamic effect of the environment on motor operation. Based on the fusion analysis of this impact factor and energy efficiency optimization objectives, the motor control strategy has stronger environmental adaptability, significantly improving the accuracy of energy-saving control and effectively optimizing the energy efficiency performance of motor operation.

[0016] 2. This invention constructs a closed-loop control mechanism of "preliminary control strategy and real-time dynamic correction." By continuously monitoring the motor's operating response, it performs real-time fine-tuning and online optimization of key control parameters based on real-time data, ensuring that control commands are always synchronized with the motor's real-time operating status and environmental changes. This adaptive adjustment mode keeps the motor continuously in its optimal operating range, reducing ineffective energy consumption, improving operational stability, reducing wear and tear on the motor due to mismatched operation, extending the motor's effective service life, and enhancing the practicality and reliability of the control method. Attached Figure Description

[0017] Figure 1 A flowchart illustrating an adaptive energy-saving control method for motors that integrates environmental perception, provided in an embodiment of the present invention; Figure 2 A functional block diagram of a motor adaptive energy-saving control system integrating environmental perception is provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a motor adaptive energy-saving control method integrating environmental perception. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the motor adaptive energy-saving control method integrating environmental perception can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a motor adaptive energy-saving control method incorporating environmental perception, according to an embodiment of the present invention. In this embodiment, the motor adaptive energy-saving control method incorporating environmental perception includes: S1: Collect real-time electrical parameters and operating status parameters of the motor to construct real-time data of the motor, and collect multi-dimensional physical quantities of the motor in the working environment. In this embodiment of the invention, the real-time electrical parameters and operating status parameters of the motor are collected to construct the real-time data of the motor, and multi-dimensional physical quantities of the motor's environment in the working environment are collected, including: The three-phase stator current, stator voltage, and speed of the motor are collected to form the first electrical data set of the motor; The winding temperature and bearing vibration intensity of the motor are collected to form the first state dataset of the motor; The air temperature, relative humidity, and air velocity in the working environment of the motor are collected to form the first raw environmental dataset of the working environment; The first electrical dataset, the first state dataset, and the first environmental dataset are time-aligned based on a unified timestamp to obtain the original data set of the motor. The data in the original dataset are preprocessed with dimension normalization to obtain the real-time data of the motor and the multidimensional physical quantities of the environment.

[0021] High-precision current sensors are connected in series in the three-phase stator winding circuit of the motor. The sensing end of the sensor is in close contact with the winding conductor to ensure real-time capture of dynamic changes in current. At the same time, voltage sensors are connected in parallel at both ends of the three-phase stator winding. The sensing end of the sensor is reliably connected to the winding terminal to accurately collect voltage fluctuations. A speed sensor is fixed at the output shaft of the motor. The sensor's probe is aligned with the outer edge mark of the shaft. The speed information is obtained by detecting the rotation frequency of the mark in real time. All sensors are connected to the data acquisition module through shielded cables. The acquisition module receives the electrical signals converted by the sensors at a preset fixed frequency and accurately records the three values ​​of the three-phase stator current, the three values ​​of the stator voltage, and the speed value at each moment. All recorded values ​​are arranged in chronological order to form the first electrical dataset of the motor.

[0022] Temperature sensors are uniformly embedded inside the stator windings of the motor, with the sensing elements of the sensors in direct contact with the winding conductors. A spare temperature sensor is also attached to the winding surface to ensure measurement accuracy. Vibration sensors are fixed to the front and rear bearing end covers of the motor, with the mounting surfaces of the sensors tightly fitted to the end cover surfaces and kept horizontal to ensure effective sensing of the vibration amplitude during bearing operation. The temperature sensors convert the thermal state of the windings into corresponding electrical signals, and the vibration sensors convert the vibration intensity of the bearings into collectable electrical signals. These electrical signals are transmitted in real-time to the data acquisition module via a dedicated transmission line. The acquisition module analyzes the received signals in real-time, recording the winding temperature and bearing vibration intensity values ​​at each moment. All recorded values ​​are integrated in chronological order to form the motor's first state dataset.

[0023] Three environmental monitoring points are evenly distributed in the surrounding area of ​​the motor's working environment, 1-2 meters away from the motor casing and in an unobstructed location. Each monitoring point is equipped with an air temperature sensor, an air relative humidity sensor, and an ambient air velocity sensor. The temperature sensor obtains temperature information by sensing the heat conduction of the surrounding air, the humidity sensor measures relative humidity by absorbing water vapor in the air and converting it into an electrical signal, and the velocity sensor calculates the flow velocity by detecting the pressure difference generated when the air flows. All environmental sensors are connected to a data acquisition module. The acquisition module receives and records the air temperature, air relative humidity, and ambient air velocity values ​​of each monitoring point in real time at the same acquisition frequency as the electrical parameters and operating status parameters. The average value of the same data from the three monitoring points at each moment is taken as the environmental data corresponding to that moment. After being arranged in chronological order, the first raw environmental dataset of the working environment is formed.

[0024] To achieve time-series alignment of the data, all sensors and data acquisition modules are connected to the same high-precision clock module. This clock module generates continuous timestamps according to a unified time base, with each timestamp accurate to the millisecond level. Each data entry in the first electrical dataset, the first state dataset, and the first environmental raw dataset is accompanied by a unique timestamp generated by this clock module. The three datasets are sorted according to the chronological order of the timestamps. Then, using the timestamp as an index, each data entry corresponding to each timestamp is matched one by one. For a missing data entry of a certain type under a certain timestamp, the data corresponding to the same type of data at the two adjacent valid timestamps before and after that timestamp is found. The difference between these two valid data is calculated. According to the time interval ratio between the missing timestamp and the valid timestamps before and after, the numerical increment corresponding to the difference is allocated. The previous valid data is added to the increment to obtain the supplementary value of the missing data. This ensures that each timestamp contains complete data on three-phase stator current, stator voltage, speed, winding temperature, bearing vibration intensity, air temperature, relative humidity, and ambient air velocity. All aligned timestamps and corresponding data are integrated in sequence to obtain the original data set of the motor.

[0025] When performing dimensional normalization preprocessing on each data item in the original dataset, the type of each data item is first sorted out, and the dimensional attributes of the three-phase stator current, stator voltage, speed, winding temperature, bearing vibration intensity, air temperature, air relative humidity, and ambient air velocity are clarified. For each data item, all records of that data in the original dataset are traversed, and the maximum and minimum values ​​are selected. Then, for the value of that data at each moment, the minimum value of that data is subtracted from the value at that moment to obtain the difference between the value and the minimum value. This difference is then divided by the difference between the maximum and minimum values ​​of that data to obtain the normalized result of that data at that moment. Following the same operation process, the dimensional normalization processing of the values ​​of all eight data items at each moment in the original dataset is completed in sequence to ensure that the normalization results of all data are within the same numerical range, and finally, the real-time data of the motor and the multidimensional physical quantities of the environment are obtained.

[0026] The beneficial effects include the comprehensive acquisition of electrical parameters such as three-phase stator current, stator voltage, and speed of the motor, as well as operating status parameters such as winding temperature and bearing vibration intensity, and environmental physical quantities such as air temperature, relative humidity, and ambient air velocity in the working environment, through the reasonable arrangement and reliable connection of multiple types of high-precision sensors. This ensures the authenticity and completeness of the acquired data. Data time-series alignment is achieved using timestamps generated by a unified high-precision clock module. By reasonably supplementing missing data, consistency and continuity of different types of data in the time dimension are ensured, forming a complete original dataset. Standardized dimensional normalization eliminates the influence of dimensional differences in various data types, ensuring all data are within the same numerical range. This provides standardized, high-quality data support for subsequent applications such as motor operating status analysis and fault diagnosis, effectively improving the reliability and effectiveness of data use.

[0027] S2: Determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load; In this embodiment of the invention, determining the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load includes: From the real-time data, extract load characteristic parameters representing the motor load level; Establish a sliding time window correlation dataset between the environmental multidimensional physical quantities and the load characteristic parameters; On the associated dataset within the sliding time window, the causal contribution between environmental parameters and load characteristic parameters is evaluated to obtain the real-time influence coefficient of each dimension of environmental parameter on the current motor load.

[0028] After evaluating the causal contribution between environmental parameters and load characteristic parameters on the associated dataset within the sliding time window to obtain the real-time influence coefficient of each environmental parameter on the current motor load, the method further includes: The influence intensity is dynamically corrected based on the instantaneous values ​​and historical trends of the environmental multidimensional physical quantities; The influence coefficients after dynamic correction are fused; Based on feedback from historical synthesis effects, the coefficients after fusion are adjusted online to obtain the environmental impact factor of motor load.

[0029] The formulas for calculating the environmental impact factors are as follows: ; In the formula, For the first Environmental impact factors within a time window For the first Real-time impact coefficients of each dimension The real-time impact coefficient is obtained based on experience. For time window The first one obtained inside Real-time dynamic correlation coefficient between environmental parameters and motor load The change sensitivity weighting coefficient is obtained based on experience. For the first Historical trends of environmental parameters For the first The measured values ​​of each environmental parameter, For the first The mean and standard deviation of an environmental parameter in long-term historical data. For the first The standard deviation of an environmental parameter in long-term historical data.

[0030] The real-time impact coefficient is derived from empirical summaries, with specific values ​​determined directly through past practical experience. The real-time dynamic correlation coefficient is derived from the [missing information - likely a specific factor or source]. Data analysis within a time window to obtain the data within that time window. The calculation process involves all environmental parameter data and corresponding motor load data. First, calculate the mean of each of the two sets of data. Then, calculate the difference between each data point in each set and its corresponding mean. The covariance is obtained by multiplying the differences in environmental parameters with the differences in motor load, summing the results, and then calculating the sum of the squares of the differences between the two sets of data. The standard deviation is obtained by dividing the sum of the squares of the differences between the two sets of data by the number of data points. Finally, the time window is obtained by dividing the covariance by the product of the two standard deviations. Inner The real-time dynamic correlation coefficient between environmental parameters and motor load.

[0031] The sensitivity-to-change weighting coefficient is derived from empirical summaries, with specific values ​​determined directly through past practical experience. Historical change trends are derived from... Historical data for an environmental parameter are collected, including long-term historical data. These data are then arranged chronologically, and the difference between data points at adjacent time points is calculated. The variation patterns of these differences are analyzed to obtain the... The historical trend of environmental parameter changes. Measurements are derived from direct measurements, obtained through corresponding measuring equipment. The environmental parameters are monitored in real time, and the resulting values ​​are the measured values. The mean value is derived from the first... The long-term historical data of an environmental parameter are calculated as follows: All long-term historical data for that environmental parameter are collected, all data are summed, and the sum is divided by the number of historical data points to obtain the result. The mean of an environmental parameter in long-term historical data.

[0032] Standard deviation comes from the first The long-term historical data of an environmental parameter are used for calculation. The process is as follows: First, obtain the long-term historical data and corresponding mean of the environmental parameter. Calculate the difference between each historical data point and the mean. Square each difference and sum the results. Divide the sum by the number of historical data points to obtain the variance. Then, take the square root of the variance to obtain the variance of the first historical data point. The standard deviation of each environmental parameter in long-term historical data is calculated first. The sum of two parts for each dimension is calculated. The first part is the product of the absolute values ​​of the real-time impact coefficient and the real-time dynamic correlation coefficient. The second part is the product of the change sensitivity weight coefficient and the quotient of the absolute value of the historical change trend divided by the maximum value of the absolute value of the historical change trend. The two parts are then added together.

[0033] Then calculate the product of the above sum and the standardized measurement value. The standardization process is to use the first... The measured value of each environmental parameter is subtracted from its mean in long-term historical data, and then the difference is divided by its standard deviation in long-term historical data. The sum of these products over all dimensions is then taken to obtain the result. Environmental impact factors within a given time window. These environmental impact factors comprehensively reflect the environmental impact factors within the given time window. The combined effect of the dynamic correlation between environmental parameters and motor load within a given time window, the historical trend of environmental parameters, and the deviation of environmental parameter measurements from long-term historical data on environmental impact, directly reflects the overall environmental impact. The degree of influence of the environment on motor operation within a time window.

[0034] Electrical parameters directly related to the load level are extracted from real-time motor data, including all time-series values ​​corresponding to three-phase stator current, stator voltage, and speed. For three-phase stator current, the three current values ​​corresponding to each time stamp are iterated, and the average fluctuation range of the three current values ​​within each cycle is calculated. This fluctuation range directly reflects the change in motor output power and is used as a current-related load feature. For stator voltage and speed, the synergy between the changes in voltage and speed values ​​at each time stamp is analyzed simultaneously. When the voltage is stable, the fluctuation amplitude of speed is directly related to the stable state of the load and is used as a speed-related load feature. At the same time, the consistency of the changing trends of voltage and current within the same time interval is calculated. The degree of consistency reflects the matching relationship between the load and the power input and is used as a voltage-related load feature. All extracted current-related, speed-related, and voltage-related features are associated with their corresponding time stamps. The quantitative results of all features are integrated in chronological order to form load feature parameters representing the motor load level.

[0035] A fixed-duration sliding time window is set, with the window duration determined based on the motor load's response cycle to environmental changes. This ensures complete coverage of the entire process by which environmental parameter changes affect the load. The sliding step size is consistent with the real-time data acquisition frequency; that is, for each new timestamp of real-time data, the window moves forward by one acquisition interval. For each sliding window, all quantized results of load characteristic parameters corresponding to all timestamps within the window are extracted. Simultaneously, all values ​​of environmental multidimensional physical quantities corresponding to all timestamps within the same window are extracted. The load characteristic parameter data and environmental multidimensional physical quantity data corresponding to the same timestamp within the same window are bound one-to-one. Each window forms a dataset containing all binding relationships within that time period. According to the sliding order of the windows, all datasets corresponding to the windows are arranged sequentially to form a sliding time window association dataset between environmental multidimensional physical quantities and load characteristic parameters.

[0036] For each window in the sliding time window correlation dataset, the correlation between changes in environmental parameters and load characteristic parameters in each dimension is analyzed one by one. Taking air temperature as an example, the air temperature values ​​and load characteristic parameter values ​​corresponding to all timestamps within the window are traversed. When the air temperature value shows an upward trend, the direction of change in the load characteristic parameter value is recorded. If the load characteristic parameter value also increases, the proportional relationship between the two increases is calculated. If the load characteristic parameter value shows a downward trend, the inverse proportional relationship between the two changes is calculated. If the load characteristic parameter value remains stable, the result that the air temperature change has no significant impact on it is recorded. Following the same analysis method, the correlation analysis of changes in relative humidity, ambient air velocity, and load characteristic parameters is completed, and the response degree of the load characteristic parameter when the environmental parameter in each dimension changes is calculated. Combining the analysis results of multiple consecutive windows, the consistency of the response degree of each environmental parameter in each dimension is checked, and the deviation results caused by abnormal data are eliminated. The checked response degree is converted into a quantifiable value, which directly reflects the influence of the corresponding environmental parameter in each dimension on the current motor load, that is, the real-time influence coefficient of each environmental parameter in each dimension on the current motor load.

[0037] When dynamically correcting the real-time influence coefficient based on the instantaneous values ​​and historical trends of multidimensional environmental physical quantities, the following steps are first taken: First, by reviewing the environmental parameter records under normal motor operating conditions, the stable fluctuation ranges of air temperature, relative humidity, and ambient air velocity are determined. The median value of this range is set as the baseline instantaneous value for the corresponding environmental parameter. Then, for the instantaneous values ​​of the current multidimensional environmental physical quantities, their deviations from the corresponding baseline instantaneous values ​​are compared. If the instantaneous value of a certain environmental parameter exceeds the baseline range, and the larger the deviation, the more significant the immediate impact of that parameter on the motor load, and the correction magnitude of the real-time influence coefficient for that dimension needs to be increased accordingly. If the instantaneous value fluctuates within the baseline range, a smaller correction is applied. Simultaneously, the values ​​of environmental parameters in each dimension are extracted from multiple consecutive sliding time windows in the past. These values ​​are then arranged chronologically to observe their change trajectories, determining whether the historical trend is a continuous increase, a continuous decrease, or a fluctuating pattern. If a certain environmental parameter shows a continuous increase or decrease, and this trend has persisted for multiple windows, it indicates a cumulative effect on the load, requiring further strengthening of the real-time impact coefficient correction on top of the instantaneous value correction. If the trend is a fluctuating pattern, the correction magnitude corresponding to the instantaneous value remains unchanged. By combining the results of instantaneous value deviation correction and historical trend cumulative correction, the real-time impact coefficient of each environmental parameter dimension is adjusted, ultimately yielding the dynamically corrected impact coefficient.

[0038] When fusing the dynamically corrected influence coefficients, the historical data of long-term motor operation is first analyzed to statistically determine the frequency and magnitude of significant changes in motor load caused by air temperature, relative humidity, and ambient air velocity. A higher frequency and magnitude of these changes indicate a greater importance of the environmental parameter to the motor load. Based on the importance ranking, a fixed weight is assigned to the dynamically corrected influence coefficient of each dimension's environmental parameter; parameters with higher importance have a larger weight, and the sum of all parameter weights is a uniform fixed value. Then, the dynamically corrected influence coefficient of each dimension is multiplied by its corresponding weight to obtain a weighted result for each dimension. Finally, the weighted results of all dimensions are summed. The summed value is the comprehensive quantitative result integrating the influence of environmental parameters from each dimension; this result is the fusion coefficient obtained after fusing the dynamically corrected influence coefficients.

[0039] When adjusting the fused coefficients online based on historical synthesis effect feedback, the output results of each fused coefficient applied to motor load analysis are first recorded. Simultaneously, actual load change data of the motor during the same period is collected, and the output results are compared with the actual load change data to calculate the degree of deviation. If the deviation is within a preset reasonable range, it indicates that the current fused coefficients accurately reflect the comprehensive impact of environmental parameters on the load, and no adjustment is needed. If the deviation exceeds a reasonable range, the cause of the deviation is analyzed. If the deviation stems from an unreasonable weight allocation of a certain dimension of environmental parameters, the weight ratio of that dimension needs to be adjusted according to the direction of the deviation. If the deviation stems from an improper setting of the dynamic correction amplitude, the intensity of the instantaneous value deviation correction or historical trend correction for that dimension is adjusted accordingly. During the online adjustment process, the application effect of the fused coefficients and the actual load change data of the motor are collected in real time. Deviation comparison and cause analysis are continuously performed, and the weight allocation and correction logic are continuously optimized to ensure that the fused coefficients consistently match the actual operating state of the motor. After multiple cyclic adjustments, when the deviation stabilizes within a reasonable range, the fused coefficients become the environmental impact factor of the motor load.

[0040] The beneficial effects include the accurate extraction of feature parameters directly related to motor load levels, ensuring that load characteristics truly reflect the motor load state and laying a reliable foundation for subsequent correlation analysis. By reasonably setting the sliding time window and sliding step size, precise binding of multidimensional environmental physical quantities and load feature parameters in the time dimension is achieved. The resulting correlation dataset fully covers the entire process of environmental changes affecting the load, ensuring the effectiveness and comprehensiveness of data correlation. Based on the correlation dataset, correlation analysis of changes in multidimensional environmental parameters and load characteristics is conducted, and consistency verification is performed using continuous window results to effectively eliminate abnormal deviations. This accurately obtains the real-time impact coefficients of each dimension of environmental parameters on the current motor load, providing an accurate basis for subsequently determining the environmental impact factors of the motor load and improving the scientificity and reliability of motor load analysis and environmental impact assessment.

[0041] The beneficial effects include dynamically correcting the real-time impact coefficient by combining the instantaneous deviations of multidimensional environmental physical quantities with historical trends, effectively balancing immediate and cumulative effects and improving the adaptability of the impact coefficient to environmental changes. Coefficient fusion is performed by assigning weights based on the importance of environmental parameters' impact on motor load, achieving a reasonable integration of environmental impacts across various dimensions and ensuring the scientific validity of the comprehensive quantitative results. Continuous online adjustment of the fusion coefficients through historical synthesis effect feedback, and constant optimization of weight allocation and correction logic, ensures that the final motor load environmental impact factor accurately reflects the actual operating state of the motor, significantly improving the accuracy and reliability of environmental impact assessment and providing a high-quality basis for subsequent motor load-related analyses.

[0042] S3: Perform a fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor; In this embodiment of the invention, the step of fusing and analyzing the environmental impact factors and the preset energy efficiency optimization target to generate a preliminary control strategy for the motor includes: Based on the historical operating data and energy consumption records of the motor, a benchmark energy efficiency curve of the motor under different load ranges is established. The preset energy efficiency optimization target is analyzed as the optimization direction and constraint boundary for the benchmark energy efficiency curve; The environmental impact factors are used as dynamic correction variables and mapped onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factors. Based on the deformation characteristics, the optimization direction, and the constraint boundary, search the set of optimal operating points that satisfy the current conditions in the control parameter space; The optimal operating point set is converted into a corresponding control command sequence to obtain the initial control strategy of the motor.

[0043] The step of mapping the environmental impact factors as dynamic correction variables onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factors includes: Extract the key feature parameters of the benchmark energy efficiency curve; Establish the nonlinear influence relationship between the environmental impact factors and each of the key characteristic parameters; Based on the nonlinear influence relationship and the specific values ​​of the current environmental impact factors, the key characteristic parameters are adjusted in real time to obtain a dynamically adjusted energy efficiency curve. The shape change of the energy efficiency curve is output as a deformation feature.

[0044] All operational and energy consumption records from the motor's long-term operation were reviewed. First, invalid data resulting from equipment failures, sudden shutdowns, or other abnormal conditions were removed, retaining only valid data from normal operating conditions. This valid data includes load values, corresponding energy consumption values, and operating time information at each moment. The valid data was divided into several continuous and non-overlapping load intervals based on load values, ensuring each interval contains a sufficient amount of valid data to guarantee the reliability of the statistical results. For each load interval, the total energy consumption and total operating time of all operating periods within that interval were calculated. The average energy efficiency value for that interval was obtained by dividing the total energy consumption by the total operating time. The median value of each load interval was used as the x-axis, and the corresponding average energy efficiency value as the y-axis. All coordinate points corresponding to each interval were marked on the coordinate system. A smooth curve was used to connect these coordinate points sequentially according to the load intervals, forming a baseline energy efficiency curve for the motor under different load intervals. This curve visually reflects the correspondence between motor load and energy efficiency when there are no environmental influences.

[0045] Clearly define the preset energy efficiency optimization goals, which are determined based on the application scenario requirements and energy-saving requirements of the motor. These goals may include reducing energy consumption per unit load and improving the energy efficiency level in specific load ranges. Break down these goals into specific actionable directions, i.e., determine the trend that the baseline energy efficiency curve needs to be adjusted. For example, if the goal is to reduce energy consumption per unit load, the optimization direction is to improve the energy efficiency values ​​of the baseline energy efficiency curve in all load ranges; if the goal is to improve the energy efficiency in a specific load range, the optimization direction is to focus on improving the energy efficiency values ​​in that range. Simultaneously, combine the motor's rated operating parameters, equipment safety operating standards, and actual application conditions to determine constraint boundaries. These constraint boundaries include the maximum load limit, minimum load limit, allowable voltage fluctuation range, and frequency adjustment range of the motor. These constraint boundaries set insurmountable limits for subsequent operating point searches, ensuring the safety and feasibility of the control strategy.

[0046] The quantification results of the influence of environmental parameters of each dimension corresponding to the environmental impact factor are used as dynamic correction variables and applied to the baseline energy efficiency curve. For each load interval on the baseline energy efficiency curve, the influence of each dimension parameter of the environmental impact factor on the energy efficiency of that interval is analyzed. For example, the impact factor corresponding to increased air temperature will lead to a decrease in motor heat dissipation efficiency, thus making the actual energy efficiency of that load interval lower than the baseline energy efficiency. In this case, the energy efficiency value of the corresponding load interval on the baseline energy efficiency curve is adjusted down according to the magnitude of the impact factor. If the impact factor corresponding to increased relative humidity increases motor insulation loss, the energy efficiency value of the corresponding interval is also adjusted down according to the impact factor value. If the impact factor corresponding to increased ambient air velocity can improve heat dissipation, the energy efficiency value of the corresponding interval is increased. After performing the above correction operations on all load intervals on the baseline energy efficiency curve, the differences between the corrected curve and the original baseline energy efficiency curve are observed, including the overall rising and falling trend of the curve, the change range of energy efficiency values ​​in each interval, and the shift of the peak position of the curve. These differences are the deformation characteristics of the baseline energy efficiency curve.

[0047] The core parameters included in the control parameter space are clearly defined. These parameters include stator voltage regulation parameters, operating frequency regulation parameters, and speed control parameters of the motor. All parameters take values ​​within the preset constraint boundary range. Based on the deformation characteristics of the benchmark energy efficiency curve, the core search direction is determined, that is, prioritizing the search for parameter combinations that can make the energy efficiency conform to the optimization direction. For example, if the deformation characteristics show that the energy efficiency decreases in a certain load range, then the search direction is to find parameter combinations that can improve the energy efficiency in that range. In the control parameter space, each possible control parameter combination is selected at fixed intervals. This combination is substituted into the motor operation simulation scenario to simulate the energy efficiency performance of the motor under the influence of current environmental factors under this parameter combination. It is then determined whether the energy efficiency performance conforms to the optimization direction, and the load, voltage, frequency, etc., corresponding to this parameter combination are checked to see if they are within the constraint boundary. The above simulation and verification are performed on all selected control parameter combinations. All control parameter combinations that both conform to the optimization direction and satisfy the constraint boundary are selected. Each combination corresponds to a motor operating point, and these operating points are integrated to form the optimal operating point set.

[0048] The specific values ​​of control parameters corresponding to each operating point in the optimal operating point set are extracted, including the specific adjustment values ​​of stator voltage, the specific setting values ​​of operating frequency, and the specific control values ​​of speed. Based on the motor's operating logic and the controller's working principle, the control parameter values ​​of each operating point are converted into electrical signal commands that the controller can recognize and execute. For example, the voltage adjustment value is mapped to the pulse width modulation signal parameter output by the controller, and the frequency setting value is mapped to the oscillation frequency control signal parameter of the controller. According to the temporal pattern of load changes during motor operation, the control commands corresponding to all operating points are arranged in chronological order to ensure smooth transitions between adjacent commands and avoid parameter abrupt changes that could lead to motor instability. Simultaneously, the execution duration of each command is specified in the command sequence, which is determined based on the load duration of the corresponding operating point. This ultimately forms a continuous and directly executable sequence of control commands, which constitutes the initial control strategy for the motor.

[0049] Observe the overall shape of the benchmark energy efficiency curve and locate the coordinate point with the highest energy efficiency value in the curve. This point is the peak point. Record the load value and energy efficiency value corresponding to the peak point. Analyze the curve segment by segment from the minimum load end to the maximum load end to identify the locations where the slope of the curve changes significantly. These locations are the inflection points. Record the load value and energy efficiency value corresponding to each inflection point. Divide the curve into several continuous line segments according to the inflection points. By comparing the changes in energy efficiency value and load value at the two ends of each line segment, determine the degree of inclination of each line segment, which is the slope of each interval. At the same time, record the minimum load, maximum load and their corresponding energy efficiency values ​​at both ends of the curve, which are respectively used as the starting point feature and ending point feature of the curve. Integrate the above peak point, inflection point, slope of each interval, starting point feature and ending point feature to form the key feature parameters of the benchmark energy efficiency curve.

[0050] Data on the changes in key characteristic parameters corresponding to different environmental impact factor values ​​during the historical operation of the motor were collected, ensuring that the data covered the entire range of environmental impact factor changes and the common load range of the motor. For each key characteristic parameter, the corresponding characteristic parameter values ​​were arranged in order of magnitude of the environmental impact factor values, and the changing patterns of the two were observed. If, as the environmental impact factor value increased, the corresponding characteristic parameter value changed slowly first and then rapidly, or first rose and then fell, exhibiting a non-linear trend, then several intervals were divided based on this trend. The correspondence between the changes in environmental impact factor values ​​and characteristic parameter values ​​within each interval was clarified. For example, within a certain interval, for every certain increase in the environmental impact factor, the characteristic parameter value decreased by a certain amount. The correspondence between the changes in each key characteristic parameter and the environmental impact factor was analyzed one by one in the same way, ultimately forming a non-linear influence relationship between the environmental impact factor and each key characteristic parameter.

[0051] Obtain the specific value of the environmental impact factor for the motor load at the current moment. For each key characteristic parameter, determine the relationship interval in which the specific value falls by referring to the established nonlinear influence relationship. Based on the clear correspondence within the interval, calculate the amount of change that the key characteristic parameter should undergo under the environmental impact factor value. For example, if the current environmental impact factor value is in a certain interval, and the peak point energy efficiency value corresponding to that interval decreases as the environmental impact factor increases, then calculate the decrease in peak point energy efficiency corresponding to the current value according to the proportion of the change within the interval. This decrease is the instantaneous correction amount for the key characteristic parameter of peak point energy efficiency. Calculate the instantaneous correction amounts for all key characteristic parameters in sequence, and add or cancel the corresponding instantaneous correction amounts to the original key characteristic parameter values ​​to obtain the adjusted key characteristic parameters. Following the line segment connection logic of the baseline energy efficiency curve, connect the coordinate points corresponding to all adjusted key characteristic parameters sequentially with a smooth curve to form a new curve, which is the dynamically adjusted energy efficiency curve.

[0052] The dynamically adjusted energy efficiency curve is compared segment by segment with the original baseline energy efficiency curve. The changes at peak points are analyzed, including whether the corresponding energy efficiency value increases or decreases, and whether the corresponding load position shifts left or right. Changes at inflection points are statistically analyzed, including whether the number of inflection points increases or decreases, whether the load and energy efficiency values ​​corresponding to each inflection point change, and whether the spacing between inflection points changes. The differences in slope between intervals are observed to determine whether each curve segment is steeper or gentler than the original curve, and the degree of steepness or gentleness. The overall trend of the curve is examined to determine whether the curve has shifted upwards or downwards as a whole, or whether only local intervals have changed shape. Simultaneously, the shift of load and energy efficiency values ​​at the start and end points of the curve is recorded, along with the direction of the shift. These specific changes obtained from the comparison are systematically summarized to form a complete description of the shape changes of the adjusted energy efficiency curve; this description is the deformation characteristic.

[0053] The beneficial effects include establishing a benchmark energy efficiency curve based on historical effective operating data and energy consumption records of the motor, providing a reliable reference for energy efficiency optimization. By analyzing the energy efficiency optimization objectives, the optimization direction and constraint boundaries are clearly defined, ensuring that the optimization process is both targeted and safe. Environmental impact factors are mapped onto the benchmark energy efficiency curve as dynamic correction variables, making the curve deformation characteristics conform to actual environmental impacts and improving the accuracy of energy efficiency analysis. The optimal operating point set is accurately searched within the constraint boundaries, ensuring that the selected parameter combination meets optimization requirements and operational safety. The optimal operating point set is converted into a continuous and smooth control command sequence, and the resulting preliminary control strategy can be directly executed, effectively connecting the energy efficiency optimization objectives with the actual motor control, providing a scientific and feasible control basis for improving motor energy efficiency.

[0054] The core key characteristic parameters of the benchmark energy efficiency curve are accurately extracted, covering core elements such as peak points, inflection points, and interval slopes. This provides clear and comprehensive adjustment targets for subsequent dynamic corrections, ensuring a highly targeted correction process. By analyzing the changing patterns of environmental impact factors and key characteristic parameters in historical data, a nonlinear impact relationship that fits actual operating conditions is established, ensuring accurate and reliable quantitative transmission of environmental impacts. Based on the specific values ​​of current environmental impact factors and nonlinear impact relationships, the real-time correction amounts for each key characteristic parameter are accurately calculated. Through parameter adjustment and curve reconstruction, a dynamically adapted energy efficiency curve is formed, enabling the curve to reflect the impact of environmental changes on motor energy efficiency in real time. By comparing segment by segment with the original benchmark energy efficiency curve, the system analyzes various changes in the curve shape. The resulting deformation characteristics clearly present the specific changes in the energy efficiency curve under environmental influences, providing a precise and intuitive basis for subsequent search for the optimal operating point by combining optimization objectives and constraint boundaries, significantly improving the scientific nature and adaptability of motor energy efficiency control.

[0055] S4: Dynamically adjust the key parameters in the preliminary control strategy based on the real-time data to obtain the optimal control parameter combination for the motor; In this embodiment of the invention, the step of dynamically adjusting the key parameters in the preliminary control strategy based on the real-time data to obtain the optimized control parameter combination for the motor includes: Based on the motor operating status parameters in the real-time data, the actual response of the motor during the execution of the initial control strategy is continuously monitored. The actual response is compared with the target response of the preliminary control strategy to determine the response deviation of the motor; Based on the direction and magnitude of the response deviation, determine the adjustment direction and magnitude of the key parameters in the preliminary control strategy; According to the adjustment direction and adjustment range, the key parameters are fine-tuned in real time; By collecting key parameters that have been fine-tuned in real time, the optimal combination of control parameters for the motor is obtained.

[0056] Based on real-time data such as motor winding temperature and bearing vibration intensity, the system continuously collects real-time status signals of the motor when executing the initial control strategy through temperature sensors embedded in the windings and vibration sensors fixed to the bearing end caps. After the sensors convert the physical state into electrical signals, they are transmitted to the monitoring module in real time through shielded transmission lines. The monitoring module receives the signals at a rhythm consistent with the data acquisition frequency and parses them into specific values. At the same time, it records the timestamp corresponding to each value, continuously tracking whether the winding temperature is stable within the target range and whether the bearing vibration intensity meets the preset standard. It fully captures the actual operating performance of the motor during the control process, such as speed changes and energy consumption fluctuations, forming an actual response record of the motor during the execution of the initial control strategy.

[0057] The target response of the initial control strategy is clearly defined. This target response is the ideal operating state of the motor set based on the energy efficiency optimization target. It includes preset ideal values ​​such as winding temperature, bearing vibration intensity, speed, and energy consumption at each time point. The real-time values ​​at each time point in the actual response are compared one by one with the ideal values ​​at the corresponding time points in the target response. If the winding temperature value of the actual response is higher than the ideal value of the target response, the deviation direction is determined to be positive; if the bearing vibration intensity value of the actual response is lower than the ideal value of the target response, the deviation direction is determined to be negative. The magnitude of the deviation is determined by calculating the difference between the actual value and the ideal value. The larger the difference, the more significant the deviation. The system sorts out the deviation direction and magnitude of all corresponding time points to form a complete record of motor response deviation.

[0058] For the obtained response deviations, analyze the correlation between the deviation direction and key parameters. If the response deviation is positive, the winding temperature is too high, indicating that the current settings of key parameters such as stator voltage and operating frequency in the initial control strategy are causing excessive motor heating. The adjustment direction is to reduce the stator voltage or operating frequency. If the response deviation is negative, the speed is too low, indicating that the current key parameter settings cannot meet the target speed requirements. The adjustment direction is to increase the stator voltage or operating frequency. The adjustment range is determined based on the magnitude of the deviation; the larger the deviation, the larger the adjustment range. For example, when the deviation for high winding temperature is large, the adjustment range of the stator voltage will increase accordingly. Simultaneously, refer to the motor's rated operating parameters and safety constraints to ensure that the adjustment range does not exceed the equipment's allowable operating range, avoiding abnormal motor operation due to over-adjustment. Finally, clarify the specific adjustment direction and corresponding adjustment range for each key parameter.

[0059] According to the determined adjustment direction and magnitude, the motor controller performs real-time fine-tuning of key parameters in the initial control strategy. If the adjustment direction is to reduce the stator voltage, the controller adjusts the output pulse width modulation signal to reduce the effective conduction time of the signal, thereby reducing the input voltage of the stator winding. If the adjustment direction is to increase the operating frequency, the controller changes the frequency of the internal oscillation circuit, causing the output drive signal frequency to increase synchronously, thus adjusting the motor's operating frequency. During the fine-tuning process, the controller receives status signals from sensors in real time to ensure precise execution of the adjustment actions. The adjustment of each key parameter strictly follows the preset adjustment magnitude, without any sudden changes exceeding the range, ensuring the stability of motor operation.

[0060] After completing the immediate fine-tuning of all key parameters, the specific values ​​of each fine-tuned key parameter are collected through the data recording module, including the adjusted stator voltage, operating frequency, speed control value, etc. The value of each parameter is checked to see if it is consistent with the adjustment direction and magnitude. After confirming that there is no adjustment deviation, all fine-tuned key parameters are classified and integrated according to the corresponding relationship of motor operating conditions to ensure that the key parameter combination under each operating condition is complete and accurate. The integrated parameter set is sorted according to time sequence or operating condition type to form a complete parameter set that can be directly applied to motor operation control. This set is the optimal control parameter combination for the motor.

[0061] The beneficial effects include continuously collecting motor operating status parameters and capturing the actual response during the execution of the initial control strategy in real time, ensuring a comprehensive understanding of the motor's true operating status and providing accurate data support for subsequent adjustments. By comparing the actual response with the target response one by one, the direction and magnitude of the response deviation are clearly identified, making parameter adjustments intuitive and sufficient. Based on the deviation characteristics, the adjustment direction and magnitude of key parameters are accurately determined, taking into account equipment safety constraints to ensure that the adjustment actions are targeted and reasonable. Key parameters are fine-tuned in real time according to the predetermined direction and magnitude, with real-time feedback ensuring accurate execution of adjustments and avoiding sudden parameter changes that could affect stable motor operation. The system collects and integrates the fine-tuned key parameters for verification, resulting in a complete and reliable optimized control parameter combination. This effectively corrects deviations in the initial control strategy, making the motor operating status more aligned with energy efficiency optimization goals and significantly improving the accuracy and effectiveness of control.

[0062] S5: Adjust the motor in real time based on the optimized control parameter set.

[0063] In this embodiment of the invention, adjusting the motor in real time according to the optimized control parameter set includes: Each parameter in the optimized control parameter set is converted into a control command that the motor driver can recognize; The control commands are sent sequentially to the motor driver; The motor driver updates the key variables used to generate the drive signal internally according to the received control command, and generates an updated motor drive signal. The updated motor drive signal is used to drive the motor to change its operating state accordingly.

[0064] The optimized control parameter set includes key parameters such as stator voltage, operating frequency, and speed control values. For each parameter, a dedicated signal conversion module performs format conversion. This module has pre-set instruction encoding rules for the motor driver. The stator voltage parameter is converted into a corresponding pulse width modulation (PWM) signal instruction parameter according to the encoding rules. Specifically, the voltage magnitude is mapped by adjusting the effective conduction time ratio of the pulses in the instruction. The operating frequency parameter is converted into a frequency control instruction for the driver's oscillation circuit. The target operating frequency is determined by setting the frequency encoding value in the instruction. The speed control value is converted into a speed feedback adjustment instruction signal, clearly defining the target threshold and adjustment step size for the rotor speed. During the conversion process, the value of each parameter is adapted to the format through hardware level conversion and encoding mapping, ensuring that the converted control instruction completely matches the signal reception format of the motor driver, ultimately forming a control instruction that the motor driver can recognize for each parameter.

[0065] The converted control commands are arranged according to the motor's operating logic sequence, first the stator voltage control commands, then the operating frequency control commands, and finally the speed control commands, ensuring that the execution order of the commands conforms to the motor's starting and running conditions. The control commands are transmitted from the signal conversion module to the motor driver's signal receiving port via shielded transmission cables. The outer shielding layer of the cable effectively isolates external electromagnetic interference, ensuring the stability of command transmission. During transmission, each control command is accompanied by a unique checksum. The checksum is generated according to a fixed rule based on the numerical characteristics of the command itself. After receiving the command, the motor driver first verifies the checksum to confirm that the command has not been distorted or lost during transmission. If the verification is successful, it sends a reception confirmation signal back to the signal conversion module. If the verification fails, it sends a retransmission request. Upon receiving the retransmission request, the signal conversion module retransmits the command until the driver successfully receives and verifies it, ensuring that all control commands are accurately and completely sent to the motor driver.

[0066] The motor driver internally includes functional units such as a pulse generation module, a voltage output module, and a frequency adjustment module. Each unit stores key variables used to generate the drive signal, including pulse width variables, oscillation frequency variables, and speed threshold variables. After receiving control commands, the driver assigns them to the corresponding functional units according to the command type. Stator voltage control commands are transmitted to the voltage output module, which adjusts its internal pulse width variable to ensure the effective conduction time ratio of the pulses meets the target voltage requirements. Running frequency control commands are transmitted to the frequency adjustment module, which updates the oscillation frequency variable to set the circuit's oscillation frequency as the target running frequency. Speed ​​control commands are transmitted to the speed adjustment module, which updates the speed threshold variable to determine the target rotor speed and adjustment response speed. After all key variables are updated, the functional units work together: the pulse generation module generates a basic pulse signal based on the updated pulse width variable; the frequency adjustment module adjusts the frequency of the pulse signal according to the oscillation frequency variable; and the voltage output module amplifies the pulse signal amplitude, ultimately generating the updated motor drive signal.

[0067] The updated motor drive signal is transmitted to the stator winding terminals of the motor through the output terminals of the motor driver. The drive signal is a three-phase sinusoidal signal, and its voltage amplitude, frequency, and pulse ratio all meet the requirements of the control command. The three-phase drive signal forms a rotating magnetic field in the stator winding. The rotational speed of the rotating magnetic field is determined by the frequency of the drive signal, and the magnetic field strength is determined by the voltage amplitude of the drive signal. The rotor adjusts its speed under the electromagnetic torque of the rotating magnetic field. When the frequency of the drive signal increases, the rotational speed of the rotating magnetic field increases, and the rotor speed increases synchronously. When the voltage amplitude of the drive signal is adjusted, the electromagnetic torque changes accordingly, and the output power of the rotor changes accordingly. At the same time, the optimization of the drive signal parameters makes the current distribution of the stator winding more reasonable, reduces winding copper losses, lowers winding temperature, makes the load torque borne by the bearings more stable, and keeps the vibration intensity within the ideal range. Ultimately, the motor operating state changes accordingly according to the requirements of the optimized control parameters.

[0068] The beneficial effects are as follows: By adapting to the preset instruction encoding rules and hardware circuits, the parameters in the optimized control parameter set are accurately converted into control instructions that the motor driver can recognize, ensuring that the instruction format fully matches the driver's receiving requirements and laying a reliable foundation for subsequent drive control. Control instructions are arranged in an orderly manner according to the motor's operating logic. Shielded transmission cables ensure that the transmission process is free from electromagnetic interference. Combined with checksum verification and retransmission mechanisms, all control instructions are ensured to be sent to the motor driver completely and accurately, avoiding instruction transmission distortion or loss. After receiving the instructions, the motor driver updates the key variables of each functional unit accordingly. Each unit works collaboratively to generate updated drive signals that meet the control requirements, ensuring the accuracy and stability of the drive signal parameters. The updated drive signal forms a suitable rotating magnetic field through the stator windings, effectively adjusting the motor's speed, output power, and other operating states. Simultaneously, it optimizes the winding current distribution and bearing load torque, reducing energy consumption and operating losses, allowing the motor to operate stably in the optimized ideal state. This fully leverages the regulatory role of the optimized control parameters, improving the motor's energy efficiency and reliability.

[0069] like Figure 2 The diagram shown is a functional block diagram of a motor adaptive energy-saving control system that integrates environmental perception, provided in an embodiment of the present invention.

[0070] The adaptive energy-saving control system 100 for motors with integrated environmental perception, as described in this invention, can be installed in electronic devices. Depending on the functions implemented, the adaptive energy-saving control system 100 may include a multi-source data acquisition module 101, an environmental coupling analysis module 102, a fusion decision-making module 103, a parameter dynamic optimization module 104, and a real-time control execution module 105. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0071] In this embodiment, the functions of each module / unit are as follows: The multi-source data acquisition module 101 is used to acquire the real-time electrical parameters and operating status parameters of the motor to construct the real-time data of the motor, and to acquire the multi-dimensional physical quantities of the motor in the working environment. The environmental coupling analysis module 102 is used to determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load. The fusion decision module 103 is used to perform fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor. The parameter dynamic optimization module 104 is used to dynamically adjust the key parameters in the preliminary control strategy according to the real-time data to obtain the optimized control parameter combination of the motor. The real-time control execution module 105 is used to adjust the motor in real time according to the optimized control parameter set.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0076] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence 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 that knowledge to obtain optimal results.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A motor adaptive energy-saving control method integrating environmental perception, characterized in that, The method includes: S1: Collect real-time electrical parameters and operating status parameters of the motor to construct real-time data of the motor, and collect multi-dimensional physical quantities of the motor in the working environment. S2: Determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load; S3: Perform a fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor; S4: Dynamically adjust the key parameters in the preliminary control strategy based on the real-time data to obtain the optimal control parameter combination for the motor; S5: Adjust the motor in real time based on the optimized control parameter set.

2. The adaptive energy-saving control method for motors integrating environmental perception as described in claim 1, characterized in that, The system collects real-time electrical parameters and operating status parameters of the motor to construct real-time data for the motor, and collects multi-dimensional physical quantities of the motor's environment in the working environment, including: The three-phase stator current, stator voltage, and speed of the motor are collected to form the first electrical data set of the motor; The winding temperature and bearing vibration intensity of the motor are collected to form the first state dataset of the motor; The air temperature, relative humidity, and air velocity in the working environment of the motor are collected to form the first raw environmental dataset of the working environment; The first electrical dataset, the first state dataset, and the first environmental dataset are time-aligned based on a unified timestamp to obtain the original data set of the motor. The data in the original dataset are preprocessed with dimension normalization to obtain the real-time data of the motor and the multidimensional physical quantities of the environment.

3. The adaptive energy-saving control method for motors integrating environmental perception as described in claim 1, characterized in that, The determination of the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load includes: From the real-time data, extract load characteristic parameters representing the motor load level; Establish a sliding time window correlation dataset between the environmental multidimensional physical quantities and the load characteristic parameters; On the associated dataset within the sliding time window, the causal contribution between environmental parameters and load characteristic parameters is evaluated to obtain the real-time influence coefficient of each dimension of environmental parameter on the current motor load.

4. The motor adaptive energy-saving control method integrating environmental perception as described in claim 3, characterized in that, After evaluating the causal contribution between environmental parameters and load characteristic parameters on the associated dataset within the sliding time window to obtain the real-time influence coefficient of each environmental parameter on the current motor load, the method further includes: The influence intensity is dynamically corrected based on the instantaneous values ​​and historical trends of the environmental multidimensional physical quantities; The influence coefficients after dynamic correction are fused; Based on feedback from historical synthesis effects, the coefficients after fusion are adjusted online to obtain the environmental impact factor of motor load.

5. The motor adaptive energy-saving control method integrating environmental perception as described in claim 4, characterized in that, The formulas for calculating the environmental impact factors are as follows: ； In the formula, For the first Environmental impact factors within a time window For the first Real-time impact coefficients of each dimension The real-time impact coefficient is obtained based on experience. For time window The first one obtained inside Real-time dynamic correlation coefficient between environmental parameters and motor load The change sensitivity weighting coefficient is obtained based on experience. For the first Historical trends of environmental parameters For the first The measured values ​​of each environmental parameter, For the first The mean and standard deviation of an environmental parameter in long-term historical data. For the first The standard deviation of an environmental parameter in long-term historical data.

6. The motor adaptive energy-saving control method integrating environmental perception as described in claim 1, characterized in that, The process of fusing and analyzing the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor includes: Based on the historical operating data and energy consumption records of the motor, a benchmark energy efficiency curve of the motor under different load ranges is established. The preset energy efficiency optimization target is analyzed as the optimization direction and constraint boundary for the benchmark energy efficiency curve; The environmental impact factors are used as dynamic correction variables and mapped onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factors. Based on the deformation characteristics, the optimization direction, and the constraint boundary, search the set of optimal operating points that satisfy the current conditions in the control parameter space; The optimal operating point set is converted into a corresponding control command sequence to obtain the initial control strategy of the motor.

7. The motor adaptive energy-saving control method integrating environmental perception as described in claim 6, characterized in that, The step of mapping the environmental impact factors as dynamic correction variables onto the baseline energy efficiency curve to obtain the deformation characteristics of the baseline energy efficiency curve under the influence of the environmental impact factors includes: Extract the key feature parameters of the benchmark energy efficiency curve; Establish the nonlinear influence relationship between the environmental impact factors and each of the key characteristic parameters; Based on the nonlinear influence relationship and the specific values ​​of the current environmental impact factors, the key characteristic parameters are adjusted in real time to obtain a dynamically adjusted energy efficiency curve. The shape change of the energy efficiency curve is output as a deformation feature.

8. The motor adaptive energy-saving control method integrating environmental perception as described in claim 1, characterized in that, The step of dynamically adjusting key parameters in the initial control strategy based on the real-time data to obtain the optimized control parameter combination for the motor includes: Based on the motor operating status parameters in the real-time data, the actual response of the motor during the execution of the initial control strategy is continuously monitored. The actual response is compared with the target response of the preliminary control strategy to determine the response deviation of the motor; Based on the direction and magnitude of the response deviation, determine the adjustment direction and magnitude of the key parameters in the preliminary control strategy; According to the adjustment direction and adjustment range, the key parameters are fine-tuned in real time; By collecting key parameters that have been fine-tuned in real time, the optimal combination of control parameters for the motor is obtained.

9. The adaptive energy-saving control method for motors integrating environmental perception as described in claim 1, characterized in that, The step of adjusting the motor in real time based on the optimized control parameter set includes: Each parameter in the optimized control parameter set is converted into a control command that the motor driver can recognize; The control commands are sent sequentially to the motor driver; The motor driver updates the key variables used to generate the drive signal internally according to the received control command, and generates an updated motor drive signal. The updated motor drive signal is used to drive the motor to change its operating state accordingly.

10. A motor adaptive energy-saving control system integrating environmental perception, characterized in that, For implementing the motor adaptive energy-saving control method integrating environmental perception as described in claim 1, the system includes: The multi-source data acquisition module is used to collect the real-time electrical parameters and operating status parameters of the motor to construct the real-time data of the motor, and to collect the multi-dimensional physical quantities of the motor in the working environment. The environmental coupling analysis module is used to determine the environmental impact factor of the motor load based on the coupling relationship between the multidimensional physical quantities of the environment and the motor load. The fusion decision module is used to perform fusion analysis on the environmental impact factors and the preset energy efficiency optimization targets to generate a preliminary control strategy for the motor. The parameter dynamic optimization module is used to dynamically adjust the key parameters in the preliminary control strategy based on the real-time data to obtain the optimized control parameter combination of the motor. The real-time control execution module is used to adjust the motor in real time based on the optimized control parameter set.

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