A method for monitoring and optimizing the operating state of an electric motor group and related devices

Abstract

The application discloses a kind of operating state monitoring optimization method and related device of motor set, it is related to motor monitoring technical field, the method includes: heat flow impedance analysis is carried out based on operating parameter and environmental parameter;Based on heat flow impedance data, the operating state evaluation of motor set is carried out;The state data of key point in the task flow served by motor set is acquired, and the task scheduling intention and its credibility are determined based on state data and operating parameter;Based on task scheduling intention and credibility, load prediction is carried out in combination with operating state evaluation result;Based on operating state evaluation result and load prediction information, parameter optimization conflict is judged and margin factor analysis is carried out;Based on parameter optimization conflict determination result and target margin factor, target operating parameter range is determined;Based on target operating parameter range, operating parameter adjustment is carried out.The application realizes more comprehensive and accurate motor set monitoring, and realizes the self-adaptive adjustment to motor set operating parameter.

Description

Technical Field

[0001] This invention relates to the field of motor monitoring technology, and in particular to a method and related apparatus for monitoring and optimizing the operating status of motor units. Background Technology

[0002] In modern industrial production and energy systems, electric motors play a crucial role; their stable operation and high efficiency directly affect the safety and economic benefits of the entire system. However, current methods for monitoring the operating status of electric motors primarily focus on the real-time collection of basic operating data such as voltage, current, temperature, and vibration, and setting fixed thresholds for anomaly alarms. While this approach can alert when obvious problems arise, it is essentially a passive and delayed monitoring strategy. It often fails to deeply assess the gradual decline in equipment performance under certain operating conditions, nor can it accurately predict future load changes, thus failing to meet the higher demands of modern industry for maximizing energy efficiency and optimizing the equipment's life-cycle cost.

[0003] The shortcomings of existing technologies are mainly reflected in the following aspects. First, conventional monitoring methods usually analyze operating data in isolation, failing to fully consider the combined impact of environmental factors on equipment operating status, resulting in insufficient accuracy in status assessment. Simultaneously, parameter adjustments often rely on fixed rules or manual experience, leading to slow response times and an inability to make forward-looking, adaptive dynamic adjustments based on anticipated load changes and equipment status. This results in the motor unit not operating at its optimal working condition for extended periods, increasing unnecessary energy consumption and losses, and concealing operational risks. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and related device for monitoring and optimizing the operating status of electric motor sets, which realizes more comprehensive and accurate monitoring of electric motor sets and adaptive adjustment of the operating parameters of electric motor sets.

[0005] To address the aforementioned technical problems, this invention provides a method for monitoring and optimizing the operating status of an electric motor unit, the method comprising:

[0006] The operating parameters and environmental parameters of the motor unit are obtained, and thermal impedance analysis is performed based on the operating parameters and environmental parameters to obtain thermal impedance data;

[0007] Based on the thermal flow impedance data, the operating status of the motor unit is evaluated to obtain the operating status evaluation results.

[0008] Obtain the status data of key points in the task process served by the motor unit, and determine the task scheduling intention and the credibility corresponding to the task scheduling intention based on the status data and operating parameters;

[0009] Based on the task scheduling intent and credibility combined with the operating status evaluation results, load prediction of the motor group is performed to obtain load prediction information;

[0010] Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed to obtain parameter optimization conflict judgment results. Based on the operational status assessment results and load forecast information, margin factor analysis is performed to obtain the target margin factor.

[0011] The target operating parameter range of the motor unit is determined based on the parameter optimization conflict judgment results and the target margin factor.

[0012] The operating parameters of the motor unit are adjusted based on the target operating parameter range.

[0013] Optionally, the step of performing thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data includes:

[0014] Based on the operating parameters, the internal hot spot temperature and electrical parameters are extracted, and the total input power and mechanical output power are calculated based on the electrical parameters. The heat generation rate is then calculated based on the total input power and mechanical output power.

[0015] The ambient temperature is extracted based on the environmental parameters, and thermal impedance analysis is performed based on the internal hot spot temperature, ambient temperature, and heat generation rate to obtain thermal impedance data.

[0016] Optionally, the step of evaluating the operating status of the motor unit based on the thermal impedance data to obtain the operating status evaluation result includes:

[0017] Calculate the deviation between the operating parameters and the ideal operating parameters, determine the environmental impact coefficient based on the environmental parameters, and determine the initial operating pressure index based on the deviation and the environmental impact coefficient;

[0018] Based on the thermal flow impedance data, a thermal resistance correction term is determined, and the initial operating pressure index is adjusted based on the thermal resistance correction term to obtain the target operating pressure index.

[0019] Based on the heat flow impedance data, the internal overheating risk is quantified to obtain the internal overheating risk coefficient.

[0020] Based on the aforementioned operating parameters and environmental parameters, analyze the synergistic degradation factors;

[0021] The operating status of the motor unit is evaluated based on the target operating pressure index and the internal overheating risk coefficient, combined with the synergistic deterioration factor, and the operating status evaluation results are obtained.

[0022] Optionally, determining the task scheduling intent and the corresponding credibility of the task scheduling intent based on the status data and operating parameters includes:

[0023] The instantaneous features of the state data and operating parameters are extracted based on the instantaneous data window;

[0024] Calculate the matching degree between the instantaneous feature and each task mode feature, and take the task mode feature with the highest matching degree as the target task mode feature;

[0025] Based on the target task mode characteristics, the task scheduling intent is inferred to obtain the task scheduling intent of key points in the task process served by the electric motor unit.

[0026] The acquisition and processing delay of status data and operating parameters are obtained, and the credibility of the task scheduling intention is determined based on the matching degree between the acquisition and processing delay and the target task mode characteristics.

[0027] Optionally, the step of determining parameter optimization conflicts based on the operational status assessment results and load forecast information to obtain parameter optimization conflict determination results, and performing margin factor analysis based on the operational status assessment results and load forecast information to obtain target margin factors, includes:

[0028] The first parameter optimization objective of the operation status assessment result and the second parameter optimization objective of the load forecast information are determined, and parameter optimization conflict judgment is performed based on the first parameter optimization objective and the second parameter optimization objective to obtain the parameter optimization conflict judgment result;

[0029] Analyze the degree of fluctuation of environmental parameters, and determine the confidence level of the operational status assessment results based on the degree of fluctuation;

[0030] The prediction error interval width of the load forecast information is evaluated, and a margin factor analysis is performed based on the confidence level of the operation status evaluation results and the prediction error interval width of the load forecast information to obtain the target margin factor.

[0031] Optionally, determining the target operating parameter range of the motor unit based on the parameter optimization conflict judgment result and the target margin factor includes:

[0032] The determination result of the parameter optimization conflict is whether there is a conflict in the parameter optimization objectives;

[0033] If the parameter optimization conflict judgment result indicates that there is a conflict in the parameter optimization objectives, identify the time scale involved when the parameter optimization objectives conflict;

[0034] The initial operating parameter range is determined based on the operating status assessment results and load forecast information, and the initial operating parameter range is adjusted based on the time scale and target margin factor to obtain the target operating parameter range.

[0035] Optionally, adjusting the initial operating parameter range based on the time scale and the target margin factor to obtain the target operating parameter range includes:

[0036] Priority rules are set based on the aforementioned time scale;

[0037] The first parameter optimization objective and the second parameter optimization objective are balanced based on the priority rule to obtain the balanced first parameter optimization objective and the second parameter optimization objective.

[0038] The initial operating parameter range is adjusted based on the first parameter optimization objective and the second parameter optimization objective after balancing, combined with the target margin factor, to obtain the target operating parameter range.

[0039] In addition, the present invention also provides an operating status monitoring and optimization device for electric motor sets, the device comprising:

[0040] Thermal impedance analysis module: used to acquire the operating parameters and environmental parameters of the motor unit, and to perform thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data;

[0041] Operation status assessment module: used to assess the operation status of the motor unit based on the thermal impedance data and obtain the operation status assessment results;

[0042] Intent determination module: used to acquire the status data of key points in the task flow served by the motor unit, and determine the task scheduling intent and the credibility of the task scheduling intent based on the status data and operating parameters;

[0043] Load forecasting module: used to forecast the load of the motor group based on the task scheduling intention and credibility combined with the operation status evaluation results, and obtain load forecasting information;

[0044] Judgment and Analysis Module: Used to judge parameter optimization conflicts based on the operation status assessment results and load forecast information, obtain parameter optimization conflict judgment results, and perform margin factor analysis based on the operation status assessment results and load forecast information to obtain target margin factor;

[0045] Parameter range determination module: used to determine the target operating parameter range of the motor set based on the parameter optimization conflict judgment result and the target margin factor;

[0046] Parameter adjustment module: used to adjust the operating parameters of the motor set based on the target operating parameter range.

[0047] In addition, the present invention also provides an electronic device, which includes a processor and a memory. The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the above-described method for monitoring and optimizing the operating status of the motor group.

[0048] In addition, the present invention also provides a computer-readable storage medium that stores computer instructions, which, when executed on an electronic device, cause the electronic device to perform the above-described method for monitoring and optimizing the operating status of the electric motor assembly.

[0049] In this embodiment of the invention, thermal impedance analysis is performed based on the operating parameters and environmental parameters of the electric motor unit to obtain thermal impedance data. The operating status of the electric motor unit is then assessed based on this data, enabling a deeper understanding of its internal thermodynamic characteristics. This overcomes the shortcomings of traditional monitoring methods that focus only on surface data while ignoring deeper physical mechanisms. Furthermore, combining thermal impedance data with the operating status assessment provides a more accurate reflection of the actual health condition of the electric motor unit. The status data of key points in the task flow served by the electric motor unit is acquired. Based on this status data and operating parameters, the task scheduling intent and its corresponding credibility are determined. Finally, based on the task scheduling intent, credibility, and the operating status assessment results, load forecasting of the electric motor unit is performed. This makes the load forecasting forward-looking and targeted, solving the problem of insufficient evidence in existing load forecasting techniques, and allowing for a more comprehensive consideration of the impact of future load changes on the electric motor unit. Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed. Margin factor analysis is then conducted based on the operational status assessment results and load forecast information. Based on the parameter optimization conflict judgment results and the target margin factor, the target operating parameter range of the motor unit is determined. Based on the target operating parameter range, the operating parameters of the motor unit are adjusted, achieving adaptive adjustment of the motor unit's operating parameters. This effectively solves the problems of slow parameter adjustment response and inability to dynamically optimize based on predictive information in existing technologies. Consequently, energy consumption and losses are significantly reduced, equipment lifespan is extended, and overall operational safety and economic efficiency are improved. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating the method for monitoring and optimizing the operating status of an electric motor unit according to an embodiment of the present invention.

[0052] Figure 2 This is a flowchart illustrating the method for monitoring and optimizing the operating status of an electric motor unit according to another embodiment of the present invention.

[0053] Figure 3 This is a schematic diagram of the structural composition of the motor unit operation status monitoring and optimization device in an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] Please see Figure 1 , Figure 1 This is a flowchart illustrating the method for monitoring and optimizing the operating status of an electric motor unit according to an embodiment of the present invention. The method includes:

[0058] S11: Obtain the operating parameters and environmental parameters of the motor unit, and perform thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data;

[0059] In the specific implementation of this invention, the operating parameters and environmental parameters of the motor unit are obtained. Based on the operating parameters, the internal hot spot temperature and electrical parameters are extracted, and the total input power and mechanical output power are calculated based on the electrical parameters. The heat generation rate is calculated based on the total input power and mechanical output power. Based on the environmental parameters, the ambient temperature is extracted, and thermal impedance analysis is performed based on the internal hot spot temperature, ambient temperature, and heat generation rate to obtain thermal impedance data. This allows for a more accurate quantification of the heat generation and transfer process inside the motor unit, improving the reliability of the thermal impedance analysis.

[0060] S12: Based on the thermal flow impedance data, evaluate the operating status of the motor unit and obtain the operating status evaluation results;

[0061] In the specific implementation of this invention, the deviation between the operating parameters and the ideal operating parameters is calculated, the environmental impact coefficient is determined based on the environmental parameters, and the initial operating pressure index is determined based on the deviation and the environmental impact coefficient; the thermal resistance correction term is determined based on the thermal flow impedance data, and the initial operating pressure index is adjusted based on the thermal resistance correction term to obtain the target operating pressure index; the internal overheating risk is quantified based on the thermal flow impedance data to obtain the internal overheating risk coefficient; the synergistic degradation factor is analyzed based on the operating parameters and environmental parameters; and the operating status of the motor unit is evaluated based on the target operating pressure index and the internal overheating risk coefficient combined with the synergistic degradation factor. This comprehensive approach considers the influence of multiple factors on the operating status of the motor unit, thereby obtaining a more comprehensive and accurate operating status evaluation result.

[0062] S13: Obtain the status data of key points in the task process served by the motor unit, and determine the task scheduling intention and the credibility corresponding to the task scheduling intention based on the status data and operating parameters;

[0063] In the specific implementation of this invention, the status data of key points in the task flow served by the electric motor unit are acquired, and the instantaneous features of the status data and operating parameters are extracted based on the instantaneous data window; the matching degree between the instantaneous features and the features of each task mode is calculated, and the task mode feature with the highest matching degree is taken as the target task mode feature; the task scheduling intention is inferred based on the target task mode feature to obtain the task scheduling intention of key points in the task flow served by the electric motor unit; the acquisition and processing delay of the status data and operating parameters is acquired, and the credibility of the task scheduling intention is determined based on the matching degree between the acquisition and processing delay and the target task mode feature. It is possible to accurately infer the task scheduling intention of the electric motor unit through instantaneous feature extraction and pattern matching, and quantify the credibility of the task scheduling intention by combining the data acquisition and processing delay and the matching degree, so as to provide reliable input for subsequent load forecasting.

[0064] S14: Based on the task scheduling intent and credibility combined with the operating status evaluation results, perform load prediction of the motor group to obtain load prediction information;

[0065] In the specific implementation of this invention, the load margin coefficient is matched based on the credibility, and the load margin coefficient, task scheduling intention and operation status evaluation results are used to perform load prediction of the electric motor unit, thereby improving the accuracy and foresight of load prediction.

[0066] S15: Based on the operation status assessment results and load forecast information, perform parameter optimization conflict judgment to obtain parameter optimization conflict judgment results; based on the operation status assessment results and load forecast information, perform margin factor analysis to obtain target margin factor.

[0067] In the specific implementation of this invention, the first parameter optimization target of the operation status assessment result and the second parameter optimization target of the load forecast information are determined. Based on the first and second parameter optimization targets, parameter optimization conflict judgment is performed to obtain the parameter optimization conflict judgment result. The degree of fluctuation of environmental parameters is analyzed, and the confidence level of the operation status assessment result is determined based on the degree of fluctuation. The prediction error interval width of the load forecast information is evaluated, and based on the confidence level of the operation status assessment result and the prediction error interval width of the load forecast information, margin factor analysis is performed to obtain the target margin factor. This can identify potential conflicts between the operation status assessment result and the load forecast information in terms of parameter optimization targets, and comprehensively consider environmental fluctuations and prediction errors to quantify the margin factor, providing a decision-making basis for subsequent parameter optimization.

[0068] S16: Determine the target operating parameter range of the motor unit based on the parameter optimization conflict judgment results and the target margin factor;

[0069] In the specific implementation of this invention, the method identifies whether the parameter optimization conflict judgment result indicates a conflict in the parameter optimization target; if the parameter optimization conflict judgment result indicates a conflict in the parameter optimization target, the method identifies the time scale involved when the parameter optimization target conflicts; the method determines the initial operating parameter range based on the operation status assessment result and load forecast information, and adjusts the initial operating parameter range based on the time scale and target margin factor to obtain the target operating parameter range. By introducing the time scale and target margin factor to dynamically adjust the initial operating parameter range, a more reasonable target operating parameter range that is more adapted to actual operation needs can be obtained.

[0070] S17: Adjust the operating parameters of the motor unit based on the target operating parameter range.

[0071] In the specific implementation of this invention, the operating parameters of the motor unit are adjusted according to the target operating parameter range, which overcomes the shortcomings of the passive and lagging traditional monitoring methods, realizes the transformation from passive maintenance to predictive optimization, and significantly improves the operating efficiency and reliability of the motor unit.

[0072] In this embodiment of the invention, thermal impedance analysis is performed based on the operating parameters and environmental parameters of the electric motor unit to obtain thermal impedance data. The operating status of the electric motor unit is then assessed based on this data, enabling a deeper understanding of its internal thermodynamic characteristics. This overcomes the shortcomings of traditional monitoring methods that focus only on surface data while ignoring deeper physical mechanisms. Furthermore, combining thermal impedance data with the operating status assessment provides a more accurate reflection of the actual health condition of the electric motor unit. The status data of key points in the task flow served by the electric motor unit is acquired. Based on this status data and operating parameters, the task scheduling intent and its corresponding credibility are determined. Finally, based on the task scheduling intent, credibility, and the operating status assessment results, load forecasting of the electric motor unit is performed. This makes the load forecasting forward-looking and targeted, solving the problem of insufficient evidence in existing load forecasting techniques, and allowing for a more comprehensive consideration of the impact of future load changes on the electric motor unit. Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed. Margin factor analysis is then conducted based on the operational status assessment results and load forecast information. Based on the parameter optimization conflict judgment results and the target margin factor, the target operating parameter range of the motor unit is determined. Based on the target operating parameter range, the operating parameters of the motor unit are adjusted, achieving adaptive adjustment of the motor unit's operating parameters. This effectively solves the problems of slow parameter adjustment response and inability to dynamically optimize based on predictive information in existing technologies. Consequently, energy consumption and losses are significantly reduced, equipment lifespan is extended, and overall operational safety and economic efficiency are improved.

[0073] Example 2

[0074] Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for monitoring and optimizing the operating status of an electric motor unit according to another embodiment of the present invention. The method includes:

[0075] S201: Obtain the operating parameters and environmental parameters of the motor unit, and perform thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data;

[0076] In the specific implementation of this invention, the step of performing thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data includes: extracting the internal hot spot temperature and electrical parameters based on the operating parameters, calculating the total input power and mechanical output power based on the electrical parameters, and calculating the heat generation rate based on the total input power and mechanical output power; extracting the ambient temperature based on the environmental parameters, and performing thermal impedance analysis based on the internal hot spot temperature, ambient temperature, and heat generation rate to obtain thermal impedance data.

[0077] Specifically, the operating parameters and environmental parameters of the motor set are obtained. The operating parameters are physical quantities that can be directly or indirectly measured during the operation of the motor set, such as voltage, current, power, speed, vibration, bearing temperature, winding temperature, etc. The environmental parameters are physical quantities of the external environment in which the motor set operates, such as ambient temperature, humidity, air pressure, dust concentration, etc.

[0078] Based on the aforementioned operating parameters, the internal hotspot temperature and electrical parameters are extracted. The internal hotspot temperature refers to the temperature of key components inside the motor unit, which can be directly measured using embedded temperature sensors or estimated using a thermal model combined with other operating parameters. Electrical parameters typically include input voltage, input current, and power factor. Based on these electrical parameters, the total input power and mechanical output power are calculated. Total input power refers to the total electrical power absorbed by the motor unit from the grid, and mechanical output power refers to the mechanical energy provided by the motor unit to the outside. Total input power can be calculated using input voltage, input current, and power factor, while mechanical output power can be calculated using speed and torque. Based on the total input power and mechanical output power, the heat generation rate is calculated. The heat generation rate refers to the total amount of heat energy converted from energy loss within the motor unit per unit time, which can be determined by calculating the difference between the total input power and the mechanical output power.

[0079] Based on the environmental parameters, the ambient temperature is extracted. Thermal impedance analysis is then performed based on the internal hot spot temperature, ambient temperature, and heat generation rate to obtain thermal impedance data. Thermal impedance analysis involves establishing an internal heat conduction model of the motor unit and calculating the thermal resistance between various components within the motor unit, combined with the internal hot spot temperature, ambient temperature, and heat generation rate. This quantifies the heat dissipation performance and heat transfer efficiency of the motor unit, improving the accuracy of thermal impedance analysis. It helps to more accurately identify potential overheating risks within the motor unit, providing a solid data foundation for subsequent operational status assessment and parameter optimization, thereby enhancing the comprehensiveness and effectiveness of motor unit operational status monitoring.

[0080] S202: Based on the thermal flow impedance data, perform an operational status assessment of the motor unit to obtain the operational status assessment results;

[0081] In a specific implementation of this invention, the step of evaluating the operating status of the motor unit based on the thermal impedance data to obtain the operating status evaluation result includes: calculating the deviation between the operating parameters and the ideal operating parameters; determining the environmental impact coefficient based on the environmental parameters; and determining the initial operating pressure index based on the deviation and the environmental impact coefficient; determining the thermal resistance correction term based on the thermal impedance data; adjusting the initial operating pressure index based on the thermal resistance correction term to obtain the target operating pressure index; quantifying the internal overheating risk based on the thermal impedance data to obtain the internal overheating risk coefficient; analyzing the synergistic degradation factor based on the operating parameters and environmental parameters; and evaluating the operating status of the motor unit based on the target operating pressure index and the internal overheating risk coefficient combined with the synergistic degradation factor to obtain the operating status evaluation result.

[0082] Specifically, calculating the deviation between operating parameters and ideal operating parameters refers to comparing the current operating parameters of the motor unit with the preset ideal or design operating parameters. This deviation quantifies the degree to which the motor unit deviates from its optimal operating state under current conditions. An environmental impact coefficient is determined based on the environmental parameters; this coefficient characterizes the influence of environmental factors on the operating performance and thermal characteristics of the motor unit. An initial operating pressure index is determined based on the deviation and the environmental impact coefficient. This can be achieved by determining the weight of the deviation, calculating the first product of the deviation and its weight, calculating the difference between the environmental parameters and ideal environmental parameters, calculating the second product of the environmental impact coefficient and the difference between the environmental parameters and ideal environmental parameters, calculating the quotient of the second product and the range of environmental temperature variation, and calculating the sum of the first and second products. This sum is used as the initial operating pressure index, which serves as a preliminary quantitative indicator of the motor unit's operating pressure.

[0083] A thermal resistance correction term is determined based on the aforementioned thermal flow impedance data. This term is calculated according to the actual heat flow path and thermal resistance characteristics within the motor unit and is used to correct the initial operating pressure index, making it more accurately reflect the stress state of the motor unit under actual thermal load. The initial operating pressure index is then adjusted based on this thermal resistance correction term to obtain a target operating pressure index, which can more accurately assess the overall operating pressure of the motor unit.

[0084] Based on the heat flow impedance data, the internal overheating risk is quantified to obtain the internal overheating risk coefficient. By analyzing the heat flow impedance data, it is possible to identify local hot spots or overheated areas that may exist inside the motor unit and quantify their overheating risk level in order to predict potential failure risks.

[0085] Based on the analysis of the aforementioned operating parameters and environmental parameters, a synergistic degradation factor is analyzed. This synergistic degradation factor aims to capture the cumulative impact of the interaction between different operating parameters and environmental parameters on the performance and lifespan of the motor unit, such as the accelerated aging of insulation when high temperature and high load coexist.

[0086] The operating status assessment of the motor unit is based on the target operating pressure index and internal overheating risk coefficient combined with the synergistic deterioration factor. The resulting operating status assessment can more accurately capture the true health status of the motor unit under complex operating conditions and environments, effectively avoiding the one-sidedness and lag that may exist in traditional assessment methods. This provides more reliable basic data for subsequent load forecasting and parameter optimization.

[0087] It should be noted that the analysis of synergistic degradation factors based on the operating parameters and environmental parameters includes: extracting features from the operating parameters and environmental parameters to obtain parameter features, and performing synergistic degradation mode feature matching based on the parameter features to obtain target synergistic degradation mode features; and analyzing synergistic degradation factors based on the target synergistic degradation mode features.

[0088] Specifically, feature extraction is performed on the operating parameters and environmental parameters to obtain parameter features. Feature extraction refers to extracting key information that can effectively characterize the operating state and environmental impact of the electric motor unit from the original operating parameters and environmental parameters through statistical analysis or machine learning methods. For example, the mean, variance, peak value, frequency components, and trend changes of the parameters can be extracted. The purpose is to transform the raw data into more representative and analyzable parameter features. Based on the parameter features, cooperative degradation mode feature matching is performed to obtain target cooperative degradation mode features. Cooperative degradation mode feature matching refers to comparing the currently extracted parameter features with a pre-established feature library containing different cooperative degradation modes. This feature library can be built based on historical data, expert experience, or simulation models. Each mode feature represents a type of cooperative degradation, such as accelerated insulation aging caused by high temperature and humidity, or increased bearing wear caused by overload and vibration coupling. The matching process can use distance measurement, similarity calculation, or classification algorithms to identify which known cooperative degradation mode the current operating state is closest to.

[0089] Based on the characteristics of the aforementioned collaborative degradation modes, the analysis of collaborative degradation factors refers to further quantifying or evaluating the impact of specific collaborative degradation modes on the performance and lifespan of electric motor units after identification. The collaborative degradation factor can be a single numerical value representing the level of degradation risk, or a combination of multiple indicators used to describe the interaction strength of different degradation mechanisms. Its purpose is to provide more comprehensive and accurate degradation information for subsequent operational status assessments.

[0090] S203: Obtain the status data of key points in the task process served by the motor unit, and determine the task scheduling intention and the credibility corresponding to the task scheduling intention based on the status data and operating parameters;

[0091] In a specific implementation of this invention, determining the task scheduling intent and the corresponding credibility of the task scheduling intent based on the status data and operating parameters includes: extracting instantaneous features of the status data and operating parameters based on an instantaneous data window; calculating the matching degree between the instantaneous features and each task mode feature, and taking the task mode feature with the highest matching degree as the target task mode feature; inferring the task scheduling intent based on the target task mode feature to obtain the task scheduling intent of key points in the task flow served by the motor unit; acquiring the acquisition and processing delay of the status data and operating parameters, and determining the credibility of the task scheduling intent based on the matching degree between the acquisition and processing delay and the target task mode feature.

[0092] Specifically, the system acquires status data of key points in the task flow served by the electric motor unit. This status data refers to the operational data of specific links or equipment in the entire production or operation process served by the electric motor unit that have a decisive impact on the load and operating mode of the electric motor unit, such as production line speed, pump flow rate, and fan pressure. Instantaneous features of the status data and operating parameters are extracted based on an instantaneous data window. The instantaneous data window refers to a preset time period through which the operating parameters and status data of the electric motor unit can be sampled and analyzed in real time or near real time, thereby extracting instantaneous features that reflect the current operating status. These instantaneous features may include, but are not limited to, instantaneous values ​​or rates of change of parameters such as current, voltage, speed, vibration, and temperature. The purpose of extracting instantaneous features is to capture the dynamic behavior of the electric motor unit within a short period of time, providing basic data for subsequent task mode recognition.

[0093] The matching degree between the instantaneous features and the features of each task mode is calculated. Task mode features can be understood as a set of typical features predefined or learned from historical data, corresponding to various task modes that the motor unit may perform. For example, different production tasks (such as startup, stable operation, acceleration, deceleration, shutdown, load changes, etc.) will cause the motor unit to exhibit certain operating parameters and state data patterns. The matching degree can be achieved through various similarity calculation methods, such as Euclidean distance, cosine similarity, correlation coefficient, etc. The task mode feature with the highest matching degree is taken as the target task mode feature, meaning that the current operating state of the motor unit is determined to be most consistent with the target task mode.

[0094] Based on the target task pattern characteristics, task scheduling intent is inferred to obtain the task scheduling intent of key points in the task process served by the electric motor unit. Task scheduling intent inference refers to predicting tasks or their operating trends that may be executed in the future based on the identified target task pattern characteristics, combined with preset business logic or based on machine learning models. For example, if the target task pattern characteristics indicate that the electric motor unit is in the high-load start-up phase, the task scheduling intent may be inferred to be the need to continuously provide high power output. By inferring the task scheduling intent, the future working needs of the electric motor unit can be predicted in advance, providing forward-looking information for subsequent load forecasting and parameter optimization.

[0095] The acquisition and processing delay of status data and operating parameters refers to the time interval between the acquisition of data by sensors and the processing of the data by the system for decision-making. This delay may affect the real-time performance and accuracy of task scheduling intentions. The credibility of the task scheduling intention is determined based on the matching degree between the acquisition and processing delay and the characteristics of the target task mode. This aims to quantify the reliability of the inferred task scheduling intention. Generally, the smaller the acquisition and processing delay and the higher the matching degree of the target task mode characteristics, the higher the credibility of the task scheduling intention. Credibility can be a value between 0 and 1, used to represent the confidence level of the inference result, providing a risk assessment basis for subsequent load forecasting and parameter optimization, and avoiding potential risks caused by uncertainty. It is precisely because of this accurate identification of the current task mode and the reliability assessment of the scheduling intention that the monitoring and optimization process of the motor unit's operating status becomes more forward-looking and robust.

[0096] S204: Based on the task scheduling intent and credibility combined with the operating status evaluation results, perform load prediction of the motor group to obtain load prediction information;

[0097] In the specific implementation of this invention, a load margin coefficient is matched based on the aforementioned confidence level. The purpose is to introduce a dynamically adjusted margin in load forecasting to address the uncertainty of task scheduling intentions. For example, when the confidence level is high, a smaller load margin coefficient can be matched, indicating a higher confidence in the forecast result; when the confidence level is low, a larger load margin coefficient is matched to increase the conservatism of the forecast and reduce the risk caused by forecast errors. The load margin coefficient can be a preset lookup table or a value dynamically generated by a machine learning model based on historical data and confidence level. Based on the load margin coefficient, task scheduling intention, and operating status evaluation results, the random forest algorithm is used to forecast the load of the electric motor units, obtaining load forecast information. The random forest algorithm can handle high-dimensional data, has good modeling ability for nonlinear relationships of input features, and is robust to noise and overfitting. When forecasting the load of the electric motor units, the random forest algorithm uses the load margin coefficient, task scheduling intention, and operating status evaluation results as input features, and outputs the load forecast information of the electric motor units through a trained model.

[0098] S205: Determine the first parameter optimization target of the operation status assessment result and the second parameter optimization target of the load forecast information, and make a parameter optimization conflict judgment based on the first parameter optimization target and the second parameter optimization target to obtain the parameter optimization conflict judgment result;

[0099] In the specific implementation of this invention, a first parameter optimization objective based on the operating status assessment result and a second parameter optimization objective based on the load forecast information are determined. The first parameter optimization objective typically refers to an optimization objective directly related to the operating status assessment result of the motor unit, such as aiming to minimize wear, extend lifespan, and reduce failure rate. It can be a combination of one or more indicators, reflecting the optimal operating expectation of the motor unit under its current health condition. The second parameter optimization objective typically refers to an optimization objective directly related to the load forecast information of the motor unit, such as aiming to meet future task requirements, optimize energy consumption, and improve response speed. It can be a combination of one or more indicators, reflecting the optimal operating expectation of the motor unit under future load demands. These objectives can be quantified into specific numerical ranges or performance indicators, such as operating temperature not exceeding a certain threshold, efficiency not falling below a certain percentage, and response time within a certain range.

[0100] Based on the first and second parameter optimization objectives, a parameter optimization conflict judgment is performed to obtain the result. Parameter optimization conflict judgment refers to assessing whether there is a contradiction or incompatibility between the first and second parameter optimization objectives. For example, the operational status assessment result may suggest reducing the load to extend equipment life, while load forecast information may require increasing the load to meet emergency task requirements. This judgment can be made by comparing the priority, scope of influence, or degree of overlap of the two optimization objectives. For example, a multi-objective optimization model can be constructed, and potential conflicts can be identified by analyzing the Pareto front between different objective functions. When two objectives cannot simultaneously reach their optimum or meet preset conditions within the same operating parameter range, a conflict is judged to exist.

[0101] S206: Analyze the degree of fluctuation of environmental parameters and determine the confidence level of the operational status assessment results based on the degree of fluctuation;

[0102] In the specific implementation of this invention, the degree of fluctuation of environmental parameters is analyzed. The degree of fluctuation of environmental parameters can be understood as the range or rate of change of external conditions such as ambient temperature, humidity, and air pressure within a certain time window. For example, the degree of fluctuation can be quantified by calculating the standard deviation, coefficient of variation, or maximum-minimum difference of environmental parameters. Based on the degree of fluctuation, the confidence level of the operating status assessment result is determined. Confidence level refers to a quantitative measure of the reliability of the current motor unit operating status assessment result. Drastic fluctuations in environmental parameters may introduce measurement errors or model uncertainties, thereby reducing the confidence level of the operating status assessment result. Therefore, determining the confidence level based on the degree of fluctuation of environmental parameters means that when environmental fluctuations are large, the confidence level of the assessment result will decrease accordingly, and vice versa. For example, a mapping relationship can be established to map the degree of fluctuation of environmental parameters to a confidence level value between 0 and 1; the greater the fluctuation, the lower the confidence level.

[0103] S207: Evaluate the prediction error interval width of the load forecast information, and perform margin factor analysis based on the confidence level of the operation status evaluation results and the prediction error interval width of the load forecast information to obtain the target margin factor.

[0104] In the specific implementation of this invention, the prediction error interval width of the load forecast information is evaluated. The prediction error interval width refers to the quantification of the uncertainty of future load forecast results. Any forecast will have errors, and this interval width reflects the range in which the predicted value may deviate from the true value. For example, it can be obtained through statistical methods or prediction uncertainty estimation using machine learning models. Based on the confidence level of the operating status assessment results and the prediction error interval width of the load forecast information, margin factor analysis is performed to obtain the target margin factor. Margin factor analysis refers to comprehensively considering the confidence level of the operating status assessment results and the prediction error interval width of the load forecast information to determine a margin factor for adjusting the operating parameters of the motor unit. This margin factor aims to reserve a certain safety margin or redundancy for the future operation of the motor unit. For example, when the confidence level of the operating status assessment results is low or the prediction error interval width of the load forecast information is large, a larger margin factor needs to be set to cope with higher uncertainty risks. Conversely, when the confidence level is high and the error interval is narrow, a smaller margin factor can be set to improve operating efficiency. The target margin factor is used to guide the determination of the target operating parameter range of the subsequent motor units. This factor comprehensively reflects the reliability of the current operating status and the uncertainty of future load forecasts, thus providing a guarantee for the robust operation of the motor units.

[0105] S208: Determine the target operating parameter range of the motor unit based on the parameter optimization conflict judgment results and the target margin factor;

[0106] In a specific implementation of this invention, determining the target operating parameter range of the motor unit based on the parameter optimization conflict judgment result and the target margin factor includes: identifying whether the parameter optimization conflict judgment result indicates a conflict in the parameter optimization target; if the parameter optimization conflict judgment result indicates a conflict in the parameter optimization target, identifying the time scale involved when the parameter optimization target conflict occurs; determining the initial operating parameter range based on the operating status evaluation result and load forecast information, and adjusting the initial operating parameter range based on the time scale and the target margin factor to obtain the target operating parameter range.

[0107] Specifically, identifying whether the parameter optimization conflict judgment result indicates a conflict in the parameter optimization objectives means analyzing the previously obtained parameter optimization conflict judgment results to determine whether there are situations where multiple optimization objectives contradict each other or are difficult to satisfy simultaneously. For example, one optimization objective might be to maximize the efficiency of the motor unit, while another optimization objective might be to minimize wear or extend lifespan. These two objectives may conflict under certain operating conditions. If the parameter optimization conflict judgment result indicates that there is no conflict in the parameter optimization objectives, then there is no need to balance the first and second parameter optimization objectives, and subsequent processing can directly adjust the initial operating parameter range based on the original parameter optimization objectives.

[0108] If the parameter optimization conflict judgment result indicates that there is a conflict in the parameter optimization objective, the time scale involved in the conflict can be understood as determining whether these conflicts are short-term, medium-term, or long-term. For example, short-term conflicts may be related to instantaneous load fluctuations, while long-term conflicts may be related to equipment aging or long-term task plans. Identifying the time scale helps to take targeted adjustment strategies in the future.

[0109] Determining the initial operating parameter range based on the operational status assessment results and load forecast information means comprehensively considering the current health status of the motor unit and future workload requirements to initially define a feasible range of operating parameters. For example, if the operational status assessment results indicate a slight risk of overheating in the motor unit, and the load forecast information shows that the future load will increase, the initial operating parameter range may be set relatively conservatively. Adjusting the initial operating parameter range based on the time scale and target margin factor to obtain the target operating parameter range aims to optimize the initial range while considering the temporal characteristics of the conflict and the required system margin. For example, for short-term conflicts, rapid parameter adjustments may be necessary to avoid instantaneous overload; for long-term conflicts, a more stable parameter adjustment strategy may be required. The target margin factor ensures that the adjusted parameter range meets performance requirements while retaining a certain safety and reliability margin, avoiding blind adjustments or suboptimal solutions that may exist in traditional methods.

[0110] Furthermore, adjusting the initial operating parameter range based on the time scale and the target margin factor to obtain the target operating parameter range includes: setting priority rules based on the time scale; balancing the first parameter optimization target and the second parameter optimization target based on the priority rules to obtain the balanced first parameter optimization target and the balanced second parameter optimization target; and adjusting the initial operating parameter range based on the balanced first parameter optimization target and the balanced second parameter optimization target combined with the target margin factor to obtain the target operating parameter range.

[0111] Specifically, priority rules are set based on the time scale. Priority rules refer to guiding principles for selecting or weighing multiple optimization objectives. The purpose is to ensure that the operation of the motor unit can prioritize meeting the most critical needs at different time scales. For example, at a short time scale, the safety and stability of the motor unit may be given priority to avoid sudden failures; while at a long time scale, operating efficiency and equipment life may be given priority to maximize economic benefits. These priority rules can be preset or dynamically adjusted according to the specific application scenario of the motor unit, historical operating data, and expert experience.

[0112] Based on the priority rules, the optimization objectives for the first and second parameters are balanced to obtain balanced optimization objectives. Balancing involves finding a compromise between two or more conflicting optimization objectives, ensuring that each objective is satisfied to a certain extent. Specifically, balancing can be achieved using weighted averaging, multi-objective optimization algorithms, or constraint satisfaction. For example, different weights can be assigned to the first and second parameter optimization objectives according to the priority rules, and then a comprehensive optimization objective can be obtained through weighted summation; alternatively, one objective can be used as the primary optimization objective, and the other as a constraint. These objectives, after considering the time scale and priority, can better guide subsequent parameter range adjustments.

[0113] The initial operating parameter range is adjusted based on the first and second parameter optimization objectives after balancing, combined with the target margin factor, to obtain the target operating parameter range. By combining the optimization objectives after balancing with the target margin factor, it can be ensured that the adjusted target operating parameter range can not only effectively resolve parameter optimization conflicts, but also provide a safe and efficient operating range for the motor unit under the consideration of uncertainties.

[0114] It should be noted that the time scale involved when the optimization objectives of the identification parameters conflict includes: obtaining the operating mode of the motor group and the dynamic characteristics of the task plan to which the motor belongs, and determining the time scale division standard based on the operating mode and dynamic characteristics; and identifying the time scale involved when the optimization objectives of the identification parameters conflict based on the time scale division standard.

[0115] Specifically, the process involves acquiring the operating mode of the electric motor set and the dynamic characteristics of the task plan to which the motor belongs. The operating mode refers to the typical working state of the electric motor set under different operating conditions, such as continuous operation, intermittent operation, and start / stop cycle mode. These operating modes can usually be obtained through preset control strategies or analysis of historical operating data. Dynamic characteristics refer to the dynamic changes in time requirements, load variation trends, priorities, and other dynamic features at various key points in the entire task process served by the electric motor set. For example, one task may require the electric motor set to provide high power output in a short period, while another task may require the electric motor set to operate stably for a long period. Based on the operating mode and dynamic characteristics, a time scale division standard is determined. This time scale division standard is a time scale identification and division strategy with different time granularities defined according to the operating mode of the electric motor set and the dynamic characteristics of the task plan.

[0116] Identifying the time scale involved when there is a conflict in the parameter optimization objectives based on the time scale division standard refers to determining at which time granularity the current parameter optimization objective conflict occurs, according to the time scale division standard. For example, if the conflict is caused by a sudden change in instantaneous load, it is identified as a short-time scale conflict; if it is caused by performance degradation due to long-term operation wear, it is identified as a long-time scale conflict. This makes subsequent parameter adjustments more targeted and avoids the inefficiency or over-intervention problems that may be caused by a one-size-fits-all adjustment strategy.

[0117] S209: Adjust the operating parameters of the motor unit based on the target operating parameter range.

[0118] In the specific implementation of this invention, the operating parameters of the motor group are adjusted based on the target operating parameter range. For example, the power supply frequency and voltage of the motor can be adjusted by a frequency converter, thereby changing the speed and output power so that its operating parameters fall within the target operating parameter range.

[0119] In this embodiment of the invention, thermal impedance analysis is performed based on the operating parameters and environmental parameters of the electric motor unit to obtain thermal impedance data. The operating status of the electric motor unit is then assessed based on this data, enabling a deeper understanding of its internal thermodynamic characteristics. This overcomes the shortcomings of traditional monitoring methods that focus only on surface data while ignoring deeper physical mechanisms. Furthermore, combining thermal impedance data with the operating status assessment provides a more accurate reflection of the actual health condition of the electric motor unit. The status data of key points in the task flow served by the electric motor unit is acquired. Based on this status data and operating parameters, the task scheduling intent and its corresponding credibility are determined. Finally, based on the task scheduling intent, credibility, and the operating status assessment results, load forecasting of the electric motor unit is performed. This makes the load forecasting forward-looking and targeted, solving the problem of insufficient evidence in existing load forecasting techniques, and allowing for a more comprehensive consideration of the impact of future load changes on the electric motor unit. Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed. Margin factor analysis is then conducted based on the operational status assessment results and load forecast information. Based on the parameter optimization conflict judgment results and the target margin factor, the target operating parameter range of the motor unit is determined. Based on the target operating parameter range, the operating parameters of the motor unit are adjusted, achieving adaptive adjustment of the motor unit's operating parameters. This effectively solves the problems of slow parameter adjustment response and inability to dynamically optimize based on predictive information in existing technologies. Consequently, energy consumption and losses are significantly reduced, equipment lifespan is extended, and overall operational safety and economic efficiency are improved.

[0120] Example 3

[0121] Please see Figure 3 , Figure 3This is a schematic diagram of the structural composition of the motor unit operation status monitoring and optimization device in an embodiment of the present invention. The device includes:

[0122] Thermal impedance analysis module 31: used to acquire the operating parameters and environmental parameters of the motor unit, and to perform thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data;

[0123] Operation status assessment module 32: used to assess the operation status of the motor unit based on the thermal impedance data and obtain the operation status assessment result;

[0124] Intent determination module 33: used to obtain the status data of key points in the task process served by the motor group, and determine the task scheduling intent and the credibility corresponding to the task scheduling intent based on the status data and operating parameters;

[0125] Load forecasting module 34: used to forecast the load of the motor group based on the task scheduling intention and credibility combined with the operation status evaluation result, and obtain load forecasting information;

[0126] Judgment and analysis module 35: used to judge parameter optimization conflicts based on the operation status assessment results and load forecast information, obtain parameter optimization conflict judgment results, and perform margin factor analysis based on the operation status assessment results and load forecast information to obtain target margin factor;

[0127] Parameter range determination module 36: used to determine the target operating parameter range of the motor set based on the parameter optimization conflict judgment result and the target margin factor;

[0128] Parameter adjustment module 37: used to adjust the operating parameters of the motor set based on the target operating parameter range.

[0129] In the specific implementation of this invention, the specific implementation of the device item can be referred to the implementation of the method item above, and will not be repeated here.

[0130] In this embodiment of the invention, thermal impedance analysis is performed based on the operating parameters and environmental parameters of the electric motor unit to obtain thermal impedance data. The operating status of the electric motor unit is then assessed based on this data, enabling a deeper understanding of its internal thermodynamic characteristics. This overcomes the shortcomings of traditional monitoring methods that focus only on surface data while ignoring deeper physical mechanisms. Furthermore, combining thermal impedance data with the operating status assessment provides a more accurate reflection of the actual health condition of the electric motor unit. The status data of key points in the task flow served by the electric motor unit is acquired. Based on this status data and operating parameters, the task scheduling intent and its corresponding credibility are determined. Finally, based on the task scheduling intent, credibility, and the operating status assessment results, load forecasting of the electric motor unit is performed. This makes the load forecasting forward-looking and targeted, solving the problem of insufficient evidence in existing load forecasting techniques, and allowing for a more comprehensive consideration of the impact of future load changes on the electric motor unit. Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed. Margin factor analysis is then conducted based on the operational status assessment results and load forecast information. Based on the parameter optimization conflict judgment results and the target margin factor, the target operating parameter range of the motor unit is determined. Based on the target operating parameter range, the operating parameters of the motor unit are adjusted, achieving adaptive adjustment of the motor unit's operating parameters. This effectively solves the problems of slow parameter adjustment response and inability to dynamically optimize based on predictive information in existing technologies. Consequently, energy consumption and losses are significantly reduced, equipment lifespan is extended, and overall operational safety and economic efficiency are improved.

[0131] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the method for monitoring and optimizing the operating status of a motor assembly, as described in any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.

[0132] Example 4

[0133] Please see Figure 4 , Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention.

[0134] This invention also provides an electronic device, such as... Figure 4 As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. Those skilled in the art will understand that... Figure 4 The illustrated electronic device does not constitute a limitation on all devices and may include more or fewer components than illustrated, or combine certain components. Memory 41 can be used to store computer program 42 and various functional modules. Processor 43 runs the computer program 42 stored in memory 41, thereby performing various functional applications and data processing of the device. Memory can be internal memory or external memory, or both. Internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External memory may include hard disks, floppy disks, ZIP disks, USB flash drives, magnetic tapes, etc. Processor 43 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or a processor 43, or any conventional processor, etc. The processors and memories disclosed in this invention include, but are not limited to, these types of processors and memories. The processors and memories disclosed in this invention are merely examples and not intended to be limiting.

[0135] As one embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the motor group operation status monitoring and optimization method in any of the above embodiments. For specific implementation process, please refer to the above embodiments, which will not be repeated here.

[0136] In this embodiment of the invention, thermal impedance analysis is performed based on the operating parameters and environmental parameters of the electric motor unit to obtain thermal impedance data. The operating status of the electric motor unit is then assessed based on this data, enabling a deeper understanding of its internal thermodynamic characteristics. This overcomes the shortcomings of traditional monitoring methods that focus only on surface data while ignoring deeper physical mechanisms. Furthermore, combining thermal impedance data with the operating status assessment provides a more accurate reflection of the actual health condition of the electric motor unit. The status data of key points in the task flow served by the electric motor unit is acquired. Based on this status data and operating parameters, the task scheduling intent and its corresponding credibility are determined. Finally, based on the task scheduling intent, credibility, and the operating status assessment results, load forecasting of the electric motor unit is performed. This makes the load forecasting forward-looking and targeted, solving the problem of insufficient evidence in existing load forecasting techniques, and allowing for a more comprehensive consideration of the impact of future load changes on the electric motor unit. Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed. Margin factor analysis is then conducted based on the operational status assessment results and load forecast information. Based on the parameter optimization conflict judgment results and the target margin factor, the target operating parameter range of the motor unit is determined. Based on the target operating parameter range, the operating parameters of the motor unit are adjusted, achieving adaptive adjustment of the motor unit's operating parameters. This effectively solves the problems of slow parameter adjustment response and inability to dynamically optimize based on predictive information in existing technologies. Consequently, energy consumption and losses are significantly reduced, equipment lifespan is extended, and overall operational safety and economic efficiency are improved.

[0137] Furthermore, the above provides a detailed description of the method and related device for monitoring and optimizing the operating status of an electric motor unit provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring and optimizing the operating status of an electric motor unit, characterized in that, The method includes: The operating parameters and environmental parameters of the motor unit are obtained, and thermal impedance analysis is performed based on the operating parameters and environmental parameters to obtain thermal impedance data; Based on the thermal flow impedance data, the operating status of the motor unit is evaluated to obtain the operating status evaluation results. Obtain the status data of key points in the task process served by the motor unit, and determine the task scheduling intention and the credibility corresponding to the task scheduling intention based on the status data and operating parameters; Based on the task scheduling intent and credibility combined with the operating status evaluation results, load prediction of the motor group is performed to obtain load prediction information; Based on the operational status assessment results and load forecast information, parameter optimization conflict judgment is performed to obtain parameter optimization conflict judgment results. Based on the operational status assessment results and load forecast information, margin factor analysis is performed to obtain the target margin factor. The target operating parameter range of the motor unit is determined based on the parameter optimization conflict judgment results and the target margin factor. The operating parameters of the motor unit are adjusted based on the target operating parameter range; The step of evaluating the operating status of the motor unit based on the thermal impedance data to obtain the operating status evaluation result includes: calculating the deviation of the operating parameters from the ideal operating parameters; determining the environmental impact coefficient based on the environmental parameters; and determining the initial operating pressure index based on the deviation and the environmental impact coefficient; determining the thermal resistance correction term based on the thermal impedance data; adjusting the initial operating pressure index based on the thermal resistance correction term to obtain the target operating pressure index; quantifying the internal overheating risk based on the thermal impedance data to obtain the internal overheating risk coefficient; analyzing the synergistic degradation factor based on the operating parameters and environmental parameters; and evaluating the operating status of the motor unit based on the target operating pressure index and the internal overheating risk coefficient combined with the synergistic degradation factor to obtain the operating status evaluation result.

2. The method for monitoring and optimizing the operating status of an electric motor unit according to claim 1, characterized in that, The process of performing thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data includes: Based on the operating parameters, the internal hot spot temperature and electrical parameters are extracted, and the total input power and mechanical output power are calculated based on the electrical parameters. The heat generation rate is then calculated based on the total input power and mechanical output power. The ambient temperature is extracted based on the environmental parameters, and thermal impedance analysis is performed based on the internal hot spot temperature, ambient temperature, and heat generation rate to obtain thermal impedance data.

3. The method for monitoring and optimizing the operating status of a motor set according to claim 1, characterized in that, The process of determining the task scheduling intent and the corresponding credibility of the task scheduling intent based on the status data and operating parameters includes: The instantaneous features of the state data and operating parameters are extracted based on the instantaneous data window; Calculate the matching degree between the instantaneous feature and each task mode feature, and take the task mode feature with the highest matching degree as the target task mode feature; Based on the target task mode characteristics, the task scheduling intent is inferred to obtain the task scheduling intent of key points in the task process served by the electric motor unit. The acquisition and processing delay of status data and operating parameters are obtained, and the credibility of the task scheduling intention is determined based on the matching degree between the acquisition and processing delay and the target task mode characteristics.

4. The method for monitoring and optimizing the operating status of an electric motor unit according to claim 1, characterized in that, The step involves determining parameter optimization conflicts based on the operational status assessment results and load forecast information, obtaining parameter optimization conflict determination results, and performing margin factor analysis based on the operational status assessment results and load forecast information to obtain the target margin factor, including: The first parameter optimization objective of the operation status assessment result and the second parameter optimization objective of the load forecast information are determined, and parameter optimization conflict judgment is performed based on the first parameter optimization objective and the second parameter optimization objective to obtain the parameter optimization conflict judgment result; Analyze the degree of fluctuation of environmental parameters, and determine the confidence level of the operational status assessment results based on the degree of fluctuation; The prediction error interval width of the load forecast information is evaluated, and a margin factor analysis is performed based on the confidence level of the operation status evaluation results and the prediction error interval width of the load forecast information to obtain the target margin factor.

5. The method for monitoring and optimizing the operating status of a motor set according to claim 4, characterized in that, The determination of the target operating parameter range of the motor unit based on the parameter optimization conflict judgment result and the target margin factor includes: The determination result of the parameter optimization conflict is whether there is a conflict in the parameter optimization objectives; If the parameter optimization conflict judgment result indicates that there is a conflict in the parameter optimization objectives, identify the time scale involved when the parameter optimization objectives conflict; The initial operating parameter range is determined based on the operating status assessment results and load forecast information, and the initial operating parameter range is adjusted based on the time scale and target margin factor to obtain the target operating parameter range.

6. The method for monitoring and optimizing the operating status of an electric motor unit according to claim 5, characterized in that, The step of adjusting the initial operating parameter range based on the time scale and the target margin factor to obtain the target operating parameter range includes: Priority rules are set based on the aforementioned time scale; The first parameter optimization objective and the second parameter optimization objective are balanced based on the priority rule to obtain the balanced first parameter optimization objective and the second parameter optimization objective. The initial operating parameter range is adjusted based on the first parameter optimization objective and the second parameter optimization objective after balancing, combined with the target margin factor, to obtain the target operating parameter range.

7. A device for monitoring and optimizing the operating status of an electric motor unit, characterized in that, The device includes: Thermal impedance analysis module: used to acquire the operating parameters and environmental parameters of the motor unit, and to perform thermal impedance analysis based on the operating parameters and environmental parameters to obtain thermal impedance data; Operation status assessment module: used to assess the operation status of the motor unit based on the thermal impedance data and obtain the operation status assessment results; Intent determination module: used to acquire the status data of key points in the task flow served by the motor unit, and determine the task scheduling intent and the credibility of the task scheduling intent based on the status data and operating parameters; Load forecasting module: used to forecast the load of the motor group based on the task scheduling intention and credibility combined with the operation status evaluation results, and obtain load forecasting information; Judgment and Analysis Module: Used to judge parameter optimization conflicts based on the operation status assessment results and load forecast information, obtain parameter optimization conflict judgment results, and perform margin factor analysis based on the operation status assessment results and load forecast information to obtain target margin factor; Parameter range determination module: used to determine the target operating parameter range of the motor set based on the parameter optimization conflict judgment result and the target margin factor; Parameter adjustment module: used to adjust the operating parameters of the motor set based on the target operating parameter range; The step of evaluating the operating status of the motor unit based on the thermal impedance data to obtain the operating status evaluation result includes: calculating the deviation of the operating parameters from the ideal operating parameters; determining the environmental impact coefficient based on the environmental parameters; and determining the initial operating pressure index based on the deviation and the environmental impact coefficient; determining the thermal resistance correction term based on the thermal impedance data; adjusting the initial operating pressure index based on the thermal resistance correction term to obtain the target operating pressure index; quantifying the internal overheating risk based on the thermal impedance data to obtain the internal overheating risk coefficient; analyzing the synergistic degradation factor based on the operating parameters and environmental parameters; and evaluating the operating status of the motor unit based on the target operating pressure index and the internal overheating risk coefficient combined with the synergistic degradation factor to obtain the operating status evaluation result.

8. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the motor group operation status monitoring and optimization method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the motor unit operation status monitoring and optimization method as described in any one of claims 1 to 6.

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