Anti-burning control system of permanent magnet synchronous motor

Abstract

The invention provides an anti-burning control system for a permanent magnet synchronous motor, belongs to the technical field of computer data processing, and is used for monitoring various data of the permanent magnet synchronous motor, acquiring real-time operation data of the permanent magnet synchronous motor and establishing a real-time database based on the real-time data. Generating a dynamic security threshold based on the historical operation data in the real-time database; and comparing the comprehensive abnormal index with a dynamic safety threshold value, and performing control operation on the permanent magnet synchronous motor based on a comparison result. According to the method, a corresponding permanent magnet motor self-adaptive safety threshold value can be generated according to real-time operation data and historical behavior modes of the motor through data processing of a construction set, so that the limitation of a traditional fixed threshold value in the face of complex and changeable working conditions is overcome, the fault recognition accuracy is remarkably improved, and the fault recognition efficiency is improved. False report and missing report caused by improper threshold values are effectively avoided, and meanwhile, independent current, rotating speed and temperature data are fused into a comprehensive index.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a burn-out prevention control system for permanent magnet synchronous motors. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs), as highly efficient and energy-saving power devices, are widely used in electric vehicles, industrial automation, and precision manufacturing due to their simple structure, high power density, and superior control performance. With the continuous expansion of their application scenarios and the increasing demands for equipment reliability, effective monitoring of the operating status and fault protection of PMSMs to prevent burnout caused by overload, poor heat dissipation, or component aging has become a crucial aspect of ensuring production safety and efficiency.

[0003] Currently, protection for permanent magnet synchronous motors typically employs a monitoring system combining computers and sensors. These systems use sensors to collect real-time operating parameters such as motor current, speed, and temperature, comparing the collected data with pre-set fixed safety thresholds. Once a parameter's value exceeds its corresponding threshold, the system triggers an alarm or executes an emergency stop command, cutting off the motor's power supply to prevent equipment damage. This method, based on data acquisition and threshold comparison, provides a basic level of protection for the motor.

[0004] However, the aforementioned existing technical solutions have significant shortcomings. The fixed safety thresholds upon which their protection logic relies are often based on theoretical design or universally applicable empirical data, failing to adapt to the dynamic changes of specific motors under different loads, ambient temperatures, and their own aging processes. This leads to potential misjudgments in actual operation. Furthermore, most existing technologies perform individual detection and judgment of parameters such as current, speed, and temperature, lacking comprehensive detection and analysis of the inherent correlations between these parameters. This makes it difficult to identify complex fault modes where multiple parameters are abnormally correlated but a single parameter has not yet exceeded its limit, thus limiting the sensitivity and foresight of the protection. Summary of the Invention

[0005] This invention provides a permanent magnet synchronous motor anti-burnout control system for protecting permanent magnet motors from burnout.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, a burnout prevention control system for a permanent magnet synchronous motor is provided. The system is applied to motor control. The method includes: acquiring real-time operating data of the permanent magnet synchronous motor and establishing a real-time database containing historical operating data based on the real-time operating data. Based on the historical operational data in the real-time database, dynamic safety thresholds are generated through adaptive analysis; Based on the real-time operational data, a comprehensive anomaly index is generated through multi-parameter collaborative analysis. For example, the current current, speed and temperature data can be integrated into a single comprehensive abnormality index that can comprehensively reflect the health status of the motor. The comprehensive anomaly index is compared with the dynamic safety threshold, and control operations for the permanent magnet synchronous motor are generated based on the comparison results.

[0007] Optionally, the step of acquiring real-time operating data of the permanent magnet synchronous motor and establishing a real-time database containing historical operating data based on the real-time operating data includes: The current, speed, and temperature data of the permanent magnet synchronous motor are collected synchronously and integrated into a set of operating data with timestamps. The current, speed, and temperature data acquired within the same sampling period are synchronously integrated to form a structured set of operating data. This set accurately reflects the comprehensive operating state of the motor at that instant. This set of operating data can be represented as a data tuple D, with the following form: D = (t, I, S, T) Where t represents the timestamp, I represents the collected current data, S represents the rotation speed data, and T represents the temperature data. By marking each set of running data D with a unique timestamp t, the timeliness and traceability of the data are guaranteed. Finally, the running data set D carrying the timestamp is continuously written into a real-time database. The set of operational data is continuously stored in a storage device to form the real-time database; The real-time database contains timestamped current, rotational speed, and temperature data, and the database is gradually improved over time. The historical operation data is obtained by extracting data from the real-time database according to the time series.

[0008] Optionally, the step of generating a dynamic security threshold based on the historical operational data in the real-time database through adaptive analysis includes: Distinguish between normal state data and abnormal state data from the historical operational data; Normal state data refers to data during the healthy and stable operation of the motor, while abnormal state data refers to abnormal state data recorded before a known fault occurs or under abnormal operating conditions. Statistical characteristics of operating parameters are calculated based on the normal state data to obtain dynamic adjustment parameters; The dynamic safety threshold is generated by combining the dynamically adjusted parameters with the basic threshold.

[0009] Optionally, the step of calculating and generating a comprehensive anomaly index based on the real-time operational data through multi-parameter collaborative analysis includes: Calculate the deviation of each operating parameter in the real-time operating data from its normal operating centerline; The deviation values ​​are weighted and fused to generate an initial anomaly score; To eliminate the influence of different physical dimensions, a weighted fusion method is used to calculate an initial anomaly score, and current, speed, and temperature are dimensionless. The standardized deviation is obtained by dividing the deviation of each parameter by its standard deviation under normal operating conditions. Pre-set weighting coefficients are assigned according to the importance of different parameters in indicating motor burnout faults. These weighting coefficients are dimensionless, and their sum is usually one. The formula for calculating the initial anomaly score is as follows: Sinit = wI × |dI| + wS × |dS| + wT × |dT| Where Sinit is the initial anomaly score. wI, wS, and wT are the preset weights of current, speed, and temperature, respectively, and dI, dS, and dT are the standardized deviations of current, speed, and temperature after dimensionless processing, obtained by dividing each deviation by the standard deviation of the corresponding parameter. The deviation of each parameter can be divided by its standard deviation under normal operating conditions to obtain the standardized deviation. Based on the importance of different parameters in indicating motor burnout faults, preset weight coefficients are assigned to them for weighted fusion. The comprehensive anomaly index is generated by normalizing the relationship between the initial anomaly score and the dynamic security threshold. The normalization process converts the initial anomaly score into a relative value that represents the ratio of the current state's deviation from the normal center to the distance from the safety boundary, thereby generating an intuitive and determinable comprehensive anomaly index.

[0010] Optionally, the step of performing weighted fusion processing on the deviation values ​​to generate an initial anomaly score includes: Furthermore, by standardizing the obtained current deviation, speed deviation, and temperature deviation values, the influence of their respective physical dimensions is eliminated, ensuring the logical consistency of subsequent calculations. At the same time, the corresponding weighting coefficient is determined based on the contribution of each operating parameter to the risk of motor burnout. The risk contribution value of each operating parameter is obtained by multiplying its deviation value by its corresponding weight coefficient. The initial anomaly score is generated by summing all the aforementioned risk contribution values.

[0011] Optionally, the step of comparing the comprehensive anomaly index with the dynamic safety threshold and generating control operations for the permanent magnet synchronous motor based on the comparison result includes: Determine whether the comprehensive anomaly index exceeds the dynamic safety threshold, and obtain the determination result; If the determination result is yes, then an emergency stop signal is generated as the control operation; If the comprehensive abnormal index is determined to have exceeded the dynamic safety threshold, it indicates that the motor's operating state has entered a high-risk area and there is a risk of burnout. In this case, the event is determined to be an emergency fault, and a high-priority emergency stop signal is immediately generated to force a power-off operation, so that the motor stops running quickly due to the loss of electromagnetic torque. If the judgment result is negative, a warning signal is generated as the control operation. Although the comprehensive anomaly index indicates an anomaly, its value does not exceed the dynamic safety threshold. This indicates that the motor status deviates from normal and there is a potential fault, but it has not yet reached a critical level. At this time, an early warning signal will be generated, and the anomaly information containing key information such as current operating data, comprehensive anomaly index and dynamic safety threshold will be packaged and reported to the superior or maintenance personnel terminal for further analysis and decision-making.

[0012] Optionally, the step of generating a warning signal as the control operation if the judgment result is negative includes: The warning level is determined based on the magnitude of the comprehensive anomaly index; Furthermore, by constructing a predefined control strategy matrix, the comprehensive anomaly index can be compared with the matrix to determine the early warning level; Based on the aforementioned warning level, control commands are generated that include reducing motor current or entering standby mode. The warning signal and the control command are sent to the monitoring terminal and the motor driver; Finally, the warning signals and abnormal information, which include the warning level and specific abnormal data, are sent to the central monitoring terminal or the operator's mobile device via industrial bus or wireless communication network, ensuring that relevant personnel can promptly learn about the motor's status and the initial intervention measures that have been taken.

[0013] Optionally, the following steps are also included: Collect the historical control operations and their corresponding motor status feedback to form historical intervention cases; The system mainly collects operational data from the time preceding each warning or emergency, including the comprehensive anomaly index and dynamic safety threshold that trigger the decision, the specific operations performed by the system, and the motor status during the operation. The historical intervention cases are analyzed using machine learning to generate updated parameters. The adaptive and multi-parameter collaborative analysis methods are optimized using the updated parameters.

[0014] Optionally, the step of analyzing the historical intervention cases through machine learning to generate updated parameters includes: The historical intervention cases are categorized into successful intervention cases and failed intervention cases; Extract case feature vectors for each case, including the pattern of changes in running data and the effect of threshold adjustment; The feature vector includes the operational data change pattern, which is quantified by calculating dynamic features such as the first derivative (rate of change) and second derivative (acceleration) of the current, rotational speed and temperature data within the time window; The effect of threshold adjustment is characterized by calculating the relative and absolute differences between the comprehensive anomaly index and the dynamic safety threshold when the event occurs. A prediction model is trained using the feature vectors of the case, and the output of the prediction model is used as the update parameter.

[0015] Optionally, it includes: The data acquisition module is used to acquire real-time operating data of the permanent magnet synchronous motor and establish a real-time database containing historical operating data based on the real-time operating data. The threshold management module is used to generate dynamic safety thresholds based on the historical operating data in the real-time database through adaptive analysis. The analysis module is used to calculate and generate a comprehensive anomaly index based on the real-time operating data through multi-parameter collaborative analysis. The control module is used to compare the comprehensive anomaly index with the dynamic safety threshold, and generate control operations for the permanent magnet synchronous motor based on the comparison result. The feedback module is used to collect historical control operations and their corresponding motor status feedback, and to optimize the threshold management module and the analysis module.

[0016] In a second aspect, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the knowledge-enhanced permanent magnet synchronous motor anti-burnout control system described in the first aspect.

[0017] In one possible design, the electronic device described in the second aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the second aspect and other electronic devices.

[0018] In the embodiments of the present invention, the electronic device described in the second aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0019] Thirdly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, the computer causes the computer to execute the permanent magnet synchronous motor anti-burnout control system described in the first aspect.

[0020] In summary, the above methods and systems have the following technical effects: This invention, through the construction of a data processing system, can generate adaptive safety thresholds for permanent magnet motors based on real-time operating data and historical behavior patterns. This overcomes the limitations of traditional fixed thresholds in the face of complex and variable operating conditions, significantly improves the accuracy of fault identification, and effectively avoids false alarms and missed alarms caused by inappropriate thresholds. Simultaneously, it integrates independent current, speed, and temperature data into a comprehensive index, enabling a holistic assessment of the motor's operating status for real-time monitoring during subsequent operation. This achieves in-depth risk assessment and early warning, with its protective foresight and reliability far exceeding simple multi-parameter independent monitoring. Furthermore, the system can take different control actions, from warning derating to emergency shutdown, depending on the severity of the anomaly, achieving a balance between ensuring safety and maintaining operation. Moreover, the system can learn from the successes and failures of historical interventions, continuously optimizing its internal models and judgment logic, allowing its protective performance to continuously improve with increasing operating time, thus achieving incremental improvement. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the anti-burnout control system for permanent magnet synchronous motors provided in an embodiment of the present invention; Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0024] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0025] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages and sent separately. Furthermore, the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0026] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separate installations, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0027] In the embodiments of this invention, the “protocol” may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to the future permanent magnet synchronous motor anti-burnout control system. The embodiments of this invention do not specifically limit this.

[0028] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0029] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0030] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0031] To facilitate understanding of the embodiments of this application, let's first take... Figure 1 The control system shown in the figure is used as an example to describe in detail the control system applicable to the embodiments of this application.

[0032] Combination Figure 1 The method provided by the embodiments of the present invention is described in detail below. A permanent magnet synchronous motor anti-burnout control system for implementing the method provided by the embodiments of the present invention is described in detail below. The device includes a query terminal, which is configured to: acquire real-time operating data of the permanent magnet synchronous motor, and establish a real-time database containing historical operating data based on the real-time operating data; Based on the historical operational data in the real-time database, dynamic safety thresholds are generated through adaptive analysis; Based on the real-time operational data, a comprehensive anomaly index is generated through multi-parameter collaborative analysis. For example, the current current, speed and temperature data can be integrated into a single comprehensive abnormality index that can comprehensively reflect the health status of the motor. The comprehensive anomaly index is compared with the dynamic safety threshold, and control operations for the permanent magnet synchronous motor are generated based on the comparison results.

[0033] Optionally, the step of acquiring real-time operating data of the permanent magnet synchronous motor and establishing a real-time database containing historical operating data based on the real-time operating data includes: The current, speed, and temperature data of the permanent magnet synchronous motor are collected synchronously and integrated into a set of operating data with timestamps. The current, speed, and temperature data acquired within the same sampling period are synchronously integrated to form a structured set of operating data. This set accurately reflects the comprehensive operating state of the motor at that instant. This set of operating data can be represented as a data tuple D, with the following form: D = (t, I, S, T) Where t represents the timestamp, I represents the collected current data, S represents the rotation speed data, and T represents the temperature data. By marking each set of running data D with a unique timestamp t, the timeliness and traceability of the data are guaranteed. Finally, the running data set D carrying the timestamp is continuously written into a real-time database. The set of operational data is continuously stored in a storage device to form the real-time database; The real-time database contains timestamped current, rotational speed, and temperature data, and the database is gradually improved over time. The historical operation data is obtained by extracting data from the real-time database according to the time series.

[0034] Optionally, the step of generating a dynamic security threshold based on the historical operational data in the real-time database through adaptive analysis includes: Distinguish between normal state data and abnormal state data from the historical operational data; Normal state data refers to data during the healthy and stable operation of the motor, while abnormal state data refers to abnormal state data recorded before a known fault occurs or under abnormal operating conditions. Statistical characteristics of operating parameters are calculated based on the normal state data to obtain dynamic adjustment parameters; The dynamic safety threshold is generated by combining the dynamically adjusted parameters with the basic threshold.

[0035] Optionally, the step of calculating and generating a comprehensive anomaly index based on the real-time operational data through multi-parameter collaborative analysis includes: Calculate the deviation of each operating parameter in the real-time operating data from its normal operating centerline; The deviation values ​​are weighted and fused to generate an initial anomaly score; To eliminate the influence of different physical dimensions, a weighted fusion method is used to calculate an initial anomaly score, and current, speed, and temperature are dimensionless. The standardized deviation is obtained by dividing the deviation of each parameter by its standard deviation under normal operating conditions. Pre-set weighting coefficients are assigned according to the importance of different parameters in indicating motor burnout faults. These weighting coefficients are dimensionless, and their sum is usually one. The formula for calculating the initial anomaly score is as follows: Sinit = wI × |dI| + wS × |dS| + wT × |dT| Where Sinit is the initial anomaly score. wI, wS, and wT are the preset weights of current, speed, and temperature, respectively, and dI, dS, and dT are the standardized deviations of current, speed, and temperature after dimensionless processing, obtained by dividing each deviation by the standard deviation of the corresponding parameter. The deviation of each parameter can be divided by its standard deviation under normal operating conditions to obtain the standardized deviation. Based on the importance of different parameters in indicating motor burnout faults, preset weight coefficients are assigned to them for weighted fusion. The comprehensive anomaly index is generated by normalizing the relationship between the initial anomaly score and the dynamic security threshold. The normalization process converts the initial anomaly score into a relative value that represents the ratio of the current state's deviation from the normal center to the distance from the safety boundary, thereby generating an intuitive and determinable comprehensive anomaly index.

[0036] Optionally, the step of performing weighted fusion processing on the deviation values ​​to generate an initial anomaly score includes: Furthermore, by standardizing the obtained current deviation, speed deviation, and temperature deviation values, the influence of their respective physical dimensions is eliminated, ensuring the logical consistency of subsequent calculations. At the same time, the corresponding weighting coefficient is determined based on the contribution of each operating parameter to the risk of motor burnout. The risk contribution value of each operating parameter is obtained by multiplying its deviation value by its corresponding weight coefficient. The initial anomaly score is generated by summing all the aforementioned risk contribution values.

[0037] Optionally, the step of comparing the comprehensive anomaly index with the dynamic safety threshold and generating control operations for the permanent magnet synchronous motor based on the comparison result includes: Determine whether the comprehensive anomaly index exceeds the dynamic safety threshold, and obtain the determination result; If the determination result is yes, then an emergency stop signal is generated as the control operation; If the comprehensive abnormal index is determined to have exceeded the dynamic safety threshold, it indicates that the motor's operating state has entered a high-risk area and there is a risk of burnout. In this case, the event is determined to be an emergency fault, and a high-priority emergency stop signal is immediately generated to force a power-off operation, so that the motor stops running quickly due to the loss of electromagnetic torque. If the judgment result is negative, a warning signal is generated as the control operation. Although the comprehensive anomaly index indicates an anomaly, its value does not exceed the dynamic safety threshold. This indicates that the motor status deviates from normal and there is a potential fault, but it has not yet reached a critical level. At this time, an early warning signal will be generated, and the anomaly information containing key information such as current operating data, comprehensive anomaly index and dynamic safety threshold will be packaged and reported to the superior or maintenance personnel terminal for further analysis and decision-making.

[0038] Optionally, the step of generating a warning signal as the control operation if the judgment result is negative includes: The warning level is determined based on the magnitude of the comprehensive anomaly index; Furthermore, by constructing a predefined control strategy matrix, the comprehensive anomaly index can be compared with the matrix to determine the early warning level; Based on the aforementioned warning level, control commands are generated that include reducing motor current or entering standby mode. The warning signal and the control command are sent to the monitoring terminal and the motor driver; Finally, the warning signals and abnormal information, which include the warning level and specific abnormal data, are sent to the central monitoring terminal or the operator's mobile device via industrial bus or wireless communication network, ensuring that relevant personnel can promptly learn about the motor's status and the initial intervention measures that have been taken.

[0039] Optionally, the following steps are also included: Collect the historical control operations and their corresponding motor status feedback to form historical intervention cases; The system mainly collects operational data from the time preceding each warning or emergency, including the comprehensive anomaly index and dynamic safety threshold that trigger the decision, the specific operations performed by the system, and the motor status during the operation. The historical intervention cases are analyzed using machine learning to generate updated parameters. The adaptive and multi-parameter collaborative analysis methods are optimized using the updated parameters.

[0040] Optionally, the step of analyzing the historical intervention cases through machine learning to generate updated parameters includes: The historical intervention cases are categorized into successful intervention cases and failed intervention cases; Extract case feature vectors for each case, including the pattern of changes in running data and the effect of threshold adjustment; The feature vector includes the operational data change pattern, which is quantified by calculating dynamic features such as the first derivative (rate of change) and second derivative (acceleration) of the current, rotational speed and temperature data within the time window; The effect of threshold adjustment is characterized by calculating the relative and absolute differences between the comprehensive anomaly index and the dynamic safety threshold when the event occurs. A prediction model is trained using the feature vectors of the case, and the output of the prediction model is used as the update parameter.

[0041] Optionally, it includes: a data acquisition module, used to acquire real-time operating data of the permanent magnet synchronous motor, and to establish a real-time database containing historical operating data based on the real-time operating data; The threshold management module is used to generate dynamic safety thresholds based on the historical operating data in the real-time database through adaptive analysis. The analysis module is used to calculate and generate a comprehensive anomaly index based on the real-time operating data through multi-parameter collaborative analysis. The control module is used to compare the comprehensive anomaly index with the dynamic safety threshold, and generate control operations for the permanent magnet synchronous motor based on the comparison result. The feedback module is used to collect historical control operations and their corresponding motor status feedback, and to optimize the threshold management module and the analysis module.

[0042] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0043] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0044] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0045] The units described as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0046] In addition, the functional units in the various embodiments of the present invention can be integrated into the processing unit, or each unit can exist physically separately, or two or more units can be integrated into the unit.

[0047] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A permanent magnet synchronous motor burn prevention control system, characterized in that, The system comprises: acquiring real-time operation data of a permanent magnet synchronous motor, and establishing a real-time database containing historical operation data based on the real-time operation data; generating a dynamic safety threshold through adaptive analysis based on the historical operation data in the real-time database; calculating a comprehensive abnormality index through multi-parameter collaborative analysis based on the real-time operation data; for example, fusing the current, speed and temperature data, and fusing them into a single comprehensive abnormality index that can comprehensively reflect the health status of the motor; comparing the comprehensive abnormality index with the dynamic safety threshold, and generating a control operation for the permanent magnet synchronous motor based on the comparison result.

2. The permanent magnet synchronous motor burn prevention control system of claim 1, wherein, The step of acquiring real-time operation data of a permanent magnet synchronous motor, and establishing a real-time database containing historical operation data based on the real-time operation data comprises: synchronously collecting current data, speed data and temperature data of the permanent magnet synchronous motor, and integrating them into a set of operation data carrying a time stamp; synchronously integrating the current data, speed data and temperature data obtained in the same sampling period to form a structured operation data set that accurately reflects the comprehensive operation state of the motor at that moment, which can be expressed as a data tuple D in the following form: D = (t, I, S, T) where t represents the time stamp, I represents the collected current data, S represents the speed data, and T represents the temperature data. By marking each operation data set D with a unique time stamp t, the time sequence and traceability of the data are ensured. Finally, the operation data set D carrying the time stamp is continuously written into a real-time database; continuously storing the operation data set into a storage device to form the real-time database; The real-time database contains current data, speed data and temperature data with time stamps, and the database is gradually improved over time; extracting data in time sequence from the real-time database to obtain the historical operation data.

3. The permanent magnet synchronous motor burn prevention control system of claim 1, wherein, The step of generating a dynamic safety threshold through adaptive analysis based on the historical operation data in the real-time database comprises: distinguishing normal state data from abnormal state data from the historical operation data; normal state data is data during stable operation of the motor, while abnormal state data is abnormal state data recorded before known faults or abnormal working conditions; calculating statistical characteristics of operation parameters based on the normal state data to obtain dynamic adjustment parameters; operating the dynamic adjustment parameters and the basic threshold to generate the dynamic safety threshold.

4. The permanent magnet synchronous motor burn prevention control system of claim 1, wherein, The step of calculating a comprehensive abnormality index through multi-parameter collaborative analysis based on the real-time operation data comprises: calculating the deviation value of each operation parameter in the real-time operation data relative to its normal operation center line; performing weighted fusion processing on the deviation value to generate an initial abnormality score; To eliminate the influence of different physical dimensions, a weighted fusion method is used to calculate an initial abnormality score, and the current, speed and temperature are dimensionless. The deviation value of each parameter can be divided by its standard deviation under normal working conditions to obtain the standardized deviation. According to the importance of different parameters to the indication of motor burnout failure, preset weight coefficients are assigned, which are dimensionless and the sum is usually one. The calculation formula of the initial abnormality score is as follows: S init = w I × |d I | + w S × |d S | + w T × |d T | wherein S init is the initial abnormality score. w I , w S , w T are preset weights of the current, the rotational speed, and the temperature, respectively, d I , d S , d T are standardized deviation values of the current, the rotational speed, and the temperature after being subjected to a dimensionless process, and are obtained by dividing each of the deviation values by a standard deviation of the corresponding parameter; According to the relationship between the initial abnormality score and the dynamic safety threshold, the normalization process is carried out to generate the comprehensive abnormality index; The normalization process converts the initial abnormality score into a relative value, which represents the ratio of the degree of deviation from the normal center to the distance from the safety boundary, thereby generating a comprehensive abnormality index that is intuitive and determinable.

5. The permanent magnet synchronous motor burn prevention control system of claim 4, wherein, The step of performing weighted fusion processing on the deviation value to generate an initial abnormality score includes: And by standardizing the obtained current deviation value, speed deviation value and temperature deviation value, the influence of their respective physical dimensions is eliminated, ensuring the logical consistency of subsequent calculations; At the same time, according to the contribution of each operating parameter to the risk of motor burnout, the corresponding weight coefficient is determined; Multiply the deviation value of each operating parameter by its corresponding weight coefficient to obtain its respective risk contribution value; Sum all the risk contribution values to generate the initial abnormality score.

6. The permanent magnet synchronous motor burn prevention control system of claim 1, wherein, The step of comparing the comprehensive abnormality index with the dynamic safety threshold and generating a control operation for the permanent magnet synchronous motor based on the comparison result includes: Determine whether the comprehensive abnormality index exceeds the dynamic safety threshold to obtain a determination result; If the determination result is yes, generate an emergency stop signal as the control operation; If the comprehensive abnormality index has exceeded the dynamic safety threshold, it indicates that the operating state of the motor has entered a high-risk area with the risk of burning out. In this case, the system determines the event as an emergency failure and immediately generates a high-priority emergency stop signal to force the power-off operation, causing the motor to stop running rapidly due to the loss of electromagnetic torque; If the determination result is no, generate a warning signal as the control operation; Although the comprehensive abnormality index indicates an abnormality, its value does not exceed the dynamic safety threshold, indicating that the motor state deviates from normal and there is a potential failure, but it has not reached a critical level. At this time, the system generates a warning signal and packages the abnormal information containing the current operating data, comprehensive abnormality index and dynamic safety threshold, etc. Key information is reported to the superior monitoring system or operation and maintenance personnel terminal for further analysis and decision-making.

7. The permanent magnet synchronous motor burn prevention control system of claim 6, wherein, The step of generating a warning signal as the control operation if the determination result is no includes: Determine the warning level according to the numerical value of the comprehensive abnormality index; And by building a pre-defined control strategy matrix, the comprehensive abnormality index is compared with the matrix to determine the warning level; Based on the warning level, generate a control instruction containing reducing the motor current or entering standby mode; Send the warning signal and the control instruction to the monitoring terminal and the motor driver; Finally, the early warning signal containing the early warning level and specific abnormal data and the abnormal information are sent to the central monitoring terminal or the mobile device of the operator through the industrial bus or the wireless communication network, so that the relevant personnel can know the state of the motor and the preliminary intervention measures taken in time.

8. The permanent magnet synchronous motor burn prevention control system of claim 1, wherein, Further comprising the following steps: collecting historical control operations and corresponding motor state feedback to form historical intervention cases; mainly collecting the operation data before each early warning or emergency, the comprehensive abnormal index triggering the decision and the dynamic safety threshold, the specific operation performed by the system, and the motor state when the operation is performed; analyzing the historical intervention cases through machine learning to generate updated parameters; optimizing the adaptive and multi-parameter collaborative analysis method using the updated parameters.

9. The permanent magnet synchronous motor burn prevention control system of claim 8, wherein, The step of analyzing the historical intervention cases through machine learning to generate updated parameters comprises: classifying the historical intervention cases into successful intervention cases and failed intervention cases; extracting case feature vectors containing operation data change patterns and threshold adjustment effects for the cases; the feature vector includes the operation data change pattern, which is quantified by calculating the first derivative (change rate) and the second derivative (acceleration) of the current, speed and temperature data in the time window; and the threshold adjustment effect, which is characterized by calculating the relative gap and absolute gap between the comprehensive abnormal index and the dynamic safety threshold when the event occurs; training a prediction model using the case feature vectors, and using the output of the prediction model as the updated parameters.

10. A method for preventing burn-out of a PMSM, applied to the PMSM burn-out prevention control system according to any one of claims 1-9, characterized in that, It includes: a data acquisition module for acquiring real-time operation data of a permanent magnet synchronous motor and establishing a real-time database containing historical operation data based on the real-time operation data; a threshold management module for generating a dynamic safety threshold through adaptive analysis based on the historical operation data in the real-time database; an analysis module for calculating and generating a comprehensive abnormal index through multi-parameter collaborative analysis according to the real-time operation data; a control module for comparing the comprehensive abnormal index with the dynamic safety threshold and generating control operations for the permanent magnet synchronous motor based on the comparison result; a feedback module for collecting historical control operations and corresponding motor state feedback, and optimizing the threshold management module and the analysis module.

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