Motor operation abnormity monitoring method and system for electro-tricycle

Abstract

The invention discloses a motor operation abnormity monitoring method and system for an electric tricycle, and relates to the technical field of electric vehicles, and the method comprises the steps: arranging an action instruction analysis module between a starting device and a motor, the module comprises a plurality of monitoring analysis units, and each unit corresponds to a group of action instructions; the action instruction of the electro-tricycle is acquired in real time, the corresponding monitoring unit is activated to extract the real-time action parameter, and the load state is predicted. State data of a motor and associated components are obtained through a multi-source sensing device, abnormal risk assessment is carried out by combining load state prediction and the state data, and motor operation parameters are dynamically adjusted according to an assessment result. The technical problem that in the prior art, the mutual influence between the motor and other assemblies is neglected, so that the abnormity of the motor of the electro-tricycle under the complex load change cannot be accurately predicted and monitored is solved, and the technical effects of accurate monitoring and early warning of the abnormity of the motor of the electro-tricycle are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method and system for monitoring abnormal operation of a motor for an electric tricycle. Background Art

[0002] With the popularity of electric tricycles, its motor system as a core component directly affects the performance and safety of the vehicle. Electric tricycles are usually faced with frequent load changes, especially in urban traffic. Frequent starting and stopping, acceleration and deceleration can lead to problems such as motor overload and overtemperature, and even damage the motor. However, the existing motor monitoring system often ignores the mutual influence between the motor and other related components (such as batteries, controllers, braking systems, etc.), and it is difficult to accurately identify the abnormal conditions of the motor under complex load changes, and it is impossible to make real-time and accurate predictions of the motor. The integrated design of electric tricycles makes motor anomalies often closely related to other systems. The existing technology cannot fully consider these factors, resulting in poor anomaly detection effect. Therefore, there is an urgent need for a monitoring method that can monitor the motor load status in real time and conduct a comprehensive evaluation in combination with the status of related components to improve the safety and reliability of electric tricycle motors.

[0003] At present, relevant technologies have neglected the mutual influence between the motor and other components, resulting in the inability to accurately predict and monitor the abnormalities of the electric tricycle motor under complex load changes. Summary of the invention

[0004] The present application solves the technical problem in the prior art of neglecting the mutual influence between the motor and other components, resulting in the inability to accurately predict and monitor the abnormal operation of the electric tricycle motor under complex load changes, by providing a method and system for monitoring the abnormal operation of the motor of an electric tricycle.

[0005] The present application provides a method for monitoring abnormal operation of a motor of an electric tricycle, comprising: An action instruction analysis module is established between the starting device and the motor, wherein the action instruction analysis module includes a plurality of monitoring and analysis units, and each monitoring and analysis unit corresponds to a group of action instructions; the real-time action instructions of the electric tricycle are obtained, and according to the real-time action instructions, the corresponding target monitoring and analysis unit is activated to extract associated parameters to obtain real-time action parameters; based on the real-time action parameters, the load state is predicted by the target monitoring and analysis unit to obtain a motor load state prediction sequence; multi-source motor state data and associated component state data are obtained through multi-source sensing equipment; motor abnormality risk assessment is performed in combination with the motor load state prediction sequence, the multi-source motor state data and associated component state data, and the motor operation parameters are dynamically adjusted according to the risk assessment results.

[0006] The present application also provides a motor abnormal operation monitoring system for an electric tricycle, comprising: An action instruction analysis module establishment module, the action instruction analysis module establishment module is used to establish an action instruction analysis module between the starting device and the motor, the action instruction analysis module includes multiple monitoring and analysis units, each monitoring and analysis unit corresponds to a group of action instructions; an associated parameter extraction module, the associated parameter extraction module is used to obtain the real-time action instructions of the electric tricycle, and according to the real-time action instructions, activate the corresponding target monitoring and analysis unit to extract associated parameters to obtain real-time action parameters; a load state prediction module, the load state prediction module is used to predict the load state through the target monitoring and analysis unit based on the real-time action parameters to obtain a motor load state prediction sequence; a state data acquisition module, the state data acquisition module is used to obtain multi-source motor state data and associated component state data through multi-source sensing equipment; a motor abnormality risk assessment module, the motor abnormality risk assessment module is used to combine the motor load state prediction sequence, the multi-source motor state data and the associated component state data to perform motor abnormality risk assessment, and dynamically adjust the motor operating parameters according to the risk assessment results.

[0007] The method and system for monitoring abnormal operation of the motor for an electric tricycle proposed in this application firstly sets an action instruction analysis module between the starting device and the motor, and the module includes multiple monitoring and analysis units, each unit corresponding to a set of action instructions. By acquiring the action instructions of the electric tricycle in real time, the corresponding monitoring unit is activated to extract the real-time action parameters, and the load state prediction is performed. The state data of the motor and related components are obtained through multi-source sensing equipment, and the abnormal risk assessment is performed in combination with the load state prediction and state data. The motor operation parameters are dynamically adjusted according to the evaluation results, so as to achieve the technical effect of accurate monitoring and early warning of abnormal motors of electric tricycles under complex load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0009] Figure 1 A schematic flow chart of a method for monitoring abnormal motor operation of an electric tricycle provided in an embodiment of the present application.

[0010] Figure 2A schematic diagram of the structure of a motor operation abnormality monitoring system for an electric tricycle provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: action instruction analysis module establishment module 10 , associated parameter extraction module 20 , load state prediction module 30 , state data acquisition module 40 , motor abnormality risk assessment module 50 . DETAILED DESCRIPTION

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0013] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0014] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0015] The present application provides a method for monitoring abnormal operation of a motor of an electric tricycle, such as Figure 1 As shown, the method includes: Step S100, establish an action instruction analysis module between the starting device and the motor, the action instruction analysis module includes multiple monitoring and analysis units, and each monitoring and analysis unit corresponds to a set of action instructions. Specifically, an action instruction analysis module is constructed between the starting device and the motor of the electric tricycle, and the module is composed of multiple monitoring and analysis units. Corresponding units are established for common action instructions such as acceleration, braking, and steering, and the influence of each instruction on the motor is analyzed, such as the changes in motor current, voltage, and heat during acceleration, braking feedback, speed reduction rate, and internal magnetic field current changes during braking, wheel speed difference and torque distribution during steering, etc., actual usage data is collected to count the daily usage frequency of different instructions, and the weight of the monitoring and analysis unit is increased for high-frequency acceleration, braking, and other action instructions, and the model is trained with more relevant scene data to accurately identify the impact and find anomalies, and the integrity and timeliness of data are prioritized during data processing, storage, and transmission, so as to improve the safety and stability of the motor operation system.

[0016] In a possible implementation, an action instruction analysis module is established between the starting device and the motor, and the action instruction analysis module includes multiple monitoring and analysis units, each monitoring and analysis unit corresponds to a set of action instructions. Step S100 further includes step S110, according to the basic information of the target electric tricycle, a plurality of benchmark action instructions are obtained, and the benchmark action instructions may be a single action instruction or a combined action instruction. Specifically, the basic information of the target electric tricycle is analyzed, and the basic information covers the vehicle model, purpose, design parameters (such as motor power, battery capacity, vehicle weight, etc.) and the characteristics of the adapted control system. For example, if the target electric tricycle is a vehicle for heavy-load cargo transportation, its motor power is large, then when obtaining the benchmark action instruction, the action instructions related to heavy-load transportation are analyzed, such as frequent acceleration and start-up, slow and stable braking, etc. Through the comprehensive analysis of the basic information, multiple benchmark action instructions are accurately determined, and the instructions may be simple single action instructions such as acceleration, braking, steering, etc., or may be combined action instructions such as acceleration and steering at the same time, so as to comprehensively cover various operation situations that may occur in the actual operation of the vehicle.

[0017] Step S120, based on the multiple benchmark action instructions, extract key monitoring indicators, and establish multiple monitoring and analysis units respectively. Specifically, based on the multiple benchmark action instructions obtained, start to extract key monitoring indicators. For acceleration instructions, key monitoring indicators include the current rise rate of the motor, the voltage fluctuation amplitude, and the acceleration value of the rotor, because the indicators can intuitively reflect the power output characteristics and operating stability of the motor during the acceleration process; for braking instructions, the stroke depth of the brake pedal, the pressure change of the brake system, the back electromotive force of the motor, and the deceleration of the wheel become key monitoring indicators, which are helpful to judge the braking force, effect, and feedback impact on the motor. In terms of steering instructions, the steering wheel angle, steering speed, the speed difference between the left and right wheels, and the torque distribution ratio of the motor on the wheels on both sides are important monitoring indicators. The indicators directly affect the flexibility and accuracy of the vehicle steering and the ability of the motor to work together during the steering process. According to the extracted key monitoring indicators, multiple monitoring and analysis units are established respectively, and each unit focuses on the collection, analysis and processing of data related to a specific benchmark action instruction, thereby laying a solid foundation for the subsequent precise monitoring of the motor's operating status under different actions.

[0018] Step S130, based on the daily operation records of the target user, user behavior analysis is performed to generate a personalized correction plan. Specifically, the daily operation records of the target user are collected. The records can be obtained through the vehicle's built-in sensors and data recording system, and contain all the user's operation behavior data on the electric tricycle over a period of time (such as one month or longer), such as the action instructions, operation time, operation frequency of each operation, and vehicle driving environment information during operation (such as road slope, road conditions, etc.). User behavior analysis is performed on the daily operation records, and the user's operation behavior is classified according to similarity using a cluster analysis algorithm to find out the user's typical operation mode in different driving scenarios (such as urban roads, rural roads, slope sections, etc.); the time series analysis method is used to study the changing rules of the user's operation frequency, and determine the user's operation peak and trough periods within a day or a week; the association relationship between different action instructions is analyzed through association rule mining technology, such as whether there are certain specific action combinations that frequently appear in user operations. Based on the results of the user behavior analysis, a personalized correction plan is generated. If the analysis finds that the user frequently performs sudden acceleration and sudden braking operations in congested urban roads, then in the personalized correction plan, special parameter adjustments will be made to the monitoring and analysis units corresponding to acceleration and braking. For example, the sensitivity threshold of the acceleration command monitoring and analysis unit to the rapid rise of current is reduced, because in such frequent sudden acceleration, the rapid rise of current may be part of normal operation rather than an abnormal situation; at the same time, the monitoring frequency of the brake command monitoring and analysis unit to the pressure change of the brake system is increased, so as to capture abnormal braking conditions more promptly, such as pressure leakage of the brake system. In addition, if it is found that the user often uses a specific action combination (such as accelerating and turning at the same time) on a specific road section (such as a slope section), the relevant combined action command monitoring and analysis unit will be optimized in the personalized correction plan, such as increasing the weight of the monitoring indicator for the rationality of the torque distribution of the motor under the combined action, etc., so as to better adapt to the actual operating habits and needs of the target users.

[0019] Step S140, using the personalized correction scheme, the multiple monitoring and analysis units are initialized and configured, and the action instruction analysis module is connected and generated. Specifically, the generated personalized correction scheme is used to initialize and configure the multiple monitoring and analysis units established previously. The various parameter settings in the correction scheme (such as the threshold of the monitoring index, the data acquisition frequency, the weight of the data processing algorithm, etc.) are applied one by one to the corresponding monitoring and analysis units, so that each unit can work according to the personalized requirements. Then, the monitoring and analysis units that have been initialized and configured are connected and integrated to form a complete action instruction analysis module. During the connection process, it is ensured that the data transmission channels between the units are unobstructed, and real-time sharing and interaction of data can be achieved. For example, when the acceleration instruction monitoring and analysis unit detects an abnormal current change, the relevant data can be transmitted to other associated monitoring and analysis units (such as the braking instruction monitoring and analysis unit and the steering instruction monitoring and analysis unit) in a timely manner, so as to collaboratively determine whether the abnormal situation will affect the execution of other action instructions, thereby realizing comprehensive, accurate and personalized monitoring and analysis of the running state of the electric tricycle motor, effectively improving the safety and reliability of vehicle operation, and also better adapting to the driving style and use requirements of different users.

[0020] In a possible implementation, user behavior analysis is performed based on the daily operation records of the target user to generate a personalized correction plan. Step S130 further includes step S131, which extracts multiple sample operation records of multiple action instructions based on the daily operation records of the target user. Specifically, the daily operation record data of the target user is analyzed, and the records record in detail every operation detail of the electric tricycle during actual use, including the time when the operation occurred, the specific action instructions of the operation, and other information. For different action instructions, such as acceleration, braking, steering, etc., multiple sample operation records are extracted from them. For example, for acceleration action instructions, all operation events related to acceleration are screened out from the daily operation records and organized into a sample operation record set of acceleration actions. The sample operation records cover acceleration operations in different scenarios, different road conditions, and different driving needs, providing a data basis for subsequent in-depth analysis.

[0021] Step S132, multivariate clustering is performed on the multiple sample operation records to obtain multivariate action features of the multiple action instructions, and the multivariate action features include action type, action duration, and action frequency. Specifically, the extracted multiple sample operation records are processed using a multivariate clustering algorithm. Multivariate clustering is a method for classifying and aggregating data based on features of multiple dimensions. In this process, action type, action duration, action frequency, etc. are used as key dimensions for clustering. For example, all acceleration action sample operation records are preliminarily divided according to the length of action duration, and those with similar durations are classified into one category; and then further subdivided according to the frequency of action, the acceleration action sample operation records with similar frequency of occurrence in a specific time period are aggregated together. Through the multivariate clustering process, the multivariate action features of each action instruction are obtained. Taking the braking action as an example, after clustering, multivariate action feature groups such as sudden braking (action type), short duration (action duration), low frequency (action frequency) can be determined, as well as other feature groups such as slow braking, long duration, and high frequency. The feature group comprehensively describes the manifestations of different types of braking actions in users' daily operations.

[0022] Step S133, the contribution of the multivariate action feature is analyzed, and according to the result of the contribution analysis, the feature weight is configured for the multivariate action feature. Specifically, the contribution analysis is performed on the obtained multivariate action feature. The analysis aims to determine the degree of influence of each feature on the motor operation state and the overall vehicle performance. For example, for the acceleration action, the action type (such as gentle acceleration or rapid acceleration) has a greater impact on the load change of the motor, and its contribution is relatively high; if the frequency of the action is too high, the motor will be frequently in a high-load switching state, which has an important impact on the heat dissipation and durability of the motor, and its contribution cannot be ignored; the duration of the action is closely related to the energy consumption and heat generation of the motor during an acceleration process. According to the result of the contribution analysis, the corresponding feature weight is configured for each multivariate action feature. For example, the feature weight of the rapid acceleration action type is determined to be 0.4, the feature weight of the high frequency is 0.3, and the feature weight of the long duration is 0.3. The weight reflects the relative importance of each feature in evaluating the impact of the acceleration action on the motor.

[0023] Step S134, based on the multivariate action features and the corresponding feature weights, the personalized impact analysis is performed on the multiple action instructions, and the personalized correction scheme is generated according to the impact analysis results, and the personalized correction scheme includes the correction strategy for the monitoring and analysis unit corresponding to each action instruction. Specifically, based on the multivariate action features and the corresponding feature weights, a personalized impact analysis is performed on multiple action instructions. Taking the steering action instruction as an example, if its multivariate action features are large-angle steering (action type), short duration, and high frequency, and the corresponding feature weights are 0.5, 0.2, and 0.3 respectively, the comprehensive impact value of the steering action instruction in the user's daily operation can be calculated based on the information. Through analysis, the specific impact of each action instruction on the motor operation under the user's personalized operating habits is fully understood. Then, a personalized correction scheme is generated according to the impact analysis results. For the above-mentioned steering action instructions, if analysis finds that its high-frequency large-angle steering will cause a greater risk of wear on the motor shaft system, then in the personalized correction plan, the monitoring and analysis unit corresponding to the steering instruction will increase the sensitivity of the motor shaft system torque and temperature monitoring, and adjust the data collection frequency so that it can detect potential abnormalities more promptly; at the same time, optimize the data processing algorithm in the monitoring and analysis unit, and conduct a more in-depth analysis of the data related to large-angle steering, so as to provide early warning of possible faults, thereby ensuring that the electric tricycle can still maintain a good motor operating state and vehicle performance under the user's personalized driving style, thereby improving the safety and reliability of the vehicle.

[0024] Step S200, obtain the real-time action instruction of the electric tricycle, and according to the real-time action instruction, activate the corresponding target monitoring and analysis unit to extract the associated parameters and obtain the real-time action parameters. Specifically, in the control system of the electric tricycle, an instruction acquisition module is set to obtain the action instruction in real time. The module is closely connected with the operating component and can convert the driver's operation into a digital instruction signal, such as receiving the acceleration instruction signal when stepping on the accelerator pedal. After obtaining the instruction, the corresponding monitoring and analysis unit is activated according to the preset corresponding relationship, such as activating its corresponding unit when receiving the acceleration instruction. Then the unit obtains data from multiple sources such as motor current, voltage sensor, speed sensor and load sensor, extracts the associated parameters and integrates the data after data cleaning, conversion and fusion, and finally obtains the real-time action parameters that can fully and accurately reflect the actual operating state of the motor when executing the real-time action instruction, providing a key data basis for subsequent work and ensuring the safe and stable operation of the vehicle.

[0025] Step S300, based on the real-time action parameters, the load state is predicted by the target monitoring and analysis unit to obtain a motor load state prediction sequence. Specifically, the target monitoring and analysis unit is embedded with a load state prediction model constructed based on a large number of real-time action parameters and motor load state information under different road conditions, loads, and driving styles. After obtaining the real-time action parameters, the model is input into the model. The model first parses and extracts key features closely related to the load, such as the current change rate during acceleration, the speed acceleration, and the load. Then, the built-in algorithm is used to dynamically predict the load state changes of the motor in the future based on the current parameters and similar historical situations. By continuously predicting multiple time nodes, a motor load state prediction sequence containing information such as load size and change trend is obtained, which provides a forward-looking basis for adjusting motor parameters in advance, optimizing vehicle performance, and preventing abnormalities, and ensures stable operation of the motor and safe driving of the vehicle.

[0026] In a possible implementation, based on the real-time action parameters, the load state prediction is performed by the target monitoring and analysis unit to obtain a motor load state prediction sequence, and step S300 further includes step S310, wherein the target monitoring and analysis unit is embedded with a load state prediction model, and the load state prediction model contains a user habit index for real-time action instructions. Specifically, the load state prediction model embedded in the target monitoring and analysis unit. In addition to the conventional prediction algorithm based on physical principles and a large amount of experimental data, the load state prediction model also integrates the user habit index for real-time action instructions. The user habit index is obtained by deeply analyzing the operation records of the target user over a period of time (for example, the past month). For example, for acceleration action instructions, if the user often uses the method of quickly and violently stepping on the accelerator pedal, the acceleration action instruction user habit index in the model will reflect the characteristics of the aggressive operation style; on the contrary, if the user always steps on the accelerator pedal slowly and steadily, the index will reflect this more conservative driving habit.

[0027] Step S320, according to the user habit index, analyze and obtain the duration of the real-time action instruction. Specifically, the duration of the real-time action instruction is analyzed and obtained according to the user habit index embedded in the model. Taking the braking action instruction as an example, if the user habit index shows that the user tends to slow down when braking, based on the index and some initial parameters at the beginning of the current braking action (such as the initial stepping force of the brake pedal, the initial speed of the vehicle, etc.), the model calculates the duration of the braking action instruction through pre-established associations and algorithms. Because the user's habits will largely determine the time characteristics of their operating actions, for example, users who are accustomed to slow deceleration will usually take a long time to completely stop the vehicle from the beginning of their braking action, while users who are accustomed to sudden braking will have a relatively short duration of braking action.

[0028] Step S330, taking the real-time action parameters and the duration as input, and dynamically predicting the load state through the load state prediction model, and obtaining the motor load state prediction sequence. Specifically, the real-time action parameters and the derived duration are taken as input and input into the load state prediction model to perform dynamic prediction of the load state. For example, after receiving the real-time action parameters of the acceleration action (such as the real-time current, voltage, speed, etc. of the motor) and the possible duration of the acceleration action obtained by analyzing the user habit index, the model will conduct a comprehensive analysis. Based on similar acceleration scene data in the past (these data have been classified and marked according to user habits), the model will predict the changes in the motor load during the entire acceleration process. For example, in the early stage of acceleration, the motor load will increase rapidly with the increase in speed, but because the user is used to steady acceleration, the rate of increase in load will be relatively mild; as the acceleration continues, the load will gradually reach a peak value, and then stabilize as the vehicle speed approaches the target speed. By performing such prediction calculations on multiple future time nodes, a motor load state prediction sequence is finally obtained. The sequence shows in detail the estimated load status of the motor at different moments in the future, providing a critical basis for subsequent motor operation control, fault prevention, etc., and can effectively ensure that the electric tricycle motor operates stably and efficiently under the premise of meeting the user's driving habits.

[0029] Step S400, obtain multi-source motor status data and associated component status data through multi-source sensing equipment. Specifically, according to the structure of the electric tricycle and the distribution of the motor and its associated components, the multi-source sensing equipment is reasonably deployed, and current, voltage, and temperature sensors are installed on the motor to collect the current, voltage, and temperature data during its operation. The data can reflect the motor load, power supply stability, and heat dissipation, and are integrated to form multi-source motor status data; temperature sensors are installed near components adjacent to the motor and that may interfere with the motor, such as the controller, and vibration sensors are installed on the transmission chain or transmission shaft to collect their temperature and vibration data. When the controller is hot or the transmission components vibrate abnormally, it will affect the motor's operating temperature and stability. Integrating the status data of the associated components and combining it with the motor status data can provide strong support for motor abnormality risk assessment and fault prevention, and ensure the stable and reliable operation of the motor and power system.

[0030] In a possible implementation, multi-source motor status data and associated component status data are obtained through multi-source sensing equipment, and step S400 further includes step S410, obtaining the structural layout information and braking principle data of the electric tricycle. Specifically, by consulting the design drawings and technical manuals of the electric tricycle and communicating with the vehicle manufacturer, the structural layout information of the electric tricycle is fully obtained, including the specific position of the motor in the vehicle, the layout of the transmission components (such as the transmission shaft, chain, etc.) connected to the motor, the installation position of the battery pack, and the distribution of various controllers. At the same time, the braking principle data of the electric tricycle is studied in depth to understand the type of braking system (such as mechanical braking, hydraulic braking or electric braking) used, the force transmission path during braking, the working mode of the brake pad and the brake disc, and the coordinated control logic between the braking system and the motor. For example, for an electric tricycle that uses hydraulic braking and has an energy recovery function, it is necessary to clarify how the motor switches to the power generation mode during braking, and how the hydraulic system cooperates with the regenerative braking of the motor to achieve a smooth deceleration of the vehicle.

[0031] Step S420, based on the structural layout information and the braking principle data, the motor is subjected to correlation analysis of associated components, and the braking associated components and the interference associated components are extracted according to the correlation analysis results. Specifically, based on the acquired structural layout information and the braking principle data, the motor is subjected to correlation analysis of associated components. From the perspective of structural layout, the reducer directly connected to the motor through the transmission shaft can be regarded as a braking associated component, because when the vehicle brakes, the reverse resistance of the motor will be transmitted through the reducer, and the gear ratio and other parameters of the reducer will also affect the load characteristics of the motor during the braking process. From the perspective of the braking principle, the brake controller is also an important braking associated component, which is responsible for coordinating the degree of intervention of the regenerative braking and mechanical braking of the motor according to the driver's braking operation instructions. For the interference associated components, considering the spatial position factors, such as the electronic control unit installed near the motor, since it generates heat during operation and is close to the motor, its temperature rise will interfere with the heat dissipation environment of the motor, thereby affecting the operating temperature of the motor; for example, some auxiliary equipment that shares a mounting bracket with the motor may transmit its vibration during operation to the motor, affecting the stability of the motor and the stress of the shaft system. Through analysis, the braking-related components and interference-related components are accurately extracted.

[0032] Step S430, deploy multi-source sensing equipment, obtain the state data of the braking associated components and the interference associated components, and form the associated component state data. Specifically, according to the extracted braking associated components and interference associated components, the multi-source sensing equipment is reasonably deployed. For the braking associated components, a torque sensor is installed on the input shaft and output shaft of the reducer to monitor the change of torque during braking, so as to understand the load change of the motor; a signal monitoring sensor is installed on the brake controller to obtain information such as the strength and frequency of the braking control signal in real time. For the interference associated components, a temperature sensor is installed on the housing of the electronic control unit close to the motor to accurately measure its operating temperature; a vibration sensor is installed on the auxiliary equipment bracket that may transmit vibration to the motor to monitor its vibration amplitude and frequency. Through the deployed multi-source sensing equipment, the state data of the braking associated components and the interference associated components are continuously collected. The data from the torque sensor, signal monitoring sensor, temperature sensor and vibration sensor are integrated to form the associated component state data. The data can comprehensively reflect the working status of components associated with the motor during the operation of the electric tricycle, providing a rich and critical data basis for the subsequent evaluation of the motor's operating environment, predicting possible abnormal conditions of the motor, and formulating corresponding control strategies, which helps to ensure the stable and efficient operation of the electric tricycle motor and the safety performance of the entire vehicle.

[0033] Step S500, combining the motor load state prediction sequence, the multi-source motor state data and the associated component state data, conduct motor abnormality risk assessment, and dynamically adjust the motor operating parameters according to the risk assessment results. Specifically, first summarize the motor load state prediction sequence, the multi-source motor state data and the associated component state data, analyze the impact of the predicted load state and the associated components on the motor and generate the influence coefficient, and build a motor abnormality risk assessment model based on this, and incorporate the data and motor characteristics, failure cases and experience-determined weights into the model to calculate the evaluation value, and divide it into low, medium and high risk states. When the risk is low, fine-tune the input voltage and optimize the control algorithm to improve efficiency and reduce energy consumption. When the risk is medium, the power is reduced according to the associated component state, such as the transmission component vibrates greatly, and the heat dissipation is increased according to the motor temperature. When the risk is high, the power is greatly reduced or even stopped and an alarm is triggered for inspection and maintenance. In this way, the motor parameters are dynamically adjusted according to the evaluation results to prevent failures, improve reliability and safety, and ensure the normal operation of the vehicle.

[0034] In a possible implementation, the motor abnormality risk assessment is performed in combination with the motor load state prediction sequence, the multi-source motor state data and the associated component state data, and the motor operating parameters are dynamically adjusted according to the risk assessment results. Step S500 further includes step S510, pre-processing the motor load state prediction sequence, the multi-source motor state data and the associated component state data to obtain a standard state data set. Specifically, comprehensive data preprocessing is carried out for the motor load state prediction sequence, the multi-source motor state data and the associated component state data. In the data cleaning stage, the abnormal values ​​and noise data in the data are carefully checked and eliminated. For example, for the motor current data, if there is a maximum or minimum value that is obviously deviated from the normal range, and it is judged to be caused by sensor failure or instantaneous electromagnetic interference, it will be removed from the data set. At the same time, the data is normalized so that data from different sources and different magnitudes can be compared and analyzed on the same scale. For example, the motor temperature data and the temperature data of related components are normalized to the range of 0 to 1. This makes it easier to observe the relative relationship between them and the comprehensive impact on the risk of motor abnormalities. After these processes, a standard state data set is obtained, laying the foundation for subsequent accurate evaluation.

[0035] Step S520, determining the evaluation object, the evaluation object is the motor and its associated components. Specifically, the evaluation object is clearly defined as the motor and its associated components. As the core power component of the electric tricycle, the motor's operating state directly determines the performance and safety of the vehicle. The associated components include components that are structurally connected to the motor or spatially adjacent to the motor, such as a controller, a transmission gear, a heat sink, etc. These associated components interact with the motor, and their working conditions will have a direct or indirect impact on the motor. For example, an abnormal output signal of the controller may cause the motor speed to lose control, wear or jamming of the transmission gear will increase the load of the motor, and a failure of the heat sink will cause the motor temperature to be too high, so including them in the scope of the evaluation object can comprehensively and systematically evaluate the health of the entire power system.

[0036] Step S530, select key indicators that can reflect the abnormal risk of the motor from the standard state data set as key evaluation indicators, and configure weights for each key evaluation indicator. Specifically, carefully select key evaluation indicators that can reflect the abnormal risk of the motor from the standard state data set. For the motor itself, the temperature change rate of the motor is an extremely critical indicator, because a sharp rise in the motor temperature often indicates internal faults such as winding short circuit or overload operation; the current fluctuation amplitude of the motor is also very important. Large current fluctuations may indicate that the electrical connection of the motor is unstable or the load changes are abnormally drastic. In terms of associated components, the difference between the temperature of the associated components and the normal working temperature of the motor is one of the key indicators. If the difference is too large, it may affect the heat dissipation environment of the motor and cause a fault; the vibration amplitude of the associated components is also an important indicator. Excessive vibration may cause uneven force on the motor shaft system, accelerating wear and damage. Weights are configured according to the relative importance of the key evaluation indicators to the abnormal risk of the motor. The weight of each key indicator is determined by analyzing a large amount of historical fault data and summarizing actual engineering experience. For example, statistics show that among many motor failure cases, failures caused by overtemperature account for a high proportion, so the weight of the motor temperature change rate will be relatively large, set to 0.4; and although the vibration amplitude of the associated components can also cause failures, it is relatively rare, and its weight may be set to 0.15. Through such a reasonable weight configuration, the impact of each indicator can be more accurately reflected in the evaluation process.

[0037] Step S540, construct an ideal solution and a negative ideal solution, and use the ideal solution and the negative ideal solution to perform an abnormal risk assessment of the motor to obtain a risk assessment result. Specifically, construct an ideal solution and a negative ideal solution. An ideal solution refers to a virtual situation in which all evaluation indicators are in the best state, such as a motor temperature change rate of zero, a very small current fluctuation amplitude, a zero difference between the temperature of the associated component and the normal working temperature of the motor, and a zero vibration amplitude, etc. It represents the perfect operating state of the motor and its associated components. The negative ideal solution is in the worst state in all evaluation indicators, such as a sharp rise in motor temperature, a large current fluctuation, a serious deviation of the temperature of the associated component from the normal range, and a violent vibration, etc. This is the worst possible operating condition of the motor and its associated components. The abnormal risk assessment of the motor is performed by calculating the relative distance between each evaluation object (the actual operating state of the motor and its associated components) and the ideal solution and the negative ideal solution. A specific distance calculation method, such as the Euclidean distance formula, is used to calculate the actual value of each key evaluation indicator and the corresponding indicator value of the ideal solution and the negative ideal solution. According to the calculated relative distance, the abnormal risk degree of the motor is determined. For example, if the distance between the evaluation object and the ideal solution is close and the distance between the negative ideal solution is far, it means that the motor and its related components are in a good operating state and the risk is low; on the contrary, if the distance between the evaluation object and the ideal solution is far and the distance between the negative ideal solution is close, it means that the motor is in a high-risk state and may be about to fail. Through such an evaluation method, the results of the abnormal risk assessment of the motor can be quantitatively obtained, providing a scientific basis for taking corresponding measures such as early warning, maintenance or adjustment of motor operating parameters.

[0038] In one possible implementation, an ideal solution and a negative ideal solution are constructed, and the risk assessment of motor abnormality is performed using the ideal solution and the negative ideal solution to obtain a risk assessment result. Step S540 further includes step S541, calculating the relative distance between each evaluation object and the ideal solution and the negative ideal solution. Specifically, to calculate the relative distance between each evaluation object and the ideal solution and the negative ideal solution, it is necessary to select a suitable distance measurement method, such as the Euclidean distance formula. For each key evaluation indicator of the motor and its associated components, the actual value of the evaluation object on the indicator, the optimal value of the ideal solution on the indicator, and the worst value of the negative ideal solution on the indicator are substituted into the distance formula for calculation. For example, for the key indicator of motor temperature change rate, assume that the motor temperature change rate of the evaluation object is The ideal solution is that the motor temperature change rate is The motor temperature change rate of the negative ideal solution is , then the distance between the object and the ideal solution is evaluated on this indicator , the distance from the negative ideal solution This calculation is performed on all key evaluation indicators of the motor and its associated components, thereby obtaining the relative distance vector between the evaluation object and the ideal solution and the negative ideal solution in multiple dimensions.

[0039] Step S542, calculate the relative score of each evaluation object according to the relative distance. Specifically, a relative score calculation model is constructed according to the calculated relative distance. The calculation of the relative score is based on the relative position relationship between the evaluation object and the ideal solution and the negative ideal solution. Assume that the distance between the evaluation object and the ideal solution is , and the distance from the negative ideal solution is , then the relative score . Through this formula, the relative distance calculated previously is substituted to calculate the relative score of each evaluation object. The higher the relative score, the closer the evaluation object is to the ideal solution and the better its operating state; the lower the relative score, the closer it is to the negative ideal solution and the worse its operating state. For example, if the relative score of an evaluation object is close to 1, it means that the motor and its associated components perform relatively well in all key indicators and are close to a perfect operating state; if the relative score is close to 0, it means that there are major problems with its operating state and it is close to the worst case.

[0040] Step S543, according to the relative score, extract the relative score of each associated component to perform interference index analysis, and generate multiple interference factors. Specifically, according to the relative score, extract the relative score of each associated component to perform interference index analysis. For each associated component, such as a controller, a transmission component, etc., its relative score reflects the degree of its influence on the operation of the motor. For example, a low relative score of the controller may mean that its output signal is unstable or the temperature is too high, which will interfere with the operation of the motor. Through statistical analysis of the relative scores of each associated component, multiple interference factors are generated by factor analysis and other methods. If it is found that the relative scores of multiple associated components have commonalities in some aspects, such as the temperature-related scores of multiple associated components are all low, then a temperature interference factor can be generated, which represents the comprehensive interference degree of the high temperature state of these associated components on the operation of the motor; if there is a problem with the vibration-related score of the associated component, a vibration interference factor can be generated. These interference factors can more comprehensively and generally reflect the interference of the associated components on the operation of the motor.

[0041] Step S544, based on the relative score, extract the motor relative score, and correct the score by using the multiple interference factors, and generate the motor abnormality risk level according to the corrected motor relative score. Specifically, based on the relative score, extract the motor relative score, and correct the score by using multiple interference factors. Since the state of the associated components will affect the operation of the motor, it is necessary to adjust the relative score of the motor according to the interference factors. For example, if there is a higher temperature interference factor, it means that the high temperature of the associated components may make it difficult for the motor to dissipate heat, thereby increasing the risk of motor failure, then it is necessary to lower the relative score of the motor. Suppose the original relative score of the motor is The interference factor is , the corrected motor relative score ,in It is a correction factor determined based on experience or experiments. According to the corrected motor relative score, the motor abnormality risk level is generated. The corrected motor relative score is compared with the pre-set risk level threshold. For example, if the corrected motor relative score is greater than 0.8, the motor is judged to be at a low risk level, and its operation is relatively stable, and only routine monitoring is required; if the score is between 0.5 and 0.8, it is a medium risk level, and further inspection and possible maintenance of the motor and its associated components are required; if the score is less than 0.5, it is a high risk level, and the motor may be about to fail, and immediate measures need to be taken, such as stopping the motor operation, performing emergency repairs, etc., so as to achieve effective assessment and hierarchical management of motor abnormality risks and ensure the safe operation of electric tricycle motors.

[0042] The embodiment of the present application adopts a method of setting an action instruction analysis module between the starting device and the motor, and the module includes multiple monitoring and analysis units, each of which corresponds to a set of action instructions. By acquiring the action instructions of the electric tricycle in real time, the corresponding monitoring unit is activated to extract the real-time action parameters and perform load state prediction. The state data of the motor and related components are obtained through multi-source sensing equipment, and abnormal risk assessment is performed in combination with load state prediction and state data. The motor operating parameters are dynamically adjusted according to the evaluation results, achieving the technical effect of accurate monitoring and early warning of abnormalities of the electric tricycle motor under complex load changes.

[0043] In the above, refer to Figure 1 The motor operation abnormality monitoring method for an electric tricycle according to an embodiment of the present invention is described in detail. Figure 2 A motor operation abnormality monitoring system for an electric tricycle according to an embodiment of the present invention is described.

[0044] The motor operation abnormality monitoring system for electric tricycles according to the embodiment of the present invention is used to solve the technical problem that the prior art ignores the mutual influence between the motor and other components, resulting in the inability to accurately predict and monitor the abnormality of the electric tricycle motor under complex load changes, and achieves the technical effect of accurately monitoring and warning the abnormality of the electric tricycle motor under complex load changes. The motor operation abnormality monitoring system for electric tricycles includes: an action instruction analysis module establishment module 10, an associated parameter extraction module 20, a load state prediction module 30, a state data acquisition module 40, and a motor abnormality risk assessment module 50.

[0045] The action instruction analysis module establishment module 10 is used to establish an action instruction analysis module between the starting device and the motor. The action instruction analysis module includes a plurality of monitoring and analysis units, and each monitoring and analysis unit corresponds to a set of action instructions.

[0046] The associated parameter extraction module 20 is used to obtain the real-time action instructions of the electric tricycle, and according to the real-time action instructions, activate the corresponding target monitoring and analysis unit to extract the associated parameters to obtain the real-time action parameters.

[0047] The load state prediction module 30 is used to perform load state prediction based on the real-time action parameters through the target monitoring and analysis unit to obtain a motor load state prediction sequence.

[0048] The status data acquisition module 40 is used to acquire multi-source motor status data and associated component status data through multi-source sensing devices.

[0049] The motor abnormality risk assessment module 50 is used to combine the motor load state prediction sequence, the multi-source motor state data and the associated component state data to perform motor abnormality risk assessment, and dynamically adjust the motor operating parameters according to the risk assessment results.

[0050] Below, the specific configuration of the action instruction analysis module establishment module 10 will be described in detail. As described above, an action instruction analysis module is established between the starting device and the motor, and the action instruction analysis module includes multiple monitoring and analysis units, each monitoring and analysis unit corresponds to a group of action instructions, and the action instruction analysis module establishment module 10 further includes: a benchmark action instruction acquisition unit, the benchmark action instruction acquisition unit is used to obtain multiple benchmark action instructions according to the basic information of the target electric tricycle, and the benchmark action instruction can be a single action instruction or a combined action instruction; a key monitoring indicator extraction unit, the key monitoring indicator extraction unit is used to extract key monitoring indicators based on the multiple benchmark action instructions, and establish multiple monitoring and analysis units respectively; a user behavior analysis unit, the user behavior analysis unit is used to perform user behavior analysis based on the daily operation records of the target user, and generate a personalized correction plan; an initialization configuration unit, the initialization configuration unit is used to adopt the personalized correction plan, initialize the configuration of the multiple monitoring and analysis units, and connect to generate the action instruction analysis module.

[0051] Among them, according to the daily operation records of the target user, user behavior analysis is performed to generate a personalized correction plan, and the user behavior analysis unit further includes: a sample operation record extraction subunit, the sample operation record extraction subunit is used to extract multiple sample operation records of multiple action instructions based on the daily operation records of the target user; a multivariate clustering subunit, the multivariate clustering subunit is used to perform multivariate clustering on the multiple sample operation records to obtain multivariate action features of the multiple action instructions, the multivariate action features include action type, action duration, and action frequency; a contribution analysis subunit, the contribution analysis subunit is used to perform contribution analysis on the multivariate action features, and configure feature weights for the multivariate action features according to the contribution analysis results; a personalized impact analysis subunit, the personalized impact analysis subunit is used to perform personalized impact analysis on the multiple action instructions based on the multivariate action features and the corresponding feature weights, and generate the personalized correction plan according to the impact analysis results, and the personalized correction plan includes a correction strategy for the monitoring and analysis unit corresponding to each action instruction.

[0052] The specific configuration of the load state prediction module 30 will be described in detail below. As described above, based on the real-time action parameters, the load state prediction is performed through the target monitoring and analysis unit to obtain the motor load state prediction sequence, and the load state prediction module 30 further includes: a target monitoring and analysis unit component unit, the target monitoring and analysis unit component unit is used for the target monitoring and analysis unit to embed a load state prediction model, and the load state prediction model contains a user habit index for real-time action instructions; an instruction duration analysis unit, the instruction duration analysis unit is used to analyze and obtain the duration of the real-time action instruction according to the user habit index; a load state prediction unit, the load state prediction unit is used to take the real-time action parameters and duration as input, and through the load state prediction model, perform dynamic prediction of the load state to obtain the motor load state prediction sequence.

[0053] The specific configuration of the state data acquisition module 40 will be described in detail below. As described above, the state data acquisition module 40 further includes: a structure principle data acquisition unit, which is used to acquire the structural layout information and braking principle data of the electric tricycle; an associated component extraction unit, which is used to perform an associated component correlation analysis on the motor based on the structural layout information and braking principle data, and extract the braking associated component and the interference associated component according to the correlation analysis result; and a state data acquisition unit, which is used to arrange the multi-source sensing device, acquire the state data of the braking associated component and the interference associated component, and form the associated component state data.

[0054] Below, the specific configuration of the motor abnormality risk assessment module 50 will be described in detail. As described above, in combination with the motor load state prediction sequence, the multi-source motor state data and the associated component state data, the motor abnormality risk assessment is performed, and according to the risk assessment result, the motor operating parameters are dynamically adjusted. The motor abnormality risk assessment module 50 further includes: a state data preprocessing unit, the state data preprocessing unit is used to preprocess the motor load state prediction sequence, the multi-source motor state data and the associated component state data to obtain a standard state data set; an evaluation object determination unit, the evaluation object determination unit is used to determine the evaluation object, the evaluation object is the motor and its associated components; a key evaluation indicator determination unit, the key evaluation indicator determination unit is used to select key indicators that can reflect the motor abnormality risk from the standard state data set as key evaluation indicators, and configure weights for each key evaluation indicator; a risk assessment result acquisition unit, the risk assessment result acquisition unit is used to construct an ideal solution and a negative ideal solution, and perform motor abnormality risk assessment through the ideal solution and the negative ideal solution to obtain a risk assessment result.

[0055] Among them, an ideal solution and a negative ideal solution are constructed, and a motor abnormality risk assessment is performed through the ideal solution and the negative ideal solution to obtain a risk assessment result. The risk assessment result acquisition unit further includes: a relative distance calculation subunit, the relative distance calculation subunit is used to calculate the relative distance between each assessment object and the ideal solution and the negative ideal solution; a relative score calculation subunit, the relative score calculation subunit is used to calculate the relative score of each assessment object according to the relative distance; an interference factor generation subunit, the interference factor generation subunit is used to extract the relative scores of each associated component according to the relative scores, perform interference index analysis, and generate multiple interference factors; a score correction subunit, the score correction subunit is used to extract the motor relative score based on the relative score, and correct the score through the multiple interference factors, and generate the motor abnormality risk level according to the corrected motor relative score.

[0056] The motor abnormal operation monitoring system for an electric tricycle provided in an embodiment of the present invention can execute the motor abnormal operation monitoring method for an electric tricycle provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0057] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0058] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for monitoring abnormal operation of a motor of an electric tricycle, characterized in that: The method comprises: Establishing an action instruction analysis module between the starting device and the motor, wherein the action instruction analysis module includes a plurality of monitoring and analysis units, each monitoring and analysis unit corresponding to a set of action instructions; Acquire the real-time action instruction of the electric tricycle, and according to the real-time action instruction, activate the corresponding target monitoring and analysis unit to extract the associated parameters to obtain the real-time action parameters; Based on the real-time action parameters, load state prediction is performed by the target monitoring and analysis unit to obtain a motor load state prediction sequence; Acquire multi-source motor status data and associated component status data through multi-source sensing devices; The motor load state prediction sequence, the multi-source motor state data and the associated component state data are combined to perform motor abnormality risk assessment, and the motor operating parameters are dynamically adjusted according to the risk assessment results.

2. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 1, characterized in that: An action instruction analysis module is established between the starting device and the motor, and the action instruction analysis module includes multiple monitoring and analysis units, including: According to the basic information of the target electric tricycle, a plurality of reference action instructions are obtained, wherein the reference action instructions may be a single action instruction or a combined action instruction; Based on the multiple benchmark action instructions, extract key monitoring indicators and establish multiple monitoring and analysis units respectively; Conduct user behavior analysis based on target users’ daily operation records and generate personalized correction plans; The personalized correction scheme is adopted to initialize the configuration of the multiple monitoring and analysis units, and connect and generate the action instruction analysis module.

3. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 2, characterized in that: Conduct user behavior analysis based on target users’ daily operation records and generate personalized correction plans, including: Based on the daily operation records of the target user, extract multiple sample operation records of multiple action instructions respectively; Performing multivariate clustering on the plurality of sample operation records to obtain multivariate action features of the plurality of action instructions, the multivariate action features including action type, action occurrence duration, and action occurrence frequency; Performing contribution analysis on the multivariate action features, and configuring feature weights for the multivariate action features according to the contribution analysis results; Based on the multivariate action features and the corresponding feature weights, a personalized impact analysis is performed on the multiple action instructions, and the personalized correction scheme is generated according to the impact analysis results. The personalized correction scheme includes a correction strategy for the monitoring and analysis unit corresponding to each action instruction.

4. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 1, characterized in that: Based on the real-time action parameters, the target monitoring and analysis unit performs load state prediction to obtain a motor load state prediction sequence, including: The target monitoring and analysis unit is embedded with a load state prediction model, wherein the load state prediction model includes a user habit index for real-time action instructions; Analyze and obtain the duration of the real-time action instruction according to the user habit index; The real-time action parameters and duration are used as inputs, and the load state prediction model is used to perform dynamic prediction of the load state to obtain a motor load state prediction sequence.

5. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 2, characterized in that: Through multi-source sensing devices, multi-source motor status data and associated component status data are obtained, including: Obtaining the structural layout information and braking principle data of the electric tricycle; Based on the structural layout information and the braking principle data, a correlation analysis of associated components of the motor is performed, and according to the correlation analysis results, braking associated components and interference associated components are extracted; Multi-source sensing equipment is deployed to obtain the status data of the braking associated components and the interference associated components to form the associated component status data.

6. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 5, characterized in that: Combining the motor load state prediction sequence, the multi-source motor state data and the associated component state data, performing motor abnormality risk assessment includes: Preprocessing the motor load state prediction sequence, the multi-source motor state data and the associated component state data to obtain a standard state data set; Determining an evaluation object, wherein the evaluation object is a motor and its associated components; Selecting key indicators that can reflect the abnormal risk of the motor from the standard state data set as key evaluation indicators, and configuring a weight for each key evaluation indicator; An ideal solution and a negative ideal solution are constructed, and the risk assessment of motor abnormality is performed using the ideal solution and the negative ideal solution to obtain a risk assessment result.

7. The method for monitoring abnormal operation of a motor for an electric tricycle according to claim 6, characterized in that: The ideal solution and the negative ideal solution are used to evaluate the risk of motor abnormality, including: Calculate the relative distance of each evaluation object from the ideal solution and the negative ideal solution; Calculating a relative score for each evaluation object according to the relative distance; According to the relative scores, extracting the relative scores of the associated components to perform interference index analysis and generate multiple interference factors; Based on the relative score, the motor relative score is extracted, and the score is corrected using the multiple interference factors, and the motor abnormality risk level is generated based on the corrected motor relative score.

8. A motor abnormality monitoring system for an electric tricycle, characterized in that: The system is used to implement the motor operation abnormality monitoring method for an electric tricycle according to any one of claims 1 to 7, and the system comprises: An action instruction analysis module establishment module, wherein the action instruction analysis module establishment module is used to establish an action instruction analysis module between the starting device and the motor, wherein the action instruction analysis module includes a plurality of monitoring and analysis units, each monitoring and analysis unit corresponding to a set of action instructions; A correlation parameter extraction module, which is used to obtain the real-time action instructions of the electric tricycle, and according to the real-time action instructions, activate the corresponding target monitoring and analysis unit to extract the correlation parameters to obtain the real-time action parameters; A load state prediction module, the load state prediction module is used to perform load state prediction through the target monitoring and analysis unit based on the real-time action parameters to obtain a motor load state prediction sequence; A state data acquisition module, the state data acquisition module is used to acquire multi-source motor state data and associated component state data through a multi-source sensing device; A motor abnormality risk assessment module, wherein the motor abnormality risk assessment module is used to combine the motor load state prediction sequence, the multi-source motor state data and the associated component state data to perform motor abnormality risk assessment, and dynamically adjust the motor operating parameters according to the risk assessment results.

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