A method, device, electronic device and storage medium for monitoring the state of a driving motor

Abstract

The present application provides a drive motor state monitoring method, device, electronic device and storage medium. The method includes obtaining motor monitoring data of the drive motor, inputting the motor monitoring data into a digital twin model of the drive motor to obtain actual observed state parameters, and the digital twin model is used to characterize the motor characteristic relationship of the simulated drive motor; performing parameter estimation on the actual observed state parameters and operation control data through particle filtering to obtain estimated state parameters, and the estimated state parameters include at least one parameter estimation value, and comparing the at least one parameter estimation value with the corresponding preset parameter threshold to obtain the drive motor state, and the parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristic of the drive motor; the present application reduces the interference of noise on motor state monitoring through digital twin models and particle filtering, improves the accuracy, real-time performance and monitoring efficiency of nonlinear state monitoring, and thus reduces the risk of vehicle safety accidents.

Description

Technical Field

[0001] The present application relates to the field of motor monitoring technology, and in particular to a method, device, electronic device and storage medium for monitoring the state of a drive motor. Background Art

[0002] With the development and widespread adoption of new energy vehicles, electric vehicles have become the mainstream direction of future automotive development. However, the failure rate of electric vehicle drive motor systems is high, making effective monitoring and diagnosis of motor system status a critical research topic. Traditional fault detection methods rely primarily on manual experience, making them difficult to adapt to complex operating conditions and changing environments. Furthermore, faults are often diagnosed only after they occur, resulting in a delay. In recent years, digital twin technology has been widely applied to vehicle system modeling and state estimation, achieving considerable success. Digital twins use digital simulation to simulate the physical entity of a research object in terms of both mechanism and appearance. This allows for intuitive visualization of difficult-to-observe operational states, enabling better analysis of the research object and the development of appropriate operation and maintenance strategies.

[0003] At present, due to the influence of factors such as nonlinearity and noise interference in drive motor status monitoring, it is not possible to achieve real-time and accurate monitoring of the drive motor status through digital twin technology. Summary of the Invention

[0004] The present application provides a drive motor status monitoring method, device, electronic device and storage medium to solve the above-mentioned technical problem of being unable to accurately monitor the drive motor status in real time.

[0005] In one embodiment of the present application, the present application provides a method for monitoring the state of a drive motor, including: obtaining motor monitoring data of the drive motor, the motor monitoring data including operation control data, perception acquisition data and motor inherent data; inputting the motor monitoring data into a digital twin model of the drive motor to obtain actual observed state parameters, the digital twin model is used to characterize and simulate the motor characteristic relationship of the drive motor; performing parameter estimation on the actual observed state parameters and the operation control data through particle filtering to obtain estimated state parameters, the estimated state parameters including at least one parameter estimation value; comparing the at least one parameter estimation value with a corresponding preset parameter threshold to obtain the state of the drive motor, the parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristics of the drive motor.

[0006] In one embodiment of the present application, the at least one parameter estimation value is compared with the corresponding preset parameter threshold to obtain the drive motor state, including: if all parameter estimation values ​​meet the range of the preset parameter threshold, the drive motor state is determined to be normal, so as to obtain new motor monitoring data to continue the drive motor state monitoring; if there is at least one parameter estimation value that does not meet the range of the preset parameter threshold, the drive motor state is determined to be abnormal, so as to generate abnormal prompt information.

[0007] In one embodiment of the present application, the motor monitoring data is input into the digital twin model of the drive motor to obtain actual observed state parameters, including: if the motor characteristic relationship is a torque characteristic relationship, the first monitoring data is input into the torque twin model to obtain the value of the rotor flux; if the motor characteristic relationship is a speed characteristic relationship, the second monitoring data is input into the speed twin model to obtain the value of the friction coefficient; if the motor characteristic relationship is a quadrature-axis characteristic relationship and a direct-axis characteristic relationship, the third monitoring data and the value of the rotor flux are input into the electrical twin model to obtain electrical characteristic values, and the electrical characteristic values ​​include the value of the direct-axis component of inductance, the value of the quadrature-axis component of inductance and the value of the stator resistance; wherein the digital twin model includes the torque twin model, the speed twin model and the electrical twin model, the actual observed state parameters include the value of the rotor flux, the value of the friction coefficient and at least one of the electrical characteristic values, and the motor monitoring data includes the first monitoring data, the second monitoring data and the third monitoring data.

[0008] In one embodiment of the present application, the method for determining the digital twin model includes: if the motor characteristic relationship is a torque characteristic relationship, then the torque twin model is determined according to the number of permanent magnet pole pairs, stator quadrature axis current, rotor flux and motor torque; if the motor characteristic relationship is a speed characteristic relationship, then the speed twin model is determined according to angular acceleration, moment of inertia, motor torque, external load torque, friction coefficient and motor speed; if the motor characteristic relationship is a direct axis characteristic relationship, then the speed twin model is determined according to stator resistance, stator direct axis current, inductance direct axis component, electrical angular velocity, inductance quadrature axis component, stator quadrature axis component, The direct-axis twin model is determined by the stator direct-axis current and the stator direct-axis voltage; if the motor characteristic relationship is a quadrature-axis characteristic relationship, the quadrature-axis twin model is determined according to the stator resistance, the stator quadrature-axis current, the inductance quadrature-axis component, the electrical angular velocity, the inductance direct-axis component, the stator direct-axis current, the rotor flux and the stator quadrature-axis voltage; wherein the digital twin model includes the torque twin model, the speed twin model, the direct-axis twin model and the quadrature-axis twin model, the inductance direct-axis component and the inductance quadrature-axis component are equal, and the electrical twin model includes the direct-axis twin model and the quadrature-axis twin model.

[0009] In one embodiment of the present application, the actual observed state parameters and the operation control data are estimated by particle filtering to obtain estimated state parameters, including: obtaining multiple previous moment state parameters and multiple previous moment state weights corresponding to the actual observed state parameters; performing state transfer on each of the previous moment state parameters according to a preset state transfer relationship, a preset process noise relationship and the operation control data to obtain multiple current moment state parameters; performing state observation on each of the current moment state parameters according to a preset observation relationship and a preset measurement noise relationship to obtain multiple expected observed state parameters; updating the previous moment state weight corresponding to each of the current moment state parameters based on the actual observed state parameters and each of the expected observed state parameters to obtain multiple current moment state weights; and performing weighted summation on each of the current moment state weights and each of the current moment state parameters to obtain estimated state parameters.

[0010] In one embodiment of the present application, a weighted sum is performed on each current moment state weight and each current moment state parameter to obtain an estimated state parameter, including: normalizing each current moment state weight to obtain a plurality of normalized state weights; resampling each current moment state parameter based on each normalized state weight to obtain a plurality of sampled state weights and a plurality of sampled state parameters; and weighted summing each sampled state parameter according to each sampled state weight to obtain an estimated state parameter.

[0011] In one embodiment of the present application, the actual observed state parameters and the operation control data are estimated by particle filtering. Before obtaining the estimated state parameters, the method further includes: obtaining historical monitoring data of the drive motor; correcting the transfer parameters of the initial state transfer relationship according to the historical monitoring data to obtain a preset state transfer relationship; correcting the observation parameters of the initial observation relationship according to the historical monitoring data to obtain a preset observation relationship; wherein, the initial state transfer relationship is used to characterize the influence of different historical control data on the temporal evolution of at least one inherent monitoring feature, and the initial observation relationship is used to map the current state parameters to the observation space.

[0012] In one embodiment of the present application, the present application provides a drive motor state monitoring device, including: a data acquisition module, used to acquire motor monitoring data of the drive motor, the motor monitoring data including operation control data, perception acquisition data and motor inherent data; a model calculation module, used to input the motor monitoring data into the digital twin model of the drive motor to obtain actual observation state parameters, and the digital twin model is used to characterize and simulate the motor characteristic relationship of the drive motor; a particle estimation module, used to perform parameter estimation on the actual observation state parameters and the operation control data through particle filtering to obtain estimated state parameters, and the estimated state parameters include at least one parameter estimation value; a state determination module, used to compare the at least one parameter estimation value with the corresponding preset parameter threshold to obtain the drive motor state, and the parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristic of the drive motor.

[0013] In one embodiment of the present application, the present application provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic device implements the drive motor status monitoring method as described in any of the above embodiments.

[0014] In one embodiment of the present application, the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer executes the drive motor state monitoring method described in any one of the above embodiments.

[0015] Beneficial effects of the embodiments of the present application: The present application provides a drive motor state monitoring method, device, electronic device and storage medium. The embodiments of the present application simulate the characteristics of the drive motor through a digital twin model to obtain real-time actual observation state parameters, and perform particle filtering through the actual observation state parameters and operation control data to obtain estimated state parameters. The motor state is monitored by estimating the state parameters, which can reduce the interference of noise on motor state monitoring, improve the accuracy, real-time performance and monitoring efficiency of nonlinear state monitoring, and thus reduce the risk of vehicle safety accidents.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0018] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied;

[0019] Figure 2 A schematic diagram of a process for monitoring a driving motor state according to an embodiment of the present application is shown;

[0020] Figure 3 A schematic structural diagram of a three-phase permanent magnet synchronous motor according to an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram of a digital twin model according to an embodiment of the present application is shown;

[0022] Figure 5 A block diagram of a drive motor state monitoring device according to an embodiment of the present application is shown;

[0023] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0026] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0027] See also Figure 1 , Figure 1 Schematic diagram showing an exemplary system architecture to which the technical solution of the embodiment of the present application can be applied. Figure 1 As shown, the system architecture may include a computer device 101 and sensors 102. Computer device 101 may be at least one of a microcomputer, an embedded computer, a neural network computer, etc., and the number and type of sensors may be multiple. Sensor 102 is used to transmit sensed motor monitoring data to computer device 101 for motor status monitoring.

[0028] Exemplarily, the computer device 101 obtains motor monitoring data of the drive motor, which includes operation control data, sensory acquisition data and motor inherent data; the motor monitoring data is input into the digital twin model of the drive motor to obtain actual observed state parameters, and the digital twin model is used to characterize the motor characteristic relationship of the simulated drive motor; the actual observed state parameters and the operation control data are estimated by particle filtering to obtain estimated state parameters, and the estimated state parameters include at least one parameter estimation value; at least one parameter estimation value is compared with the corresponding preset parameter threshold to obtain the drive motor state, and the parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristics of the drive motor.

[0029] In related technologies, due to the influence of factors such as nonlinearity and noise interference in drive motor status monitoring, it is impossible to achieve real-time and accurate monitoring of the drive motor status through digital twin technology.

[0030] In order to solve the above technical problems, the present application provides a drive motor status monitoring method, device, electronic device and storage medium. The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below.

[0031] See also Figure 2 , Figure 2 FIG. 1 shows a flow chart of a method for monitoring the state of a driving motor according to an embodiment of the present application. Figure 2 As shown, in an exemplary embodiment, the driving motor state monitoring method includes at least steps S210 to S240, which are described in detail as follows:

[0032] Step S210: Acquire motor monitoring data of the drive motor.

[0033] Among them, motor monitoring data includes operation control data, perception collection data and motor inherent data.

[0034] In one embodiment of the present application, the drive motor includes a three-phase permanent magnet synchronous motor.

[0035] In one embodiment of the present application, operational control data is used to represent data used to control the operation of the drive motor, such as motor speed and torque. Sensory acquisition data is used to represent data collected by various sensors, such as three-phase current, three-phase voltage, and motor rotor position. Intrinsic motor data is used to represent data that does not change with operating conditions, such as the number of permanent magnet pole pairs and fixed moment of inertia.

[0036] In one embodiment of the present application, the drive motor may also be equipped with an adjustable moment of inertia.

[0037] Step S220: Input the motor monitoring data into the digital twin model of the drive motor to obtain actual observed state parameters.

[0038] Among them, the digital twin model is used to characterize the motor characteristic relationship of the simulated drive motor.

[0039] In one embodiment of the present application, the method for determining the digital twin model includes: if the motor characteristic relationship is a torque characteristic relationship, then the torque twin model is determined according to the number of permanent magnet pole pairs, stator quadrature axis current, rotor flux and motor torque; if the motor characteristic relationship is a speed characteristic relationship, then the speed twin model is determined according to angular acceleration, moment of inertia, motor torque, external load torque, friction coefficient and motor speed; if the motor characteristic relationship is a direct axis characteristic relationship, then the speed twin model is determined according to stator resistance, stator direct axis current, inductance direct axis component, electrical angular velocity, inductance cross axis current, and motor speed. The direct-axis twin model is determined according to the stator quadrature-axis component, stator quadrature-axis current and stator direct-axis voltage; if the motor characteristic relationship is a quadrature-axis characteristic relationship, the quadrature-axis twin model is determined according to the stator resistance, stator quadrature-axis current, inductance quadrature-axis component, electrical angular velocity, inductance direct-axis component, stator direct-axis current, rotor flux and stator quadrature-axis voltage; among them, the digital twin model includes a torque twin model, a speed twin model, a direct-axis twin model and a quadrature-axis twin model, the inductance direct-axis component and the inductance quadrature-axis component are equal, and the electrical twin model includes a direct-axis twin model and a quadrature-axis twin model.

[0040] In one embodiment of the present application, the digital twin model includes a twin physical model and a twin data model of the drive motor.

[0041] In one embodiment of the present application, a twin physical model is established based on the structure of the vehicle drive motor. The twin physical model includes a speed twin model and a torque twin model. The twin physical model of the drive motor can be constructed using Matlab's Simulink simulation module.

[0042] In one embodiment of the present application, a twin data model is constructed based on the electrical parameters of the drive motor, such as a direct-axis twin model and a quadrature-axis twin model, that is, an electrical twin model.

[0043] In one embodiment of the present application, see Figure 3 , Figure 3 FIG. 1 shows a schematic structural diagram of a three-phase permanent magnet synchronous motor according to an embodiment of the present application. Figure 3 As shown, Ru, Rv, and Rw represent the resistance in the three-phase winding of the motor, Lu, Lv, and Lw represent the self-inductance coefficient in the three-phase winding of the motor, Uu, Uv, and Uw represent the voltage in the three-phase winding of the motor, iu, iv, and iw represent the stator current in the three-phase winding of the motor, q is the quadrature axis, and d is the direct axis, thereby establishing a twin physical model.

[0044] In one embodiment of the present application, the torque twin model is shown as follows:

[0045]

[0046] Among them, T r is the motor torque, P n is the number of permanent magnet pole pairs, i q is the stator quadrature axis current, is the rotor flux.

[0047] In one embodiment of the present application, in a permanent magnet synchronous motor, the rotor flux linkage is used to characterize the magnetic flux generated by the permanent magnet.

[0048] In one embodiment of the present application, the speed twin model is shown as follows:

[0049]

[0050] in, is the angular acceleration, J is the moment of inertia, T r is the motor torque, T load is the external load torque, μ CoF is the friction coefficient, n r is the motor speed.

[0051] In one embodiment of the present application, see Figure 4 , Figure 4 FIG1 shows a schematic diagram of a digital twin model according to an embodiment of the present application. Figure 4As shown, the motor speed and motor torque are external inputs and can be calculated through the three-phase current, three-phase voltage, and motor rotor position collected by the rotary transformer; iα, iβ are the current values ​​obtained by performing Clarke transformation on the three-phase current of the driving motor, so as to convert the three-phase coordinate system corresponding to the ABC coordinate system into the two-phase stationary coordinate system corresponding to the α-β coordinate system; id, iq are the current values ​​obtained by performing Park transformation on the driving motor, so as to convert the two-phase stationary coordinate system corresponding to the α-β coordinate system into the two-phase rotating coordinate system corresponding to the dq coordinate system; n r Represents the motor speed, T r represents the motor torque; the θ collected by the resolver is the angular position of the rotor relative to the stator; by performing proportional-integral (PI) control on the motor speed, the quadrature-axis control current i is output qr , based on i qr The stator quadrature-axis current is subjected to quadrature-axis PI current control to obtain the stator quadrature-axis voltage, and the stator direct-axis voltage is subjected to direct-axis PI current control based on the initial direct-axis control current with a current value of 0 and the stator direct-axis current to obtain the stator direct-axis voltage; the data after Park inverse transformation of the stator direct-axis voltage and the stator direct-axis voltage are respectively input into space vector pulse width modulation (SVPWM) to control the rotor rotation of the drive motor through SVPWM and a three-phase inverter.

[0052] In one embodiment of the present application, the direct axis twin model is shown as follows:

[0053]

[0054] Among them, u d is the stator direct axis voltage, R is the stator resistance, i d is the stator direct axis current, L d is the direct axis component of inductance, ω e is the electrical angular velocity, L q is the quadrature-axis component of inductance, i q is the stator quadrature-axis current.

[0055] In one embodiment of the present application, the cross-axis twin model is shown as follows:

[0056]

[0057] Among them, u q is the stator quadrature axis voltage, R is the stator resistance, i q is the stator quadrature axis current, L q is the quadrature-axis component of inductance, ω e is the electrical angular velocity, L dis the direct axis component of inductance, i d is the stator direct axis current, is the rotor flux.

[0058] In one embodiment of the present application, the motor monitoring data is input into the digital twin model of the drive motor to obtain actual observed state parameters, including: if the motor characteristic relationship is a torque characteristic relationship, the first monitoring data is input into the torque twin model to obtain the value of the rotor flux; if the motor characteristic relationship is a speed characteristic relationship, the second monitoring data is input into the speed twin model to obtain the value of the friction coefficient; if the motor characteristic relationship is a quadrature-axis characteristic relationship and a direct-axis characteristic relationship, the third monitoring data and the value of the rotor flux are input into the electrical twin model to obtain electrical characteristic values, which include the value of the direct-axis component of the inductance, the value of the quadrature-axis component of the inductance, and the value of the stator resistance; wherein the digital twin model includes a torque twin model, a speed twin model, and an electrical twin model, the actual observed state parameters include at least one of the value of the rotor flux, the value of the friction coefficient, and the electrical characteristic value, and the motor monitoring data includes the first monitoring data, the second monitoring data, and the third monitoring data.

[0059] Step S230 , performing parameter estimation on the actual observed state parameters and the operation control data through particle filtering to obtain estimated state parameters.

[0060] The estimated state parameter includes at least one parameter estimation value.

[0061] In one embodiment of the present application, parameter estimation is performed on actual observed state parameters and operation control data through particle filtering to obtain estimated state parameters, including: obtaining multiple previous moment state parameters and multiple previous moment state weights corresponding to the actual observed state parameters; performing state transfer on each previous moment state parameter according to a preset state transfer relationship, a preset process noise relationship and operation control data to obtain multiple current moment state parameters; performing state observation on each current moment state parameter according to a preset observation relationship and a preset measurement noise relationship to obtain multiple expected observed state parameters; updating the previous moment state weight corresponding to each current moment state parameter based on the actual observed state parameter and each expected observed state parameter to obtain multiple current moment state weights; and performing weighted summation on each current moment state weight and each current moment state parameter to obtain estimated state parameters.

[0062] In one embodiment of the present application, multiple previous moment state parameters and multiple previous moment state weights corresponding to the actual observed state parameters are obtained, including: if the monitoring moment of the actual observed state parameters is the initial moment, then multiple initial state parameters randomly generated based on a preset prior distribution are used as multiple previous moment state parameters, and multiple previous moment state weights are determined according to the number of each initial state parameter; if the monitoring moment of the actual observed state parameters is not the initial moment, then multiple previous moment state parameters and multiple previous moment state weights corresponding to the monitoring moment are obtained.

[0063] In one embodiment of the present application, n1 pairs of initial state parameters x0 are randomly generated based on a preset prior distribution.

[0064] In one embodiment of the present application, the inherent monitoring characteristics include the rotor flux Friction coefficient μ CoF , stator resistance R, inductance direct axis component L d and the quadrature-axis component of the inductance L q .

[0065] In one embodiment of the present application, particles at different moments are used to characterize motor state parameters at different moments composed of at least one inherent monitoring feature.

[0066] In one embodiment of the present application, the state parameter at the previous moment is also the particle at the previous moment, the initial state parameter is also the initial particle, and the state parameter at the current moment is also the particle at the current moment.

[0067] In one embodiment of the present application, the actual observed state parameter is used to characterize a model-calculated value of at least one inherent monitoring feature in the drive motor.

[0068] In one embodiment of the present application, the weight of the initial state parameter is 1.

[0069] In one embodiment of the present application, the preset prior distribution includes a Gaussian distribution, and the probability density function of the Gaussian distribution is as follows:

[0070]

[0071] Among them, f1(x0) is the probability density of the initial state parameter x0, δ 2 is the variance, and u is the disturbance mean.

[0072] In one embodiment of the present application, a state space model is established, including a state equation and an observation equation.

[0073] In one embodiment of the present application, the actual observed state parameters and operation control data are estimated by particle filtering. Before obtaining the estimated state parameters, the method also includes: obtaining historical monitoring data of the drive motor; correcting the transfer parameters of the initial state transfer relationship according to the historical monitoring data to obtain a preset state transfer relationship; correcting the observation parameters of the initial observation relationship according to the historical monitoring data to obtain a preset observation relationship; wherein the initial state transfer relationship is used to characterize the influence of different historical control data on the temporal evolution of the motor state parameters, and the initial observation relationship is used to map the current state parameters to the observation space.

[0074] In one embodiment of the present application, a state space model of a drive motor is established, and the state space model includes a state transition model and a state observation model. The state transition model is used to determine the current state parameters, and the state observation model is used to determine the expected observed state parameters.

[0075] In one embodiment of the present application, the historical monitoring data includes experimental data of three-phase current, three-phase voltage, and motor rotor position under various operating conditions, including different temperatures, different motor speeds, and different motor loads.

[0076] In one embodiment of the present application, multiple current state parameters are determined as follows:

[0077] X T =f2(X T-1 ,μ T ,w T ) Formula (6)

[0078] Among them, X T All current state parameters x at time T T The set of f2 is the preset state transition relationship, X T-1 is the set of state parameters before time T-1, μ T Input the corresponding operation control data for the system at time T, w T is the process noise value at time T.

[0079] In one embodiment of the present application, the determination of multiple expected observation state parameters is as follows:

[0080] Y T =h(X T ,n T ) Formula (7)

[0081] Among them, Y T is all expected observation state parameters y at time T T The set of h is the preset observation relationship, X T For all current state parameters xT The collection of n T is the observation noise value at time T.

[0082] In one embodiment of the present application, the process noise value is determined as follows:

[0083]

[0084] Among them, p(w T ) is the process noise value w at time T T The probability density of , n2 is the dimension of the state parameters at the current moment, and |Q| is the determinant of the covariance matrix Q.

[0085] In one embodiment of the present application, the covariance matrix is ​​determined as follows:

[0086]

[0087] Among them, Q is the covariance matrix, R t is the stator resistance at time t, is the rotor flux at time t.

[0088] In one embodiment of the present application, the current state weight is determined as follows:

[0089]

[0090] in, is the current state weight corresponding to the current state parameter at the i-th moment t, is the state weight of the previous moment corresponding to the state parameter of the previous moment at the i-th moment t-1, is the observation likelihood function corresponding to the current state parameter at the i-th moment t, n1 is the total number of state parameters at the current moment, z t is the actual observed state parameter at time t.

[0091] In one embodiment of the present application, Based on the actual observed state parameter z at time t t and the expected observation state parameter y at time t t get.

[0092] In one embodiment of the present application, a weighted sum is performed on each current moment state weight and each current moment state parameter to obtain an estimated state parameter, including: normalizing each current moment state weight to obtain a plurality of normalized state weights; resampling each current moment state parameter based on each normalized state weight to obtain a plurality of sampled state weights and a plurality of sampled state parameters; and weighted summing each sampled state parameter according to each sampled state weight to obtain an estimated state parameter.

[0093] In one embodiment of the present application, the normalized state weight is determined as follows:

[0094]

[0095] in, is the normalized state weight corresponding to the current state parameter at the i-th moment t, is the current state weight corresponding to the current state parameter at the i-th moment t, and n1 is the total number of current state parameters.

[0096] In one embodiment of the present application, a resampling threshold is set to screen out N particles whose initial sampling weights are greater than the resampling threshold, and multiple sampling state parameters are obtained. The resampling threshold is determined as follows:

[0097]

[0098] Among them, N th is the resampling threshold, is the normalized state weight corresponding to the current state parameter at the i-th moment t, and n1 is the total number of state parameters at the current moment.

[0099] In one embodiment of the present application, the sampling state weight corresponding to the sampling state parameter is determined as follows:

[0100]

[0101] in, is the sampling state weight of the sampling state parameter at the i-th time t, and N is the total number of sampling state parameters.

[0102] In one embodiment of the present application, the estimated state parameters are determined as follows:

[0103]

[0104] in, To estimate the state parameters, is the sampling state parameter at the ith moment t, is the sampling state weight of the sampling state parameter at the i-th time t, and N is the total number of sampling state parameters.

[0105] Step S240 : Compare at least one parameter estimation value with a corresponding preset parameter threshold to obtain a driving motor state.

[0106] The parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristic of the drive motor.

[0107] In one embodiment of the present application, at least one parameter estimation value is compared with the corresponding preset parameter threshold value to obtain the drive motor state, including: if all parameter estimation values ​​meet the range of the preset parameter threshold value, the drive motor state is determined to be normal, so as to obtain new motor monitoring data to continue the drive motor state monitoring; if there is at least one parameter estimation value that does not meet the range of the preset parameter threshold value, the drive motor state is determined to be abnormal, so as to generate abnormal prompt information.

[0108] In one embodiment of the present application, the abnormal prompt information is used to alarm and remind the driver to take corresponding measures.

[0109] In one embodiment of the present application, the present application uses the three-phase current, three-phase voltage, and rotor position information collected by various sensors of the drive motor to update the parameters of the digital twin model in real time, and combines it with a particle filter algorithm to perform parameter estimation, ultimately performing real-time status monitoring of the drive motor. Compared with traditional drive motor status detection methods, the drive motor status monitoring based on digital twins and particle filter algorithms can monitor and diagnose the drive motor in real time while it is operating, realizing digital diagnostic detection and improving real-time performance, monitoring efficiency, and monitoring accuracy.

[0110] See also Figure 5 , Figure 5 A block diagram of a drive motor state monitoring device according to an embodiment of the present application is shown. The device can be applied to Figure 1 The implementation environment shown in FIG. 1 is specifically configured in the computer device 101. The apparatus may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the apparatus is applicable.

[0111] like Figure 5 As shown, a drive motor state monitoring device 500 according to an embodiment of the present application includes: a data acquisition module 501 , a model calculation module 502 , a particle estimation module 503 and a state determination module 504 .

[0112] The data acquisition module 501 is used to acquire the motor monitoring data of the drive motor, and the motor monitoring data includes operation control data, sensory acquisition data and motor inherent data;

[0113] A model calculation module 502 is used to input the motor monitoring data into the digital twin model of the drive motor to obtain actual observed state parameters. The digital twin model is used to characterize the motor characteristic relationship of the simulated drive motor;

[0114] A particle estimation module 503 is configured to perform parameter estimation on the actual observed state parameters and the operation control data through particle filtering to obtain estimated state parameters, where the estimated state parameters include at least one parameter estimation value;

[0115] The state determination module 504 is configured to compare at least one parameter estimation value with a corresponding preset parameter threshold value to obtain a state of the drive motor, wherein the parameter estimation value is used to characterize an estimation value of an inherent monitoring characteristic of the drive motor.

[0116] It should be noted that the drive motor state monitoring device provided in the above embodiment and the drive motor state monitoring method provided in the above embodiment are based on the same concept. The specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the drive motor state monitoring device provided in the above embodiment can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0117] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by one or more processors, enables the electronic device to implement the drive motor state monitoring method provided in the above-mentioned embodiments.

[0118] See also Figure 6 , Figure 6 The following is a schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application. Figure 6 The computer system 600 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0119] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603, such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the random access memory 603. The CPU 601, the read-only memory 602 and the random access memory 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0120] The following components are connected to the input / output interface 605: an input section 606 including a keyboard, a mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read therefrom can be installed into the storage section 608 as needed.

[0121] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the various functions defined in the system of the present application are executed.

[0122] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The units involved in the embodiments described in the present application can be implemented by software or by hardware, and the units described can also be set in a processor. The names of these units do not constitute a limitation on the units themselves under certain circumstances. Therefore, the technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present application.

[0125] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When executed by a computer processor, the computer program causes the computer to perform the drive motor state monitoring methods provided in the above-described embodiments. The computer-readable storage medium may be included in the electronic device described in the above-described embodiments, or may exist independently and not be incorporated into the electronic device.

[0126] In the above embodiments, unless otherwise specified, the use of serial numbers such as "first" and "second" to describe common objects only indicates that they refer to different instances of the same object, rather than indicating that the objects being described must adopt a given order, whether in time, space, sorting or any other way.

[0127] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, any equivalent modifications or alterations accomplished by a person of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for monitoring the state of a drive motor, characterized in that: The method comprises: Acquiring motor monitoring data of the drive motor, wherein the motor monitoring data includes operation control data, sensory acquisition data, and motor inherent data; Inputting the motor monitoring data into the digital twin model of the drive motor to obtain actual observed state parameters, wherein the digital twin model is used to characterize and simulate the motor characteristic relationship of the drive motor; Perform parameter estimation on the actual observed state parameters and the operation control data through particle filtering to obtain estimated state parameters, wherein the estimated state parameters include at least one parameter estimation value; wherein a plurality of previous moment state parameters and a plurality of previous moment state weights corresponding to the actual observed state parameters are obtained; a state transfer is performed on each of the previous moment state parameters according to a preset state transfer relationship, a preset process noise relationship and the operation control data to obtain a plurality of current moment state parameters; a state observation is performed on each of the current moment state parameters according to a preset observation relationship and a preset measurement noise relationship to obtain a plurality of expected observed state parameters; a previous moment state weight corresponding to each of the current moment state parameters is updated based on the actual observed state parameters and each of the expected observed state parameters to obtain a plurality of current moment state weights; a weighted summation of each of the current moment state weights and each of the current moment state parameters is performed to obtain an estimated state parameter; The at least one parameter estimation value is compared with a corresponding preset parameter threshold to obtain a driving motor state, wherein the parameter estimation value is used to characterize an estimation value of an inherent monitoring characteristic of the driving motor.

2. The driving motor state monitoring method according to claim 1, characterized in that: Comparing the at least one parameter estimation value with a corresponding preset parameter threshold to obtain a driving motor state includes: If all parameter estimation values ​​meet the range of preset parameter thresholds, the drive motor state is determined to be normal, so as to obtain new motor monitoring data and continue to monitor the drive motor state; If at least one parameter estimation value does not meet the range of the preset parameter threshold, the driving motor state is determined to be motor abnormality to generate abnormality prompt information.

3. The driving motor state monitoring method according to claim 1, characterized in that: Inputting the motor monitoring data into the digital twin model of the drive motor to obtain actual observed state parameters includes: If the motor characteristic relationship is a torque characteristic relationship, the first monitoring data is input into the torque twin model to obtain a value of the rotor flux; If the motor characteristic relationship is a speed characteristic relationship, the second monitoring data is input into the speed twin model to obtain a value of the friction coefficient; If the motor characteristic relationship is a quadrature-axis characteristic relationship and a direct-axis characteristic relationship, inputting the third monitoring data and the value of the rotor flux into the electrical twin model to obtain electrical characteristic values, where the electrical characteristic values ​​include the value of the direct-axis component of inductance, the value of the quadrature-axis component of inductance, and the value of the stator resistance; Among them, the digital twin model includes the torque twin model, the speed twin model and the electrical twin model, the actual observed state parameters include the value of the rotor flux, the value of the friction coefficient and at least one of the electrical characteristic values, and the motor monitoring data includes the first monitoring data, the second monitoring data and the third monitoring data.

4. The driving motor state monitoring method according to claim 3, characterized in that: Methods for determining the digital twin model include: If the motor characteristic relationship is a torque characteristic relationship, a torque twin model is determined according to the number of permanent magnet pole pairs, the stator quadrature axis current, the rotor flux and the motor torque; If the motor characteristic relationship is a speed characteristic relationship, determining a speed twin model according to angular acceleration, moment of inertia, motor torque, external load torque, friction coefficient, and motor speed; If the motor characteristic relationship is a direct-axis characteristic relationship, determining a direct-axis twin model according to the stator resistance, the stator direct-axis current, the direct-axis component of the inductance, the electrical angular velocity, the quadrature-axis component of the inductance, the stator quadrature-axis current, and the stator direct-axis voltage; If the motor characteristic relationship is a quadrature-axis characteristic relationship, a quadrature-axis twin model is determined according to the stator resistance, the stator quadrature-axis current, the quadrature-axis component of the inductance, the electrical angular velocity, the direct-axis component of the inductance, the stator direct-axis current, the rotor flux, and the stator quadrature-axis voltage; Among them, the digital twin model includes the torque twin model, the speed twin model, the direct-axis twin model and the quadrature-axis twin model, the direct-axis component of inductance and the quadrature-axis component of inductance are equal, and the electrical twin model includes the direct-axis twin model and the quadrature-axis twin model.

5. The driving motor state monitoring method according to any one of claims 1 to 4, characterized in that: Performing a weighted summation on each current moment state weight and each current moment state parameter to obtain an estimated state parameter, including: Normalizing each of the current state weights to obtain a plurality of normalized state weights; Resampling each of the current state parameters based on each of the normalized state weights to obtain a plurality of sampled state weights and a plurality of sampled state parameters; The sampling state parameters are weighted and summed according to the sampling state weights to obtain the estimated state parameters.

6. The driving motor state monitoring method according to any one of claims 1 to 4, characterized in that: Before performing parameter estimation on the actual observed state parameters and the operation control data by particle filtering to obtain the estimated state parameters, the method further includes: Acquiring historical monitoring data of the drive motor; Modifying the transfer parameters of the initial state transfer relationship according to the historical monitoring data to obtain a preset state transfer relationship; Correcting the observation parameters of the initial observation relationship according to the historical monitoring data to obtain a preset observation relationship; The initial state transfer relationship is used to characterize the influence of different historical control data on the temporal evolution of at least one inherent monitoring feature, and the initial observation relationship is used to map the current state parameters to the observation space.

7. A drive motor status monitoring device, characterized in that: The device comprises: A data acquisition module is used to acquire motor monitoring data of the drive motor, wherein the motor monitoring data includes operation control data, sensory acquisition data and motor inherent data; a model calculation module, configured to input the motor monitoring data into a digital twin model of the drive motor to obtain actual observed state parameters, wherein the digital twin model is used to characterize and simulate motor characteristic relationships of the drive motor; a particle estimation module configured to perform parameter estimation on the actual observed state parameters and the operation control data through particle filtering to obtain estimated state parameters, wherein the estimated state parameters include at least one parameter estimation value; wherein a plurality of previous moment state parameters and a plurality of previous moment state weights corresponding to the actual observed state parameters are obtained; a state transfer is performed on each of the previous moment state parameters according to a preset state transfer relationship, a preset process noise relationship, and the operation control data to obtain a plurality of current moment state parameters; a state observation is performed on each of the current moment state parameters according to a preset observation relationship and a preset measurement noise relationship to obtain a plurality of expected observed state parameters; a previous moment state weight corresponding to each of the current moment state parameters is updated based on the actual observed state parameters and each of the expected observed state parameters to obtain a plurality of current moment state weights; and a weighted summation is performed on each of the current moment state weights and each of the current moment state parameters to obtain an estimated state parameter; The state determination module is used to compare the at least one parameter estimation value with the corresponding preset parameter threshold to obtain the state of the drive motor, wherein the parameter estimation value is used to characterize the estimated value of the inherent monitoring characteristic of the drive motor.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the drive motor state monitoring method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the drive motor state monitoring method according to any one of claims 1 to 6.

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