Temperature determination method, training method and device of temperature prediction model, and vehicle

Abstract

The invention provides a temperature determination method, a temperature prediction model training method and device, and a vehicle, and the method comprises the steps: obtaining the operation data of an electric drive system of the vehicle at a first moment, a first temperature value of a to-be-tested assembly in a thermal network topology corresponding to the electric drive system, and a second temperature value of a first assembly which exchanges heat with the to-be-tested assembly, and determining a predicted temperature value of the to-be-measured assembly at a second moment after the first moment by adopting a temperature prediction model according to the operation data, the first temperature value and the second temperature value, and predicting the temperature value of the to-be-measured assembly through the temperature prediction model. The temperature value and the operation data of the first assembly which exchanges heat with the to-be-tested assembly are considered, and the accuracy of temperature prediction of the to-be-tested assembly is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a temperature determination method, a temperature prediction model training method, a device and a vehicle. Background Art

[0002] At present, the drive system of new energy vehicles widely adopts a three-in-one electric drive system consisting of a permanent magnet synchronous motor, its controller and a reducer. The electric drive system generates serious heat and has poor heat dissipation conditions. Therefore, it is necessary to monitor the temperature of the main components in the electric drive system in real time.

[0003] Among them, the main components in the electric drive system constitute the thermal network topology of the electric drive system. Therefore, accurately predicting the temperature of components in the electric drive system is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the present application proposes a temperature determination method, a temperature prediction model training method, a device and a vehicle to improve the accuracy of temperature prediction of components in an electric drive system.

[0006] In one aspect, an embodiment of the present application provides a temperature determination method, comprising:

[0007] Acquire operation data of an electric drive system of a vehicle at a first moment, a first temperature value of a component to be tested in the electric drive system, and a second temperature value of a first component that has heat exchange with the component to be tested;

[0008] A temperature prediction model is used to determine a predicted temperature value of the component under test at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value.

[0009] Another aspect of the present application provides a method for training a temperature prediction model, including:

[0010] Acquire a training sample; wherein the training sample includes operation data of the electric drive system of the vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component;

[0011] Inputting the operating data, the first temperature value, and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment;

[0012] The temperature prediction model is trained according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

[0013] Another aspect of the present application provides a temperature determination device, comprising:

[0014] an acquisition module, configured to acquire operating data of an electric drive system of a vehicle at a first moment, a first temperature value of a component to be tested in the electric drive system, and a second temperature value of a first component that has heat exchange with the component to be tested;

[0015] A determination module is used to determine a predicted temperature value of the component to be tested at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value by using a temperature prediction model.

[0016] Another aspect of the present application provides a training device for a temperature prediction model, including:

[0017] An acquisition module, configured to acquire training samples; wherein the training samples include operating data of an electric drive system of a vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component;

[0018] a determination module, configured to input the operating data, the first temperature value, and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment;

[0019] The training module is used to train the temperature prediction model according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

[0020] Another aspect of the present application provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above embodiment is implemented.

[0021] Another aspect of the present application is a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the above embodiment is implemented.

[0022] Another aspect of the present application provides a computer program product on which a computer program is stored. When the program is executed by a processor, the method described in the above embodiments is implemented.

[0023] The temperature determination method, temperature prediction model training method, device and vehicle proposed in the present application obtain the operating data of the electric drive system of the vehicle at a first moment, the first temperature value of the component to be measured in the thermal network topology corresponding to the electric drive system, and the second temperature value of the first component that has heat exchange with the component to be measured. The temperature prediction model is used to determine the predicted temperature value of the component to be measured at a second moment after the first moment based on the operating data, the first temperature value and the second temperature value. In the process of predicting the temperature value of the component to be measured by the temperature prediction model, the influence of the temperature value and operating data of the first component that has heat exchange with the component to be measured is taken into account, thereby improving the accuracy of the temperature prediction of the component to be measured.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A schematic diagram of a temperature determination method provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of a flow chart of another temperature determination method provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of an electric drive system provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of a thermal network topology corresponding to an electric drive system provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of a flow chart of another method for training a temperature prediction model provided in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of the structure of a temperature determination device provided in an embodiment of the present application;

[0032] Figure 7 A schematic diagram of the structure of a training device for a temperature prediction model provided in an embodiment of the present application;

[0033] Figure 8 It is a block diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following describes the temperature determination method, temperature prediction model training method, device and vehicle of the embodiments of the present application with reference to the accompanying drawings.

[0036] Figure 1 A flow chart of a temperature determination method provided in an embodiment of the present application.

[0037] The embodiment of the present application takes the temperature determination method being configured in a temperature determination device as an example. The temperature determination device can be applied to any vehicle-mounted device so that the vehicle-mounted device can perform a temperature determination function.

[0038] like Figure 1 As shown, the method may include the following steps:

[0039] Step 101 , obtaining operating data of an electric drive system of a vehicle at a first moment, a first temperature value of a component to be tested in the electric drive system, and a second temperature value of a first component that has heat exchange with the component to be tested.

[0040] Among them, the electric drive system, also known as the motor drive system, has a corresponding thermal network topology. The thermal network topology in the motor drive system is a model used to simulate and analyze the heat transfer inside the system. This model regards the motor and its drive components (such as controllers, etc.) as a network composed of multiple components. Each node in the network represents a part of the system, which may include but is not limited to key components such as motor rotors, bearings, and reducers.

[0041] The component to be tested is any component in the electric drive system, or any multiple components, to achieve prediction of one component, or synchronously output prediction results of multiple components to improve efficiency.

[0042] In the embodiment of the present application, there is heat exchange between the components in the electric drive system, especially the mutual influence of the temperature between the components with direct heat exchange is large. Therefore, for each component to be tested, the first component with heat exchange has a large influence on the temperature change of the component to be tested. Therefore, when predicting the temperature of the component to be tested, not only the first temperature value of the component to be tested should be considered, but also the second temperature value of the first component with heat exchange. As an example, if the component to be tested is the rotor of the motor, the first component with heat exchange includes the stator, bearings, cooling oil and other components of the motor.

[0043] The operating data refers to the operating condition data related to the temperature rise, including at least one of the motor speed, current, torque and coolant flow rate, and the operating data has an impact on the temperature prediction of the component to be tested. The coolant refers to a liquid that can cool the electric drive system, including oil, water, ethanol or a mixed liquid, etc., which is not limited in this embodiment.

[0044] Step 102: using a temperature prediction model to determine a predicted temperature value of the component to be tested at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value.

[0045] Among them, the temperature prediction model is a neural network model constructed based on the thermal network topology corresponding to the vehicle's electric drive system, for example, a neural network model constructed based on the thermal network topology corresponding to the vehicle's electric drive system is constructed using Python's TenserFlow.

[0046] The first moment may be a historical moment or a current moment, and the second moment may be any moment after the first moment.

[0047] In the embodiment of the present application, a temperature prediction model is used to predict the temperature value of the component to be tested at the second moment according to the operating data, the first temperature value and the second temperature value, and obtain the predicted temperature value, wherein the temperature prediction model is pre-trained, and the corresponding relationship between the operating data, the first temperature value and the second temperature value and the predicted temperature value has been learned. As an implementation method, the target temperature compensation value is determined according to the correction parameters of the temperature prediction model, the first temperature value, the second temperature value and the operating data, and the first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value. Among them, the temperature prediction model learns the most complete and optimal correction parameters during the training process, and predicts the temperature value based on the correction parameters, thereby realizing accurate temperature prediction without adding hardware, and then, based on the accurate temperature data, the control strategy of the motor is adjusted in time, thereby improving the performance and safety of the vehicle operation.

[0048] In the temperature determination method of the embodiment of the present application, the operating data of the electric drive system of the vehicle at the first moment, the first temperature value of the component to be measured in the thermal network topology corresponding to the electric drive system, and the second temperature value of the first component that has heat exchange with the component to be measured are obtained, and a temperature prediction model is used to determine the predicted temperature value of the component to be measured at a second moment after the first moment based on the operating data, the first temperature value, and the second temperature value. In the process of predicting the temperature value of the component to be measured by the temperature prediction model, the temperature value and operating data of the first component that has heat exchange with the component to be measured are taken into account, thereby improving the accuracy of the temperature prediction of the component to be measured.

[0049] Based on the above embodiments, Figure 2 A flow chart of another temperature determination method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method comprises the following steps:

[0050] Step 201, obtaining the operating data of the electric drive system of the vehicle at the first moment, the first temperature value of the component to be tested in the thermal network topology corresponding to the electric drive system, and the second temperature value of the first component that has heat exchange with the component to be tested.

[0051] In the embodiment of the present application, the electric drive system is a motor drive system, including a three-in-one electric drive system, which includes a permanent magnet synchronous motor and its controller, and a reducer. Figure 3 A schematic diagram of the structure of an electric drive system provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the system includes a permanent magnet motor, a motor controller, a reducer, bearings, an oil pan and controller cooling water. The system generates severe heat and has poor heat dissipation conditions. It is necessary to monitor the temperature of key components such as the motor rotor, bearings and reducer in real time to ensure that the key components operate within a safe temperature range and to improve the operating life and reliability of the electric drive.

[0052] As an example, Figure 4 The schematic diagram of the structure of the thermal network topology corresponding to the electric drive system provided in the embodiment of the present application includes multiple nodes, for example, 7 nodes, and the node corresponds to a component or a heat conduction element in the motor drive system, including a stator, a rotor, a bearing, a reducer, oil, cooling water, and the environment. Figure 4 As shown, Csttr is the stator heat capacity of the motor, Rsttr_rtr is the thermal resistance between the stator and the rotor, Rsttr_oil is the thermal resistance between the stator and the cooling oil, Crtr is the rotor heat capacity of the motor, Rrtr_bear is the thermal resistance between the rotor and the bearing, Rrtr_oil is the thermal resistance between the rotor and the cooling oil, Cbear is the bearing heat capacity, Rbear_gear is the thermal resistance between the bearing and the reducer, Rbear_oil is the thermal resistance between the bearing and the cooling oil, Cgear is the reducer heat capacity, Rgear_oil is the thermal resistance between the reducer and the cooling oil, Rgear_air is the thermal resistance between the reducer and the environment, Coil is the cooling oil heat capacity, Rair_oil is the thermal resistance between the environment and the cooling oil, Roil_coolant is the thermal resistance between the coolant and the cooling oil, Tair, Toil and Tcoolant are the temperature information of the ambient temperature, cooling oil and coolant respectively.

[0053] As an example, the nodes in the thermal network topology are also called thermal nodes because they have the ability to store heat. As an example, the first node that has heat exchange with the node to be tested can be a node that has direct heat exchange with the node to be tested. Among them, the node with direct heat exchange means that there is a thermal resistance between the two nodes, that is, a node can reach another node after passing through a thermal resistance, then it is considered that there is direct heat exchange between the two nodes, and the temperature between the nodes with direct heat exchange has a greater impact. Figure 4 As shown, Crtr is the node of the rotor heat capacity of the motor, referred to as the rotor node, Csttr is the node of the stator heat capacity of the motor, referred to as the stator node, and the rotor node is connected to the stator node through the thermal resistance Rsttr_rtr, so that there is a direct heat exchange between the rotor node and the stator node. Since a node in the thermal network topology corresponds to a component or element in the electric drive system, a temperature prediction model is constructed based on the thermal network topology, that is, the parameter setting of the temperature prediction model takes into account the heat transfer relationship between nodes in the thermal network topology, not only the correction parameters corresponding to the thermal resistance between nodes, but also the correction parameters related to the vehicle operation data, which improves the rationality and reliability of the parameter setting.

[0054] Step 202: determining each temperature difference value according to the first temperature value and each second temperature value.

[0055] Among them, there are multiple first components.

[0056] In the embodiment of the present application, in the electric drive system, for the component to be tested, there is at least one first component with heat exchange. Usually, there are multiple first components. In this embodiment, multiple first components are used as an example for explanation.

[0057] For each second temperature value of the first component, the difference between the first temperature value and the second temperature value is used as the corresponding temperature difference value to obtain a plurality of temperature difference values.

[0058] Step 203: determining target operating data associated with the component to be tested from the operating data.

[0059] In the embodiment of the present application, the component to be tested is any component in the motor drive system. Different components may have different associated target operating data. The target operating data associated with the component to be tested refers to operating data that is highly correlated with the temperature change of the component to be tested. For example, if the component to be tested is a rotor, the corresponding target operating data includes speed, torque, current, and cooling oil flow. If the component to be tested is a coolant, the corresponding target operating data includes cooling oil flow.

[0060] Step 204 , determining a target temperature compensation value according to the correction parameters of the temperature prediction model, a plurality of temperature differences and the target operation data.

[0061] Among them, the correction parameters of the temperature prediction model are learned by the model during the training process. The model takes into account the influence of thermal resistance between components and flow rate on the temperature change of components in the thermal network topology. It also considers the influence of operating data related to temperature rise, so that the parameters of the trained model have higher temperature prediction accuracy.

[0062] Among them, different components correspond to different target operating data. Different methods are used to determine the target temperature compensation value according to different target operating data. The specific description is as follows:

[0063] In one scenario, if the target operation data associated with the component to be tested includes liquid flow, it means that the component to be tested is a liquid type component or a non-liquid type component. Liquid type components are, for example, cooling oil or coolant, and non-liquid type components include rotors, bearings, and reducers. Among them, cooling oil is used to cool the motor, and coolant is used to cool the controller. As an implementation method, a first temperature difference is determined from multiple temperature differences, wherein the first temperature difference is determined based on the liquid component included in the multiple first components, that is, the multiple first components include liquid components, such as cooling oil components.

[0064] Then, a first correction parameter related to the first temperature difference is determined from the correction parameters, and a second correction parameter related to the second temperature difference is determined, wherein the second temperature difference is a temperature difference other than the first temperature difference among the multiple temperature differences, wherein, since the first temperature difference is the temperature difference between the component to be tested and the liquid component, the first temperature difference will also be affected by the liquid flow rate in the operating data, wherein the greater the liquid flow rate, the faster the heat generated by the liquid flow is transferred, therefore, for the component to be tested, when the first component in which heat exchange exists includes a liquid component, the liquid flow rate of the liquid component needs to be considered to improve the accuracy of the correction of the first temperature difference. The second temperature difference is the temperature difference between the component to be tested and the non-liquid component, and the influence of the liquid flow rate does not need to be considered.

[0065] Then, the first temperature compensation value is determined according to the first correction parameter, the first temperature difference and the liquid flow obtained by model training, so that the influence of the flow of the cooling medium on the temperature change is taken into account in the process of temperature prediction, thereby improving the accuracy. Among them, the first correction parameter includes the temperature difference correction coefficient and the flow correction coefficient between the components. Among them, the temperature difference correction coefficient indicates the degree of influence of the temperature difference between the node to be measured and the node with heat exchange on the temperature after being transferred through the thermal resistance. The flow correction coefficient is used to make corrections when the liquid flow does not meet the set requirements, such as when it is greater than the set value, so as to converge the influence on the temperature.

[0066] And according to the second correction parameter and the second temperature difference, a second temperature compensation value is determined, wherein the second correction parameter includes a temperature difference correction coefficient between components. It should be noted that if there is a large temperature difference in the second temperature difference, the second correction parameter also includes an exponential correction coefficient, which is also used to correct the temperature difference.

[0067] Finally, the target temperature compensation value is determined according to the first temperature compensation value and the second temperature compensation value. As an implementation method, the target temperature compensation value is determined according to the sum of the first temperature compensation value and the second temperature compensation value.

[0068] As an example, the components to be detected are cooling oil or coolant components in a thermal network.

[0069] The component to be tested is cooling oil, and the target temperature compensation value of cooling oil is ΔT oil Determined using the following formula:

[0070] ΔT oil =[W coolantoil *(T coolant -T oil ) / Flow oil +W airoil *(T air -T oil )]*dt;

[0071] Among them, W coolantoil is the temperature difference correction factor between the coolant and the cooling oil, T oil is the temperature of the cooling oil at the first moment, T coolant is the temperature of the coolant at the first moment, W airoil is the temperature difference correction factor between the environment and the cooling oil, T air is the temperature of the environment at the first moment, Flow oil Liquid flow rate of cooling oil.

[0072] If the component to be tested is a coolant, the target temperature compensation value of the coolant is ΔT coolant Determined using the following formula:

[0073] ΔT coolant =[W oilcoolant *(T oil -T coolant ) / Flow coolant ]*dt;

[0074] Among them, Flow coolant is the liquid flow rate of the coolant, W oilcoolant Correction factor for the temperature difference between coolant and cooling oil.

[0075] In the second scenario, the target operating data also includes motor operating data, including speed, torque, current, etc., and operating parameters of the motor related to the motor operating conditions. The motor operating data also has an impact on the temperature prediction of the component to be tested. Taking this into account when making temperature predictions can improve the accuracy of the prediction. That is to say, in this scenario, it is necessary to consider the impact of the temperature of the first component that has heat exchange with the component to be tested on the component to be tested, as well as the impact of the liquid flow and motor operating data on the temperature of the component to be tested, which characterizes the real-time operating losses of different components and improves the accuracy of the prediction.

[0076] As an implementation method, the first temperature compensation value and the second temperature compensation value are determined according to the method for determining the first temperature compensation value and the second temperature compensation value described in the first scenario, and then, a third correction parameter related to the motor operation data is determined from the correction parameters, and the third temperature compensation value is determined based on the third correction parameter and the motor operation data, and the target temperature compensation value is determined based on the first temperature compensation value, the second temperature compensation value and the third temperature compensation value.

[0077] It should be understood that in the motor drive system, if the component corresponding to the component to be tested is a non-liquid component, such as a rotor, a bearing, and a reducer, that is, if the component to be tested is a non-liquid component, the temperature change is usually affected by the motor operation data. Therefore, for non-liquid components, the target temperature compensation value determination method of the second scenario will be used.

[0078] As an example, the components to be tested are a rotor, a bearing, and a reducer.

[0079] For the rotor, as an implementation method, the corresponding target temperature compensation value ΔT rotor This is determined in the following way:

[0080]

[0081] Among them, T rotor is the temperature of the rotor at the first moment, T stator is the temperature of the stator at the first moment, T bear is the bearing temperature at the first moment, T oil is the temperature of the cooling oil at the first moment, Flow oil is the liquid flow rate of the cooling oil, wherein the first correction parameter includes the temperature difference correction coefficient W between the rotor and the cooling oil oilrtr Flow correction factor Pow corresponding to the rotor rtroil The second correction parameter includes the temperature difference correction coefficient W between the rotor and the stator sttrrtr , the temperature difference correction factor W between the rotor and the bearing bearrtr, and an exponential correction factor λ for the temperature difference between the bearing and the rotor bearrtr .

[0082] The third correction parameter includes the rotor current correction coefficient Fac rtrcur , rotor power correction factor Fac rtrpower and the rotor speed correction factor Fac rtrspeed , T q is the torque of the motor, n is the speed of the motor, i s is the current of the motor. Among them, the exponential correction coefficient λ bearrtr And flow correction factor Pow rtroil They are all values ​​greater than 0 and less than or equal to 1, and participate in the calculation in exponential form. When the temperature difference or flow rate is too large, it is used to converge the influence of temperature or flow rate to improve the accuracy of determining the temperature compensation value.

[0083] For the components corresponding to the bearing, as an implementation method, the corresponding target temperature compensation value ΔT bear This is determined in the following way:

[0084]

[0085] Among them, T rotor is the temperature of the rotor at the first moment, T gear is the temperature of the reducer at the first moment, T bear is the bearing temperature at the first moment, T oil is the temperature of the cooling oil at the first moment, Flow oil is the liquid flow rate of the cooling oil, wherein the first correction parameter includes the temperature difference correction coefficient W between the bearing and the cooling oil oilbear Flow correction factor Pow corresponding to the bearing bearoil The second correction parameter includes the temperature difference correction coefficient W between the reducer and the bearing gearbear , the temperature difference correction factor W between the rotor and the bearing rtrbear , and an exponential correction factor λ for the temperature difference between the rotor and the bearing rtrbear The third correction parameter includes the current correction coefficient Fac corresponding to the bearing bearcur , Power correction factor Fac corresponding to the bearing bearpower The speed correction factor Fac corresponding to the bearing bearpeed , T q is the torque of the motor, n is the speed of the motor, i s is the motor current.

[0086] Among them, the exponential correction coefficient λ rtrbear and flow correction factor Pow bearoilThey are all values ​​greater than 0 and less than or equal to 1, and participate in the calculation in exponential form. When the temperature difference or flow rate is too large, it is used to converge the influence of temperature or flow rate to improve the accuracy of determining the temperature compensation value.

[0087] For the components corresponding to the reducer, as an implementation method, the corresponding target temperature compensation value ΔT bear This is determined in the following way:

[0088]

[0089] Among them, T gear is the temperature of the reducer at the first moment, T air is the ambient temperature at the first moment, T bear is the bearing temperature at the first moment, T oil is the temperature of the cooling oil at the first moment, Flow oil is the liquid flow rate of the cooling oil, wherein the first correction parameter includes the temperature difference correction coefficient W between the cooling oil and the reducer oilgear Flow correction factor Pow corresponding to the reducer gearoil , the second correction parameter includes W beargear and W rtrbear , where W beargear is the temperature difference correction factor between the bearing and the reducer, W airgear is the temperature difference correction coefficient between the environment and the reducer. The third correction parameter includes the current correction coefficient Fac corresponding to the reducer. gearcur Power correction factor Fac corresponding to the reducer gearpower , T q is the torque of the motor, n is the speed of the motor, i s is the motor current.

[0090] Among them, the flow correction coefficient Pow gearoil It is a value greater than 0 and less than or equal to 1, and participates in the calculation in exponential form. When the flow rate is too large, it is used to converge the influence of the flow rate to improve the accuracy of determining the temperature compensation value.

[0091] As an implementation method, the temperature difference correction coefficient W in the above formula is xy , where xy represents a component with heat exchange, y is the component to be tested, and x is the first component that has heat exchange with the component to be tested y. The implementation principle of determining the temperature difference correction coefficient based on the thermal resistance of the first component x to the component to be tested y and the thermal capacity of the component to be tested y is determined by the following formula as an implementation method:

[0092]

[0093] Among them, Rxy is the thermal resistance of the first component x to the component to be measured y, C y Thermal capacity of component y under test.

[0094] For example, the temperature difference correction factor W between the rotor and the stator in the above formula is sttrrtr For example:

[0095]

[0096] Among them, R sttrrtr is the thermal resistance of the first component stator to the component under test rotor, C rtr Thermal capacity of the rotor of the component under test.

[0097] Step 205: Compensate the first temperature value according to the target temperature compensation value to obtain a predicted temperature value.

[0098] In one implementation of the embodiment of the present application, the sum of the first temperature value and the target temperature difference is used as the predicted temperature value.

[0099] As an example, taking the node to be tested as a rotor, the predicted temperature value T of the rotor rotor_next For illustration, the principle of the method for determining the predicted temperature of other nodes to be measured is the same and will not be repeated here.

[0100]

[0101] It should be noted that the temperature prediction model of the present application is used to predict the rotor temperature, which can accurately predict the rotor temperature and improve the continuous operation performance of the rotor. The change trend of the rotor temperature is determined based on the predicted future rotor temperature, so as to realize overheat protection of the rotor and prevent demagnetization. At the same time, the predicted temperature value of the rotor can be input into the torque control unit to compensate and correct the magnetic flux and improve the torque control accuracy. In addition, the temperature of the bearings and reducers is predicted, and the operating conditions and lubrication conditions of the mechanical parts other than the motor in the electric drive are monitored to improve the operating life of the electric drive and reduce the risk of failure.

[0102] In the temperature determination method of the embodiment of the present application, in the process of predicting the temperature value of the component to be measured by the temperature prediction model, the temperature value and operation data of the first component that has heat exchange with the component to be measured are taken into account, thereby improving the accuracy of the temperature prediction of the component to be measured, and realizing the prediction of a single component, and realizing the simultaneous prediction of multiple components. Compared with the traditional thermal network temperature estimation method, the present application does not need to rely on simulation data to directly calculate the thermal resistance value and heat loss between components, and the calculation of thermal resistance value and heat loss has the problem of low accuracy. Instead, the temperature prediction model is constructed based on the thermal network topology, and the main correction parameters of the model are predefined. The correction parameters include temperature difference correction parameters, flow correction parameters and correction parameters of operation data. The correction parameters of the model are optimized through iterative training, and the temperature value is predicted based on the optimal correction parameters. At the same time, the iterative optimization algorithm consumes less computing power resources, greatly reducing the time, cost and difficulty under the premise of ensuring the prediction accuracy, and the prediction result is more objective and the working condition coverage is higher.

[0103] Based on the above embodiments, the present application provides a method for training a temperature prediction model. Figure 5 A flow chart of another method for training a temperature prediction model provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the method comprises the following steps:

[0104] Step 501: Obtain training samples.

[0105] The training samples include operating data of the electric drive system of the vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component.

[0106] Step 502: Input the operating data, the first temperature value and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment.

[0107] In one implementation of the embodiment of the present application, the second node is one, and the temperature difference is determined according to the first temperature value and the second temperature value, and the target operating data associated with the first component is determined from the operating data, and the target temperature compensation value is determined according to the correction parameters of the temperature prediction model, the temperature difference and the target operating data, wherein the method for determining the target temperature compensation value can refer to the relevant explanations in the aforementioned embodiment, and the principle is the same, and will not be repeated here. Then, the first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value of the first component.

[0108] In another implementation of the embodiment of the present application, there are multiple second nodes, and each temperature difference is determined according to the first temperature value and each second temperature value, and the target operating data associated with the first component is determined from the operating data. The target temperature compensation value corresponding to the first component is determined according to the correction parameters of the temperature prediction model, multiple temperature differences and target operating data, and the first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value of the first component at the second moment. Among them, the correction parameters include parameters for correcting the operating data, and also include parameters for correcting the temperature difference between components, so as to take into account the change of thermal resistance between certain components with the temperature difference, and at the same time take into account the real-time operating loss of the components, and the model has higher accuracy.

[0109] Therefore, the temperature prediction model of the present application can be trained based on samples of a single component or based on samples of multiple components during the model training process, so that the trained temperature prediction model can realize the prediction of a single component or the simultaneous prediction of multiple components.

[0110] The relevant explanations in the aforementioned embodiments are also applicable to step 501 and step 502, and the principles are the same, which will not be repeated here.

[0111] Step 503: training the temperature prediction model according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

[0112] In an embodiment of the present application, temperature difference information is determined based on the predicted temperature value and the marked actual temperature value, a loss function is determined based on the temperature difference information, and a temperature prediction model is trained based on the loss function to obtain a trained temperature prediction model.

[0113] The first component may be one or more, and the loss function J for each first component is MESLoss Determined by the following formula:

[0114]

[0115] Among them, N is the number of training samples. is the predicted temperature of the first node, T i is the actual temperature of the first node, which is measured under laboratory conditions.

[0116] When there are multiple first components, the loss functions of the multiple first components are added together to obtain the target loss function.

[0117] Select a single training sample data and input it into the target loss function, calculate the gradient in real time according to the following formula, and update the parameter optimization result: In the formula, ω k+1is the currently optimized parameter, ω k is the parameter obtained by the previous optimization of the model, x i is the running data, α is the learning rate, is the set value, is the gradient of the target loss function with respect to the parameters.

[0118] It should be noted that the operating data of the vehicle's electric drive system can be operating data under any working condition. The aforementioned steps 501 to 503 need to be repeated multiple times, and different training samples can be used each time. Different training samples correspond to different working conditions, thereby covering the operating data under different working conditions, increasing the diversity of training samples, and making the trained model applicable to various working conditions. Then, when the loss function is less than the threshold, the training is stopped, or, when the number of repeated executions is greater than the threshold, the training is stopped. The temperature prediction model obtained after the last model parameter adjustment is used as the trained temperature prediction model. The obtained offline optimized parameters are written into the motor controller, so that the temperature of the motor rotor, bearings, reducer (or reducer box) and other components of the electric drive system can be accurately predicted in real time during the operation of the motor in any working condition. At the same time, the motor control strategy can be adjusted in time according to the temperature information to improve the performance of the vehicle.

[0119] In the training method of the temperature prediction model of the embodiment of the present application, a temperature prediction model of a high-performance thermal network topology of multiple nodes consisting of a stator, a rotor, a bearing, a reducer, oil, cooling water, and ambient temperature is established. Under laboratory conditions, the temperature values ​​of multiple nodes of the electric drive system under different typical operating conditions and the current, speed, torque, oil flow rate and cooling water flow rate changes of the permanent magnet motor at this time are collected, and the measured operating data and temperature data are input into the thermal network model. By performing random gradient optimization on the relevant coefficients such as thermal resistance and operating parameter weights in the thermal network model, the mean square error loss objective function consisting of the measured temperature and the predicted temperature is minimized, the optimal model parameters are identified, and the training effect of the model is improved.

[0120] In order to implement the above embodiment, the embodiment of the present application also proposes a temperature determination device.

[0121] Figure 6 A schematic diagram of the structure of a temperature determination device provided in an embodiment of the present application.

[0122] like Figure 6 As shown, the device may include:

[0123] The acquisition module 61 is used to acquire the operating data of the electric drive system of the vehicle at a first moment, the first temperature value of the component to be tested in the electric drive system, and the second temperature value of the first component that has heat exchange with the component to be tested.

[0124] The determination module 62 is used to determine a predicted temperature value of the component to be tested at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value by using a temperature prediction model.

[0125] Furthermore, in an implementation of the embodiment of the present application, the determination module 62 is further configured to:

[0126] determining a target temperature compensation value according to the correction parameter of the temperature prediction model, the first temperature value, the second temperature value and the operating data;

[0127] The first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value.

[0128] In an implementation of the embodiment of the present application, the first component is multiple, and the determination module 62 is further used to:

[0129] determining respective temperature difference values ​​according to the first temperature value and respective second temperature values;

[0130] Determining target operating data associated with the component under test from the operating data;

[0131] The target temperature compensation value is determined according to the correction parameter of the temperature prediction model, a plurality of temperature difference values ​​and the target operation data.

[0132] In one implementation of the embodiment of the present application, the target operation data includes liquid flow rate, and the determination module 62 is further configured to:

[0133] Determining a first temperature difference from the plurality of temperature differences; wherein the first temperature difference is determined based on a liquid component included in the plurality of first components;

[0134] Determining a first correction parameter related to the first temperature difference from the correction parameters, and determining a second correction parameter related to a second temperature difference; wherein the second temperature difference is a temperature difference other than the first temperature difference among the plurality of temperature differences;

[0135] determining a first temperature compensation value according to the first correction parameter, the first temperature difference and the liquid flow rate;

[0136] determining a second temperature compensation value according to the second correction parameter and the second temperature difference;

[0137] The target temperature compensation value is determined according to the first temperature compensation value and the second temperature compensation value.

[0138] In an implementation of the embodiment of the present application, the target operation data also includes motor operation data, and the determination module 62 is further configured to:

[0139] determining a third correction parameter related to the motor operation data from the correction parameters;

[0140] Determining a third temperature compensation value according to the third correction parameter and the motor operation data;

[0141] The target temperature compensation value is determined according to the first temperature compensation value, the second temperature compensation value, and the third temperature compensation value.

[0142] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and will not be repeated here.

[0143] In the temperature determination device of the embodiment of the present application, the operating data of the electric drive system of the vehicle at the first moment, the first temperature value of the component to be measured in the thermal network topology corresponding to the electric drive system, and the second temperature value of the first component that has heat exchange with the component to be measured are obtained, and a temperature prediction model is used to determine the predicted temperature value of the component to be measured at a second moment after the first moment based on the operating data, the first temperature value, and the second temperature value. In the process of predicting the temperature value of the component to be measured by the temperature prediction model, the temperature value and operating data of the first component that has heat exchange with the component to be measured are taken into account, thereby improving the accuracy of the temperature prediction of the component to be measured.

[0144] In order to implement the above embodiment, the embodiment of the present application also proposes a training device for a temperature prediction model.

[0145] Figure 7 A schematic diagram of the structure of a temperature prediction model training device provided in an embodiment of the present application.

[0146] like Figure 7 As shown, the device may include:

[0147] The acquisition module 71 is used to acquire training samples; wherein the training samples include operating data of the electric drive system of the vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component.

[0148] The determination module 72 is used to input the operating data, the first temperature value and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment.

[0149] The training module 73 is used to train the temperature prediction model according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

[0150] In an implementation of the embodiment of the present application, the second component is multiple, and the determination module 72 is further used to:

[0151] determining respective temperature difference values ​​according to the first temperature value and respective second temperature values;

[0152] determining target operating data associated with the first component from the operating data;

[0153] Determining a target temperature compensation value corresponding to the first component according to the correction parameter of the temperature prediction model, a plurality of temperature difference values ​​and the target operation data;

[0154] The first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value.

[0155] In one implementation of the embodiment of the present application, the training module 73 is further configured to:

[0156] Determining temperature difference information according to the predicted temperature value and the marked actual temperature value;

[0157] Determining a loss function according to the temperature difference information;

[0158] The temperature prediction model is trained according to the loss function to obtain a trained temperature prediction model.

[0159] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and will not be repeated here.

[0160] In the training device of the temperature prediction model of the embodiment of the present application, a temperature prediction model of a high-performance thermal network topology of multiple nodes consisting of a stator, a rotor, a bearing, a reducer, oil, cooling water, and ambient temperature is established. Under laboratory conditions, the temperature values ​​of multiple nodes of the electric drive system under different typical operating conditions and the current, speed, torque, oil flow rate and cooling water flow rate changes of the permanent magnet motor at this time are collected, and the measured operating data and temperature data are input into the thermal network model. By performing random gradient optimization on the relevant coefficients such as thermal resistance and operating parameter weights in the thermal network model, the mean square error loss objective function consisting of the measured temperature and the predicted temperature is minimized, the optimal model parameters are identified, and the training effect of the model is improved.

[0161] In order to implement the above embodiments, the present application also proposes a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the above method embodiments is implemented.

[0162] In order to implement the above embodiments, the present application also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the above method embodiments is implemented.

[0163] In order to implement the above embodiments, the present application also proposes a computer program product on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiments is implemented.

[0164] Figure 8 600 is a block diagram of a vehicle provided in an embodiment of the present application. For example, vehicle 600 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. Vehicle 600 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0165] Reference Figure 8 , the vehicle 600 may include various subsystems, for example, an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 600 may be interconnected by wire or wireless means.

[0166] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, among others.

[0167] The perception system 620 may include several sensors for sensing information about the environment around the vehicle 600. For example, the perception system 620 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0168] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0169] The drive system 640 may include components that provide powered motion for the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0170] Some or all functions of the vehicle 600 are controlled by a computing platform 650. The computing platform 650 may include at least one processor 651 and a memory 652, and the processor 651 may execute instructions 653 stored in the memory 652.

[0171] The processor 651 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0172] The memory 652 may be implemented by any type of volatile or nonvolatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0173] In addition to the instructions 653 , the memory 652 may also store data, such as road maps, route information, and data such as the location, direction, and speed of the vehicle. The data stored in the memory 652 may be used by the computing platform 650 .

[0174] In the embodiment of the present disclosure, the processor 651 may execute instruction 653 to complete all or part of the steps of the above method embodiment.

[0175] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0176] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0177] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0179] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0180] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0181] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0182] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A temperature determination method, characterized in that: include: Acquire operation data of an electric drive system of a vehicle at a first moment, a first temperature value of a component to be tested in the electric drive system, and a second temperature value of a first component that has heat exchange with the component to be tested; A temperature prediction model is used to determine a predicted temperature value of the component under test at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value.

2. The method according to claim 1, characterized in that The using a temperature prediction model to determine a predicted temperature value of the component under test at a second moment after the first moment according to the operating data, the first temperature value, and the second temperature value includes: determining a target temperature compensation value according to the correction parameter of the temperature prediction model, the first temperature value, the second temperature value and the operating data; The first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value.

3. The method according to claim 2, characterized in that The first component is multiple, and the target temperature compensation value is determined according to the correction parameter of the temperature prediction model, the first temperature value, the second temperature value and the operation data, including: determining respective temperature difference values ​​according to the first temperature value and respective second temperature values; Determining target operating data associated with the component under test from the operating data; The target temperature compensation value is determined according to the correction parameter of the temperature prediction model, a plurality of temperature difference values ​​and the target operation data.

4. The method according to claim 3, characterized in that The target operation data includes a liquid flow rate; and determining the target temperature compensation value according to the correction parameter of the temperature prediction model, a plurality of temperature differences and the target operation data includes: Determining a first temperature difference from the plurality of temperature differences; wherein the first temperature difference is determined based on a liquid component included in the plurality of first components; Determining a first correction parameter related to the first temperature difference from the correction parameters, and determining a second correction parameter related to a second temperature difference; wherein the second temperature difference is a temperature difference other than the first temperature difference among the plurality of temperature differences; determining a first temperature compensation value according to the first correction parameter, the first temperature difference and the liquid flow rate; determining a second temperature compensation value according to the second correction parameter and the second temperature difference; The target temperature compensation value is determined according to the first temperature compensation value and the second temperature compensation value.

5. The method according to claim 4, characterized in that The target operation data also includes motor operation data; and determining the target temperature compensation value according to the first temperature compensation value and the second temperature compensation value includes: determining a third correction parameter related to the motor operation data from the correction parameters; Determining a third temperature compensation value according to the third correction parameter and the motor operation data; The target temperature compensation value is determined according to the first temperature compensation value, the second temperature compensation value, and the third temperature compensation value.

6. A method for training a temperature prediction model, characterized in that: include: Acquire a training sample; wherein the training sample includes operation data of the electric drive system of the vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component; Inputting the operating data, the first temperature value and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment; The temperature prediction model is trained according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

7. The method according to claim 6, characterized in that The second components are multiple, and the operating data, the first temperature value, and the second temperature value are input into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment, including: determining respective temperature difference values ​​according to the first temperature value and respective second temperature values; determining target operating data associated with the first component from the operating data; Determining a target temperature compensation value corresponding to the first component according to the correction parameter of the temperature prediction model, a plurality of temperature difference values ​​and the target operation data; The first temperature value is compensated according to the target temperature compensation value to obtain the predicted temperature value.

8. The method according to claim 6, characterized in that The temperature prediction model is trained according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model, including: Determining temperature difference information according to the predicted temperature value and the marked actual temperature value; Determining a loss function according to the temperature difference information; The temperature prediction model is trained according to the loss function to obtain a trained temperature prediction model.

9. A temperature determination device, characterized in that: include: an acquisition module, configured to acquire operating data of an electric drive system of a vehicle at a first moment, a first temperature value of a component to be tested in the electric drive system, and a second temperature value of a first component that has heat exchange with the component to be tested; A determination module is used to determine a predicted temperature value of the component to be tested at a second moment after the first moment according to the operating data, the first temperature value and the second temperature value by using a temperature prediction model.

10. A training device for a temperature prediction model, characterized in that: include: An acquisition module, configured to acquire training samples; wherein the training samples include operating data of an electric drive system of a vehicle at a first moment, a first temperature value of a first component in the electric drive system, and a second temperature value of a second component that has heat exchange with the first component; a determination module, configured to input the operating data, the first temperature value, and the second temperature value into a temperature prediction model to obtain a predicted temperature value of the first component at a second moment after the first moment; The training module is used to train the temperature prediction model according to the predicted temperature value and the marked actual temperature value to obtain a trained temperature prediction model.

11. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Implement the method according to any one of claims 1 to 5, or implement the method according to any one of claims 6 to 8.

12. A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal can implement the method according to any one of claims 1 to 5, or implement the method according to any one of claims 6 to 8.

13. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5, or implements the method according to any one of claims 6 to 8.

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