A multi-stage task analysis method and device for electric vehicle drive motor

Abstract

The present application provides a method and device for analyzing multi-stage tasks of an electric vehicle drive motor, the method comprising: determining the basic conditions of the system, the system boundaries of each stage, and the success criteria of each staged task system based on the obtained functional decomposition data and classification data of the electric vehicle drive motor; constructing a GO graph model for each task stage according to the basic conditions of the system and the success criteria; connecting the GO graph model according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system; performing quantitative and qualitative reliability analysis on the virtual GO graph model to obtain quantitative analysis results and qualitative analysis results; outputting the virtual GO graph model, quantitative analysis results, and qualitative analysis results. The method and device can construct a multi-task stage GO graph model based on the GO method and PMS, and perform quantitative and qualitative analysis on the vehicle drive motor based on the model to obtain objective and accurate reliability analysis results.

Description

Technical Field

[0001] The present application relates to the technical field of data analysis, and in particular to a method and device for analyzing multi-stage tasks of a drive motor of an electric vehicle. Background Art

[0002] With the popularity of electric vehicles, the reliability of the drive motor as its core component is directly related to the endurance, power response and driving safety of electric vehicles. Therefore, it is particularly important to accurately model and analyze the reliability of electric vehicle drive motors. However, the currently widely used reliability modeling method based on the fault tree method has mature software support, but its significant defect is that it is highly dependent on the subjective experience of engineers, resulting in poor consistency in the modeling results of different personnel, and thus unable to conduct accurate reliability assessment. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method and device for analyzing the multi-stage tasks of an electric vehicle drive motor, which can construct a virtual GO graph model of the multi-stage tasks, reduce the dependence of the modeling process on personal experience, and improve the consistency and objectivity of model establishment; at the same time, it can perform quantitative and qualitative analysis of reliability, and realize a detailed evaluation of the reliability of the electric vehicle drive motor at each stage during its entire life cycle, providing a scientific basis for optimizing the design, formulating preventive maintenance strategies, and improving the safety and economy of the entire electric vehicle system.

[0004] The first aspect of the present application provides a multi-stage task analysis method for an electric vehicle drive motor, comprising:

[0005] Obtain functional decomposition and classification data of electric vehicle drive motors;

[0006] Determine the basic conditions of the system, the system boundaries of each stage and the success criteria of each stage task system based on the functional decomposition data and the classification data;

[0007] According to the basic conditions of the system and the success criteria, a GO graph model of each task stage is constructed;

[0008] Connecting the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system;

[0009] Performing a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result;

[0010] Performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result;

[0011] The virtual GO graph model, the quantitative analysis results and the qualitative analysis results are output.

[0012] In the above implementation process, this method can apply the "Go Method" (Goal-Oriented Methodology) to the multi-stage task reliability modeling and analysis of electric vehicle drive motors, thereby achieving a detailed evaluation of the reliability of each stage in the entire life cycle of electric vehicle drive motors.

[0013] Furthermore, the determining of the basic conditions of the system, the system boundaries of each stage and the success criteria of each staged task system based on the functional decomposition data and the classification data includes:

[0014] Establishing a functional block diagram and a functional flow chart based on the functional decomposition data and the classification data;

[0015] Analyze the functional block diagram and the functional flow chart to obtain analysis results; wherein the analysis results at least include time analysis results, environment analysis results, task definition results and fault criterion analysis results;

[0016] Determine the basic conditions of the system according to the analysis results; wherein the basic conditions of the system at least include system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria;

[0017] Determine the system boundaries of each stage and the success criteria of each stage task system; wherein the system boundaries of each stage include at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operation status of each stage task and the judgment conditions for the normal completion of all stage tasks.

[0018] Furthermore, the GO graph model of each task stage is constructed according to the basic conditions of the system and the success criteria, including:

[0019] Determine the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system based on the analysis results;

[0020] Selecting GO method operators according to the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system;

[0021] According to the basic conditions of the system, the success criteria of each phased task system and the GO method operator, a modular modeling method is used to perform GO method modeling to obtain a GO graph model of each task stage.

[0022] Furthermore, the reliability quantitative analysis of the virtual GO graph model is performed to obtain a quantitative analysis result, including:

[0023] Calculating a component failure probability of one of the components in a mission phase cycle based on the virtual GO graph model;

[0024] Calculate the micro-component failure probability and micro-component reliability of one of the micro-components in the component during the mission phase period according to the component failure probability;

[0025] Calculating the component reliability of the component according to the component failure probability, the microcomponent failure probability and the microcomponent reliability;

[0026] Obtain predefined probability formula operation rules;

[0027] According to the probability formula operation rules and the virtual GO graph model, component cross-stage dependency processing is performed to obtain the probability that the component is in a normal state;

[0028] Calculating reliability parameters of components or subsystems in each stage of the virtual GO graph model according to the probability that the components are in a normal state and the reliability of the components;

[0029] The component failure probability, the micro-component failure probability, the micro-component reliability, the component reliability and the reliability parameters of the components or subsystems in each stage are summarized to obtain a quantitative analysis result.

[0030] Furthermore, after the GO graph model is connected according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system, and before the reliability quantitative analysis of the virtual GO graph model is performed to obtain the quantitative analysis result, the method further includes:

[0031] Defining the state representation of the operator corresponding to the component in the virtual GO graph model;

[0032] Defining the state probability of the operator corresponding to the component in the virtual GO graph model according to the state representation;

[0033] A probability formula operation rule for processing cross-stage dependencies of components in the virtual GO graph model is constructed according to the state probability.

[0034] Furthermore, the reliability qualitative analysis of the virtual GO graph model is performed to obtain a qualitative analysis result, including:

[0035] Determine the system GO graph sub-model of each task stage in the multi-stage task system according to the virtual GO graph model;

[0036] Perform multiple GO operations according to the GO graph sub-model and a preset basic GO method qualitative analysis algorithm to obtain a subsystem-level minimum cut set;

[0037] Determine a subsystem-level GO graph of a functionally equivalent module according to the system GO graph submodel;

[0038] Performing GO method qualitative analysis on the subsystem-level GO graph to obtain the corresponding component-level minimum cut set;

[0039] Performing Boolean algebra operations on the corresponding component-level minimum cut sets or the subsystem-level minimum cut sets to obtain Boolean algebra operation results;

[0040] Performing system cut set simplification processing according to the Boolean algebra operation result and the preset stage algebraic rule to obtain a target minimum cut set; wherein the target minimum cut set includes a target component-level minimum cut set or a target subsystem-level minimum cut set;

[0041] The target minimum cut set is analyzed by using the basic GO method qualitative analysis algorithm to obtain first-order cut set data, second-order cut set data, third-order cut set data and fourth-order cut set data;

[0042] A system weakness analysis is performed based on the first-order cut set data, the second-order cut set data, the third-order cut set data, and the fourth-order cut set data to obtain a qualitative analysis result.

[0043] A second aspect of the present application provides a multi-stage task analysis device for an electric vehicle drive motor, the multi-stage task analysis device for an electric vehicle drive motor comprising:

[0044] An acquisition unit, used for acquiring functional decomposition data and classification data of a driving motor of an electric vehicle;

[0045] A determination unit, configured to determine basic system conditions, system boundaries at each stage, and success criteria for each staged task system based on the functional decomposition data and the classification data;

[0046] A first construction unit is used to construct a GO graph model of each task stage according to the basic conditions of the system and the success criteria;

[0047] A second construction unit is used to connect the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system;

[0048] A quantitative analysis unit, used to perform a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result;

[0049] A qualitative analysis unit, used for performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result;

[0050] An output unit is used to output the virtual GO graph model, the quantitative analysis results and the qualitative analysis results.

[0051] Furthermore, the determining unit includes:

[0052] Establishing a subunit, for establishing a functional block diagram and a functional flow chart based on the functional decomposition data and the classification data;

[0053] A first analysis subunit is used to analyze the functional block diagram and the functional flow chart to obtain analysis results; wherein the analysis results at least include time analysis results, environment analysis results, task definition results and fault criterion analysis results;

[0054] A first determination subunit is used to determine the basic conditions of the system according to the analysis results; wherein the basic conditions of the system at least include system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria;

[0055] The first determination subunit is also used to determine the system boundaries of each stage and the success criteria of each stage task system; wherein the system boundaries of each stage include at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operating status of each stage task and the judgment conditions for the normal completion of all stage tasks.

[0056] Furthermore, the first construction unit includes:

[0057] A second determination subunit is used to determine the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system according to the analysis results;

[0058] A selection subunit is used to select a GO method operator according to the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system;

[0059] The modeling subunit is used to perform GO modeling based on a modular modeling method according to the basic conditions of the system, the success criteria of each phased task system and the GO method operator, so as to obtain a GO graph model of each task stage.

[0060] Furthermore, the quantitative analysis unit comprises:

[0061] A calculation subunit, for calculating a component failure probability of one of the components in a mission phase cycle based on the virtual GO graph model;

[0062] The calculation subunit is further used to calculate the micro-component failure probability and micro-component reliability of one of the micro-components in the component during the mission phase period according to the component failure probability;

[0063] The calculation subunit is further used to calculate the component reliability of the component according to the component failure probability, the micro-component failure probability and the micro-component reliability;

[0064] An acquisition subunit, used to acquire a predefined probability formula operation rule;

[0065] A processing subunit, used for processing component cross-stage dependencies according to the probability formula operation rules and the virtual GO graph model, to obtain the probability that the component is in a normal state;

[0066] The calculation subunit is further used to calculate the reliability parameters of the components or subsystems in each stage of the virtual GO graph model according to the probability that the components are in a normal state and the reliability of the components;

[0067] The summarizing subunit is used to summarize the component failure probability, the micro-component failure probability, the micro-component reliability, the component reliability and the reliability parameters of the components or subsystems in each stage to obtain a quantitative analysis result.

[0068] Furthermore, after the second construction unit connects the GO graph model according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system, and before the quantitative analysis unit performs reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result, the electric vehicle drive motor multi-stage task analysis device further includes:

[0069] A definition unit, used to define the state representation of the operator corresponding to the component in the virtual GO graph model;

[0070] The definition unit is further used to define the state probability of the operator corresponding to the component in the virtual GO graph model according to the state representation;

[0071] The third construction unit is used to construct a probability formula operation rule for processing the cross-stage dependency of components in the virtual GO graph model according to the state probability.

[0072] Furthermore, the qualitative analysis unit comprises:

[0073] A third determining subunit is used to determine the system GO graph submodel of each task stage in the multi-stage task system according to the virtual GO graph model;

[0074] A first operator unit is used to perform multiple GO operations according to the GO graph sub-model and a preset basic GO method qualitative analysis algorithm to obtain a subsystem-level minimum cut set;

[0075] The third determining subunit is further used to determine the subsystem-level GO graph of the functional equivalent module according to the system GO graph submodel;

[0076] The second analysis subunit is used to perform GO method qualitative analysis on the subsystem-level GO graph to obtain the corresponding component-level minimum cut set;

[0077] A second operator unit is used to perform Boolean algebra operations on the corresponding component-level minimum cut set or the subsystem-level minimum cut set to obtain a Boolean algebra operation result;

[0078] A simplification subunit, used for performing system cut set simplification processing according to the Boolean algebraic operation result and a preset stage algebraic rule to obtain a target minimum cut set; wherein the target minimum cut set includes a target component-level minimum cut set or a target subsystem-level minimum cut set;

[0079] The third analysis subunit is used to analyze the target minimum cut set by using the basic GO method qualitative analysis algorithm to obtain first-order cut set data, second-order cut set data, third-order cut set data and fourth-order cut set data;

[0080] The third analysis subunit is further used to perform system weakness analysis based on the first-order cut set data, the second-order cut set data, the third-order cut set data and the fourth-order cut set data to obtain a qualitative analysis result.

[0081] The third aspect of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the multi-stage task analysis method for an electric vehicle drive motor as described in any one of the first aspect of the present application.

[0082] The fourth aspect of the present application provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the multi-stage task analysis method for an electric vehicle drive motor described in any one of the first aspect of the present application is executed.

[0083] The beneficial effects of the present application are: it is possible to construct a multi-mission phase GO graph model based on the goal-oriented method (GO method) and the multi-phase mission system (PMS, Phased-Mission Systems), and to conduct quantitative and qualitative analysis of the vehicle drive motor based on the model to obtain objective and accurate reliability analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A schematic flow chart of a multi-stage task analysis method for an electric vehicle drive motor provided in an embodiment of the present application;

[0086] Figure 2 A partial flow chart of another method for analyzing multi-stage tasks of a drive motor of an electric vehicle provided in an embodiment of the present application;

[0087] Figure 3 A partial flow chart of another method for analyzing multi-stage tasks of a drive motor of an electric vehicle provided in an embodiment of the present application;

[0088] Figure 4 An algorithm flow chart for obtaining first-order to fourth-order cut sets through basic GO method qualitative analysis provided in an embodiment of the present application;

[0089] Figure 5 A schematic diagram of the structure of a multi-stage task analysis device for an electric vehicle drive motor provided in an embodiment of the present application;

[0090] Figure 6 A schematic structural diagram of another electric vehicle drive motor multi-stage task analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0091] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0092] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0093] Example 1

[0094] Please see Figure 1 , Figure 1 A flowchart of a multi-stage task analysis method for an electric vehicle drive motor provided in this embodiment. The multi-stage task analysis method for an electric vehicle drive motor includes:

[0095] S101. Obtain functional decomposition data and classification data of a driving motor of an electric vehicle.

[0096] S102. Determine the basic conditions of the system, the system boundaries of each stage, and the success criteria of each staged task system based on the functional decomposition data and classification data.

[0097] S103. Construct a GO graph model for each task stage according to the basic conditions and success criteria of the system.

[0098] S104, connecting the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system.

[0099] In this embodiment, the method can apply the goal-oriented methodology (GO method) to the multi-stage task reliability modeling and analysis of the electric vehicle drive motor.

[0100] In this embodiment, the GO method, as a goal-oriented system reliability analysis method, has the ability to handle complex systems with multiple states and time sequences, and can effectively simulate and evaluate the reliability changes of the system in different mission stages, as well as the mutual influence between stages.

[0101] In this embodiment, the method can reveal the potential failure modes of the drive motor under different usage conditions by constructing a sophisticated multi-stage reliability model, quantify the reliability indicators of each stage, and provide a scientific basis for optimizing the design, formulating preventive maintenance strategies, and improving the safety and economy of the entire electric vehicle system.

[0102] S105, performing reliability quantitative analysis on the virtual GO graph model to obtain quantitative analysis results.

[0103] S106, performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result.

[0104] In this embodiment, the qualitative analysis results may include the task reliability of the electric vehicle drive motor system at each task stage and the system task reliability of the entire task stage and its minimum cut set.

[0105] S107, outputting the virtual GO graph model, quantitative analysis results and qualitative analysis results.

[0106] In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, and no limitation is made in this embodiment.

[0107] In this embodiment, the execution subject of the method may also be a smart device such as a smart phone, a tablet computer, etc., which is not limited in this embodiment.

[0108] It can be seen that the implementation of the multi-stage task analysis method for the electric vehicle drive motor described in this embodiment can construct a virtual GO graph model of the multi-stage tasks, reduce the dependence of the modeling process on personal experience, and improve the consistency and objectivity of model establishment; at the same time, it can perform quantitative and qualitative analysis of reliability, and realize a detailed evaluation of the reliability of the electric vehicle drive motor at each stage during its entire life cycle, providing a scientific basis for optimizing the design, formulating preventive maintenance strategies, and improving the safety and economy of the entire electric vehicle system.

[0109] Example 2

[0110] Please see Figure 2 and Figure 3 , Figure 2 This is a schematic diagram of the first half of a multi-stage task analysis method for an electric vehicle drive motor provided in this embodiment. Figure 3 The following is a flow chart of the second half of a multi-stage task analysis method for an electric vehicle drive motor provided in this embodiment. The multi-stage task analysis method for an electric vehicle drive motor includes:

[0111] S201. Obtain functional decomposition data and classification data of a driving motor of an electric vehicle.

[0112] In this embodiment, the data applicable to the method can be the reliability data collected by the transmission laboratory according to different tasks of the electric vehicle drive motor. It can be seen that the method does not need to obtain corresponding data for different individual operating conditions of electric vehicles, thereby greatly reducing the data collection cost and experimental cost.

[0113] S202: Create a function block diagram and a function flow chart based on the function decomposition data and the classification data.

[0114] In this embodiment, the method can perform functional decomposition and classification according to "task stage - function - component or subsystem" to establish a functional block diagram and a functional flow chart.

[0115] In this embodiment, the method can determine the reliability calculation formula of different components or subsystems in the multi-task stage through the functional block diagram and the functional flow chart.

[0116] S203: Analyze the function block diagram and the function flow chart to obtain analysis results.

[0117] In this embodiment, the analysis results at least include time analysis results, environment analysis results, task definition results and fault criterion analysis results.

[0118] S204. Determine basic system conditions based on the analysis results.

[0119] In this embodiment, the basic conditions of the system include at least system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria.

[0120] S205. Determine the system boundaries of each stage and the success criteria of each staged task system.

[0121] In this embodiment, the system boundary of each stage includes at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operation status of each stage task and the judgment conditions for the normal completion of all stage tasks.

[0122] In this embodiment, the success criterion specifically includes a criterion for determining the normal operation status of each phased task and a criterion for all phased tasks to be completed normally.

[0123] S206. According to the analysis results, determine the functional characteristics of each phased task, the characteristics of the phased system components, and the logical relationship between the components and the system.

[0124] S207, selecting a GO method operator according to the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system.

[0125] In this embodiment, GO method operators that meet the requirements are selected to describe the functional characteristics of each phased task, the characteristics of the phased system components, and the logical relationship between the components and the system determined according to the system analysis.

[0126] S208. According to the basic conditions of the system, the success criteria of each phased task system and the GO method operator, a modular modeling method is used to perform GO method modeling to obtain a GO graph model of each task stage.

[0127] S209: Define the state representation of the operator corresponding to the component in the virtual GO graph model.

[0128] S210: Define the state probability of the operator corresponding to the component in the virtual GO graph model according to the state representation.

[0129] S211. Construct a probability formula operation rule for processing cross-stage dependencies of components in a virtual GO graph model according to the state probability.

[0130] S212, connecting the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system.

[0131] In this embodiment, the method adopts the GO method modeling for this type of PMS system based on the modular modeling method.

[0132] In this embodiment, the method establishes a GO graph model of a single task stage according to the concept of "system-function-component or subsystem" for each task stage, and then connects each task stage model in combination with the task stage system boundary to construct a virtual GO graph model of the entire task stage system.

[0133] S213. Based on the virtual GO graph model, calculate the component failure probability of one of the components in the mission phase cycle.

[0134] S214. Calculate the micro-component failure probability and micro-component reliability of one of the micro-components in the component during the mission phase period according to the component failure probability.

[0135] For example, this method can be used for a multi-stage system where the time of any stage of the electric vehicle drive motor is fixed, and the task time interval of task stage i is:

[0136] ;

[0137] The state of a component (component cannot be repaired) at a certain stage i at time t is expressed as , k is the number of system components in this stage.

[0138] Assume that a certain stage in the virtual GO graph model of a multi-stage task system Middle part The failure probability is According to the definition of reliability, its micro components In the task phase cycle The failure probability is :

[0139] (1)

[0140] in, ;

[0141] , is the failure probability of the component at the initial moment of the component mission, which is generally set to 0, that is, the equipment is intact at the initial moment. Reliability It can be expressed as:

[0142] (2)

[0143] S215 . Calculate the component reliability of the component according to the component failure probability, the micro-component failure probability and the micro-component reliability.

[0144] For example, this method can combine equations (1) and (2) with the failure function of component A at task stage j to obtain Middle part The reliability is:

[0145] (3)

[0146] in, ;

[0147] .

[0148] It can be seen from formula (3) that the components In the task phase The reliability is no longer the original component reliability, but the product of the component reliabilities of the previous j stage cycles.

[0149] At this point, assuming that the entire mission success probability of the virtual GO graph model of the electric vehicle drive motor system is , at a certain task stage Middle part The probability of being in a normal state is , the success probability of the system It can be abstractly represented as follows:

[0150] (4)

[0151] Combining equations (3) and (4), we can obtain the reliability parameters of components or subsystems in each stage of the virtual GO graph model: :

[0152] (5)

[0153] It is worth noting that formula (5) is only used to solve the reliability parameters of component operators that are repeatedly used by the system in each task stage, and is not suitable for the calculation of non-repeated component operator parameters.

[0154] S216: Obtain predefined probability formula operation rules.

[0155] S217. According to the probability formula operation rules and the virtual GO graph model, the component cross-stage dependency processing is performed to obtain the probability that the component is in a normal state.

[0156] In this embodiment, the stage dependency commonly exists in the multi-stage tasks of the electric vehicle drive motor, and also has the characteristics of multiple failure modes of components and common cause failure. Therefore, this method has the following constraints in the analysis process:

[0157] (1) The failure modes of different components within a single stage are independent of each other. The failure modes of each component or subsystem are independent of each other, and the dependency only occurs between stages.

[0158] (2) The time of each task phase of the system is fixed, and the failure of each component or subsystem follows an exponential distribution;

[0159] (3) Each component or subsystem cannot be repaired during the entire system mission phase, and has only two states: normal and faulty.

[0160] For example, at different mission stages Components in the virtual GO graph model of the system The state of the corresponding operator is represented as and ,in (1 indicates normal state, 2 indicates fault state). The corresponding state probability can be expressed as:

[0161] (6)

[0162] In the formula, For components The probability of being in a normal state;

[0163] For components The probability of being in a fault state.

[0164] By referring to the stage algebraic rules in the Boolean algebra method and the state combination algorithm of the GO method, a probabilistic formula operation rule suitable for processing the cross-stage dependencies of components in the virtual GO graph model is proposed. This operation rule is applicable to a two-state system, as shown in Table 1.

[0165] Table 1 Probability calculation rules of GO method

[0166]

[0167] In Table 1, the algebraic operation of the component state on the left corresponds to the probability operation of the state on the right.

[0168] When performing GO operation to obtain When, for The corresponding state probability calculations need to be integrated.

[0169] In this embodiment, the GO operation rule is the basis for performing the GO operation. According to the characteristics of the PMS system, the method supplements the GO operation rule as follows:

[0170] ① Starting from the system input operator of the first stage of the task (which can be a virtual type 5 operator with a unit success probability of 1), the success probability of each signal flow in the GO graph model is calculated;

[0171] ② Before processing component reliability parameters, it is necessary to determine the number of stage tasks, the task stage to which the component or subsystem belongs, and its start and end time. For the same component or subsystem in different task stages, its reliability parameters are calculated according to formula (6). For components or subsystems that only appear during a certain stage of the task, their reliability parameters are calculated according to the general method.

[0172] ③ For the GO graph model of a multi-stage task system with a common signal, an improved calculation form based on the precise calculation formula with a common signal or a preset correction algorithm is used for GO calculation. The specific calculation method is determined by the complexity of the system.

[0173] ④ While processing the common signals, the cross-stage dependencies of components or subsystems are processed using the GO method probabilistic calculation method based on stage algebra;

[0174] ⑤ If there are redundant structures of the same components or subsystems in the system, the GO algorithm that considers common cause failures must be used to calculate the parameters of the corresponding operators.

[0175] S218. Calculate reliability parameters of components or subsystems in each stage of the virtual GO graph model according to the probability that the components are in a normal state and the reliability of the components.

[0176] S219, summarizing component failure probability, micro-component failure probability, micro-component reliability, component reliability, and reliability parameters of components or subsystems in each stage to obtain quantitative analysis results.

[0177] S220 , determining a system GO graph sub-model for each task stage in the multi-stage task system according to the virtual GO graph model.

[0178] S221, perform multiple GO operations according to the GO graph sub-model and the preset basic GO method qualitative analysis algorithm to obtain the subsystem level minimum cut set.

[0179] S222. Determine the subsystem-level GO graph of the functionally equivalent module according to the system GO graph submodel.

[0180] S223, perform GO method qualitative analysis on the subsystem-level GO graph to obtain the corresponding component-level minimum cut set.

[0181] S224, performing Boolean algebra operations on corresponding component-level minimum cut sets or subsystem-level minimum cut sets respectively to obtain Boolean algebra operation results.

[0182] S225, performing system cut set simplification processing according to the Boolean algebra operation result and the preset stage algebraic rule to obtain the target minimum cut set.

[0183] In this embodiment, the target minimum cut set includes a target component-level minimum cut set or a target subsystem-level minimum cut set.

[0184] For example, the GO method qualitative analysis steps for the multi-stage mission system of an electric vehicle drive motor are as follows:

[0185] ①According to the GO graph model of each task stage of the multi-stage task system and the basic GO method qualitative analysis method, multiple GO operations are performed to obtain the minimum cut set at the subsystem level;

[0186] ② If necessary, the GO method qualitative analysis can be performed on the subsystem-level GO graph of the functional equivalent module to obtain the corresponding component-level minimum cut set (the virtual input operator is not involved in the analysis);

[0187] ③ Use the logical "OR" to perform Boolean algebra operations on the subsystem-level minimum cut sets or component-level minimum cut sets of each single-mission phase system, and then combine the stage algebra rules to simplify the system cut sets to obtain the component-level or subsystem-level minimum cut sets of the entire multi-stage mission system.

[0188] S226. Use the basic GO method qualitative analysis algorithm to analyze the target minimum cut set to obtain first-order cut set data, second-order cut set data, third-order cut set data, and fourth-order cut set data.

[0189] In this embodiment, the analysis of the minimum cut set generally takes up to the fourth-order cut set. For the system GO graph model of the multi-stage task system in a certain stage cycle or the virtual GO graph model of the multi-stage task system, the algorithm flow chart of obtaining the first-order to fourth-order cut sets through basic GO method qualitative analysis is as follows: Figure 4 shown.

[0190] S227. Analyze the weak links of the system based on the first-order cut set data, the second-order cut set data, the third-order cut set data, and the fourth-order cut set data to obtain qualitative analysis results.

[0191] In this embodiment, the method can use the minimum cut set to determine the weak link of the system.

[0192] In this embodiment, the smaller the order of the minimum cut set of a system in a certain task stage, the greater the probability of the cut set occurring, the higher the importance of the cut set, and the cut set includes the components or subsystems corresponding to the bottom events, that is, the weak links of the system in this task stage.

[0193] In this embodiment, the more times a certain minimum cut set appears in different task stages, the more likely it is that the bottom events included in the cut set occur in each task stage and cause system failure, and the component or subsystem that generates the bottom event of the cut set is the weak link of the system.

[0194] S228, outputting the virtual GO graph model, quantitative analysis results and qualitative analysis results.

[0195] In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, and no limitation is made in this embodiment.

[0196] In this embodiment, the execution subject of the method may also be a smart device such as a smart phone, a tablet computer, etc., which is not limited in this embodiment.

[0197] In this embodiment, the method uses the reliability data of electric vehicle drive motors collected centrally by the transmission laboratory as the basis for analysis, cleverly avoiding the high cost and complexity of collecting data separately for each individual electric vehicle, greatly reducing the economic burden of R&D and experiments, and improving the breadth and efficiency of data utilization.

[0198] In this embodiment, the method proposes a clear analysis framework for the stage dependency and multi-failure mode characteristics in the multi-stage tasks of electric vehicle drive motors. By assuming the independence of failure modes in a single stage and the dependency between stages, combined with the characteristics of fixed system task time and exponential distribution of failures, a more simplified and targeted analysis model is established, which not only simplifies the analysis process, but also improves the practicality and explanatory power of the model.

[0199] In this embodiment, the method introduces a modular GO (Goal-Oriented) graph modeling method, builds an independent GO graph model for each task stage, and then integrates it into a virtual GO graph model for the entire task cycle through the system boundary definition. This method not only enhances the flexibility and scalability of the model, but also facilitates understanding and adjustment, which is conducive to quickly responding to changes in system design or task requirements.

[0200] In this embodiment, the method proposes a unique set of probability operation rules and processing methods for the cross-stage dependencies of components or subsystems in a multi-stage task system. By combining the stage algebraic rules with the GO method state combination algorithm, the cross-stage influence problem that is easily overlooked in traditional reliability analysis is effectively solved, thereby improving the accuracy of model prediction.

[0201] In this embodiment, the method innovatively combines the qualitative analysis and quantitative analysis of the GO method. It not only obtains the minimum cut set of the system through multiple GO operations and clarifies the weak links of the system, but also accurately calculates the reliability parameters of the system and components through formula derivation (such as equations (3) to (5)), providing solid data support for optimizing design and maintenance strategies.

[0202] In this embodiment, the method supplements and optimizes the GO operation rules, ensuring the calculation accuracy when processing complex multi-stage task systems, especially when dealing with complex scenarios such as common cause failures, redundant structures and common signals, providing more rigorous and efficient algorithm support.

[0203] It can be seen that the implementation of the multi-stage task analysis method for the electric vehicle drive motor described in this embodiment can achieve the following effects:

[0204] (1) Improving the objectivity and consistency of modeling: By using centrally collected reliability data and a clear electric vehicle drive motor system analysis framework, the modeling process is less dependent on personal experience, the consistency and objectivity of model building are improved, and the differences caused by subjective factors in the traditional fault tree method are avoided. In particular, when dealing with large-scale complex systems, the accuracy and stability of the model are significantly enhanced.

[0205] (2) Strengthening the quantitative analysis capability of multi-stage tasks: In view of the limitations of the Monte Carlo simulation method, this method is not only applicable to the reliability modeling of multi-stage tasks of electric vehicle drive motors, but also can deeply analyze the reliability changes within each stage, filling the gap of traditional methods in multi-stage quantitative analysis. Through the modular modeling of the GO method and the cross-stage dependency processing, a detailed evaluation of the reliability of each stage of the electric vehicle drive motor throughout its life cycle is achieved.

[0206] (3) Enhanced description of dynamic behavior and dependency: Compared with the combined model analysis method based on BDD, this method can better describe the multi-stage task dynamic behavior and failure / maintenance behavior dependency of electric vehicle drive motors through the improved GO method probability operation rules and stage algebraic rules. In particular, it improves the accuracy and adaptability of the model when dealing with nonlinear effects and mutations between stages, and is suitable for reliability analysis of larger-scale and more complex task systems.

[0207] (4) Refining the stage transition processing and enhancing the model accuracy: This method overcomes the simplification problem of the previous methods in the multi-stage task stage transition processing of electric vehicle drive motors through detailed stage division and inter-stage dependency processing. By accurately defining the task stages and strictly modeling the state transitions between stages, it ensures that the model can capture the key details in the stage transition and improves the accuracy of the model prediction.

[0208] (5) Adapting to the complexity of multi-stage tasks: In view of the limitations of the "single-stage task" analysis method, this method specially designed a multi-stage task reliability modeling framework that can effectively cope with the complex reliability challenges of electric vehicle drive motors under different working conditions and different life cycle stages. By combining qualitative and quantitative analysis, it not only evaluates the reliability of the entire system task, but also clarifies the reliability status of each stage, providing a powerful tool for reliability analysis of multi-stage task systems.

[0209] (6) Enhanced system design and optimization capabilities: The combination of qualitative and quantitative analysis, especially the determination of the minimum cut set, provides clear indications of weak links in the design of electric vehicle drive motors, helping engineers to quickly locate problems, optimize the design, improve the reliability of the drive motor and its related subsystems, and extend product life.

[0210] Example 3

[0211] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a multi-stage task analysis device for an electric vehicle drive motor provided in this embodiment. Figure 5 As shown, the multi-stage task analysis device for the electric vehicle drive motor includes:

[0212] An acquisition unit 310 is used to acquire function decomposition data and classification data of a driving motor of an electric vehicle;

[0213] A determination unit 320, for determining system basic conditions, system boundaries of each stage and success criteria of each stage task system based on the functional decomposition data and the classification data;

[0214] The first construction unit 330 is used to construct a GO graph model of each task stage according to the basic conditions of the system and the success criteria;

[0215] The second construction unit 340 is used to connect the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system;

[0216] A quantitative analysis unit 350 is used to perform a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result;

[0217] A qualitative analysis unit 360 is used to perform a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result;

[0218] The output unit 370 is used to output the virtual GO graph model, the quantitative analysis results and the qualitative analysis results.

[0219] In this embodiment, the explanation of the multi-stage task analysis device for the electric vehicle drive motor can refer to the description in Example 1 or Example 2, and will not be further elaborated in this embodiment.

[0220] It can be seen that the implementation of the electric vehicle drive motor multi-stage task analysis device described in this embodiment can construct a virtual GO graph model of the multi-stage task, reduce the dependence of the modeling process on personal experience, and improve the consistency and objectivity of model establishment; at the same time, it can perform quantitative and qualitative analysis of reliability, and realize a detailed evaluation of the reliability of the electric vehicle drive motor at each stage during its entire life cycle, providing a scientific basis for optimizing the design, formulating preventive maintenance strategies, and improving the safety and economy of the entire electric vehicle system.

[0221] Example 4

[0222] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a multi-stage task analysis device for an electric vehicle drive motor provided in this embodiment. Figure 6 As shown, the multi-stage task analysis device for the electric vehicle drive motor includes:

[0223] An acquisition unit 310 is used to acquire function decomposition data and classification data of a driving motor of an electric vehicle;

[0224] A determination unit 320, for determining system basic conditions, system boundaries of each stage and success criteria of each stage task system based on the functional decomposition data and the classification data;

[0225] The first construction unit 330 is used to construct a GO graph model of each task stage according to the basic conditions of the system and the success criteria;

[0226] The second construction unit 340 is used to connect the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system;

[0227] A quantitative analysis unit 350 is used to perform a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result;

[0228] A qualitative analysis unit 360 is used to perform a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result;

[0229] The output unit 370 is used to output the virtual GO graph model, the quantitative analysis results and the qualitative analysis results.

[0230] As an optional implementation, the determining unit 320 includes:

[0231] An establishing subunit 321 is used to establish a functional block diagram and a functional flow chart based on the functional decomposition data and the classification data;

[0232] The first analysis subunit 322 is used to analyze the function block diagram and the function flow chart to obtain analysis results; wherein the analysis results at least include time analysis results, environment analysis results, task definition results and fault judgment criterion analysis results;

[0233] The first determination subunit 323 is used to determine the basic conditions of the system according to the analysis results; wherein the basic conditions of the system at least include system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria;

[0234] The first determination subunit 323 is also used to determine the system boundaries of each stage and the success criteria of each stage task system; wherein the system boundaries of each stage include at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operating status of each stage task and the judgment conditions for the normal completion of all stage tasks.

[0235] As an optional implementation, the first construction unit 330 includes:

[0236] The second determination subunit 331 is used to determine the functional characteristics of each phased task, the characteristics of the phased system components, and the logical relationship between the components and the system according to the analysis results;

[0237] A selection subunit 332 is used to select a GO method operator according to the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system;

[0238] The modeling subunit 333 is used to perform GO modeling based on a modular modeling method according to the basic conditions of the system, the success criteria of each phased task system and the GO method operator, so as to obtain a GO graph model of each task stage.

[0239] As an optional implementation, the quantitative analysis unit 350 includes:

[0240] A calculation subunit 351 is used to calculate the component failure probability of one of the components in the mission phase cycle based on the virtual GO graph model;

[0241] The calculation subunit 351 is further used to calculate the micro-component failure probability and the micro-component reliability of one of the micro-components in the component during the mission phase period according to the component failure probability;

[0242] The calculation subunit 351 is further used to calculate the component reliability of the component according to the component failure probability, the micro-component failure probability and the micro-component reliability;

[0243] An acquisition subunit 352 is used to acquire a predefined probability formula operation rule;

[0244] The processing subunit 353 is used to process the component cross-stage dependency according to the probability formula operation rules and the virtual GO graph model to obtain the probability that the component is in a normal state;

[0245] The calculation subunit 351 is further used to calculate the reliability parameters of the components or subsystems in each stage of the virtual GO graph model according to the probability that the components are in a normal state and the reliability of the components;

[0246] The summarizing subunit 354 is used to summarize the component failure probability, micro-component failure probability, micro-component reliability, component reliability, and reliability parameters of components or subsystems in each stage to obtain quantitative analysis results.

[0247] As an optional implementation, after the second construction unit 340 connects the GO graph model according to the system boundary of each stage to construct a virtual GO graph model of the entire task stage system, and before the quantitative analysis unit 350 performs reliability quantitative analysis on the virtual GO graph model to obtain the quantitative analysis result, the electric vehicle drive motor multi-stage task analysis device further includes:

[0248] A definition unit 380, used to define the state representation of the operator corresponding to the component in the virtual GO graph model;

[0249] The definition unit 380 is further used to define the state probability of the operator corresponding to the component in the virtual GO graph model according to the state representation;

[0250] The third construction unit 390 is used to construct a probability formula operation rule for processing the cross-stage dependency of components in the virtual GO graph model according to the state probability.

[0251] As an optional implementation, the qualitative analysis unit 360 includes:

[0252] The third determining subunit 361 is used to determine the system GO graph submodel of each task stage in the multi-stage task system according to the virtual GO graph model;

[0253] The first operator unit 362 is used to perform multiple GO operations according to the GO graph sub-model and the preset basic GO method qualitative analysis algorithm to obtain the subsystem level minimum cut set;

[0254] The third determining subunit 361 is further used to determine the subsystem-level GO graph of the functional equivalent module according to the system GO graph submodel;

[0255] The second analysis subunit 363 is used to perform GO method qualitative analysis on the subsystem level GO graph to obtain the corresponding component level minimum cut set;

[0256] The second operator unit 364 is used to perform Boolean algebra operations on the corresponding component-level minimum cut sets or subsystem-level minimum cut sets to obtain Boolean algebra operation results;

[0257] The simplification subunit 365 is used to perform system cut set simplification processing according to the Boolean algebra operation result and the preset stage algebraic rule to obtain the target minimum cut set; wherein the target minimum cut set includes the target component level minimum cut set or the target subsystem level minimum cut set;

[0258] The third analysis subunit 366 is used to analyze the target minimum cut set by using the basic GO method qualitative analysis algorithm to obtain first-order cut set data, second-order cut set data, third-order cut set data and fourth-order cut set data;

[0259] The third analysis subunit 366 is further used to perform system weakness analysis based on the first-order cut set data, the second-order cut set data, the third-order cut set data, and the fourth-order cut set data to obtain a qualitative analysis result.

[0260] In this embodiment, the explanation of the multi-stage task analysis device for the electric vehicle drive motor can refer to the description in Example 1 or Example 2, and will not be further elaborated in this embodiment.

[0261] It can be seen that the implementation of the electric vehicle drive motor multi-stage task analysis device described in this embodiment can construct a virtual GO graph model of the multi-stage task, reduce the dependence of the modeling process on personal experience, and improve the consistency and objectivity of model establishment; at the same time, it can perform quantitative and qualitative analysis of reliability, and realize a detailed evaluation of the reliability of the electric vehicle drive motor at each stage during its entire life cycle, providing a scientific basis for optimizing the design, formulating preventive maintenance strategies, and improving the safety and economy of the entire electric vehicle system.

[0262] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the multi-stage task analysis method for an electric vehicle drive motor in Embodiment 1 or Embodiment 2 of the present application.

[0263] An embodiment of the present application provides a computer-readable storage medium storing computer program instructions. When the computer program instructions are read and executed by a processor, the multi-stage task analysis method for an electric vehicle drive motor in Embodiment 1 or Embodiment 2 of the present application is executed.

[0264] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a 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 a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0265] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0266] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0267] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0268] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0269] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A multi-stage task analysis method for electric vehicle drive motor, characterized in that: include: Obtain functional decomposition and classification data of electric vehicle drive motors; Determine the basic conditions of the system, the system boundaries of each stage and the success criteria of each stage task system based on the functional decomposition data and the classification data; According to the basic conditions of the system and the success criteria, a GO graph model of each task stage is constructed; Connecting the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system; Performing a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result; Performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result; Outputting the virtual GO graph model, the quantitative analysis results and the qualitative analysis results; Wherein, determining the basic conditions of the system, the system boundaries of each stage and the success criteria of each staged task system based on the functional decomposition data and the classification data includes: Establishing a functional block diagram and a functional flow chart based on the functional decomposition data and the classification data; Analyze the functional block diagram and the functional flow chart to obtain analysis results; wherein the analysis results at least include time analysis results, environment analysis results, task definition results and fault criterion analysis results; Determine the basic conditions of the system according to the analysis results; wherein the basic conditions of the system at least include system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria; Determine the system boundaries of each stage and the success criteria of each stage task system; wherein the system boundaries of each stage include at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operation status of each stage task and the judgment conditions for the normal completion of all stage tasks.

2. The multi-stage task analysis method for electric vehicle drive motor according to claim 1 is characterized in that: The GO graph model of each task stage is constructed according to the basic conditions of the system and the success criteria, including: Determine the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system based on the analysis results; Selecting GO method operators according to the functional characteristics of each phased task, the characteristics of the components of the phased system, and the logical relationship between the components and the system; According to the basic conditions of the system, the success criteria of each phased task system and the GO method operator, a modular modeling method is used to perform GO method modeling to obtain a GO graph model of each task stage.

3. The multi-stage task analysis method for electric vehicle drive motor according to claim 1 is characterized in that: The performing reliability quantitative analysis on the virtual GO graph model to obtain quantitative analysis results includes: Calculating a component failure probability of one of the components in a mission phase cycle based on the virtual GO graph model; Calculate the micro-component failure probability and micro-component reliability of one of the micro-components in the component during the mission phase period according to the component failure probability; Calculating the component reliability of the component according to the component failure probability, the microcomponent failure probability and the microcomponent reliability; Obtain predefined probability formula operation rules; According to the probability formula operation rules and the virtual GO graph model, component cross-stage dependency processing is performed to obtain the probability that the component is in a normal state; Calculating reliability parameters of components or subsystems in each stage of the virtual GO graph model according to the probability that the components are in a normal state and the reliability of the components; The component failure probability, the micro-component failure probability, the micro-component reliability, the component reliability and the reliability parameters of the components or subsystems in each stage are summarized to obtain a quantitative analysis result.

4. The multi-stage task analysis method for electric vehicle drive motor according to claim 1 is characterized in that: After the GO graph models are connected according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system, and before the reliability quantitative analysis of the virtual GO graph model is performed to obtain a quantitative analysis result, the method further includes: Defining the state representation of the operator corresponding to the component in the virtual GO graph model; Defining the state probability of the operator corresponding to the component in the virtual GO graph model according to the state representation; A probability formula operation rule for processing cross-stage dependencies of components in the virtual GO graph model is constructed according to the state probability.

5. The multi-stage task analysis method for electric vehicle drive motor according to claim 1 is characterized in that: The performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result includes: Determine the system GO graph sub-model of each task stage in the multi-stage task system according to the virtual GO graph model; Perform multiple GO operations according to the GO graph sub-model and a preset basic GO method qualitative analysis algorithm to obtain a subsystem-level minimum cut set; Determine a subsystem-level GO graph of a functionally equivalent module according to the system GO graph submodel; Performing GO method qualitative analysis on the subsystem-level GO graph to obtain the corresponding component-level minimum cut set; Performing Boolean algebra operations on the corresponding component-level minimum cut sets or the subsystem-level minimum cut sets to obtain Boolean algebra operation results; Performing system cut set simplification processing according to the Boolean algebra operation result and the preset stage algebraic rule to obtain a target minimum cut set; wherein the target minimum cut set includes a target component-level minimum cut set or a target subsystem-level minimum cut set; The target minimum cut set is analyzed by using the basic GO method qualitative analysis algorithm to obtain first-order cut set data, second-order cut set data, third-order cut set data and fourth-order cut set data; A system weakness analysis is performed based on the first-order cut set data, the second-order cut set data, the third-order cut set data, and the fourth-order cut set data to obtain a qualitative analysis result.

6. A multi-stage task analysis device for an electric vehicle drive motor, characterized in that: The multi-stage task analysis device for the electric vehicle drive motor comprises: An acquisition unit, used for acquiring functional decomposition data and classification data of a driving motor of an electric vehicle; A determination unit, configured to determine basic system conditions, system boundaries at each stage, and success criteria for each staged task system based on the functional decomposition data and the classification data; A first construction unit is used to construct a GO graph model of each task stage according to the basic conditions of the system and the success criteria; A second construction unit is used to connect the GO graph models according to the system boundaries of each stage to construct a virtual GO graph model of the entire task stage system; A quantitative analysis unit, used to perform a reliability quantitative analysis on the virtual GO graph model to obtain a quantitative analysis result; A qualitative analysis unit, used for performing a qualitative reliability analysis on the virtual GO graph model to obtain a qualitative analysis result; An output unit, used to output the virtual GO graph model, the quantitative analysis results and the qualitative analysis results; Wherein, the determining unit includes: Establishing a subunit, for establishing a functional block diagram and a functional flow chart based on the functional decomposition data and the classification data; A first analysis subunit is used to analyze the functional block diagram and the functional flow chart to obtain analysis results; wherein the analysis results at least include time analysis results, environment analysis results, task definition results and fault criterion analysis results; A first determination subunit is used to determine the basic conditions of the system according to the analysis results; wherein the basic conditions of the system at least include system tasks, system functions, working modes, task profiles, environmental profiles, and fault criteria; The first determination subunit is also used to determine the system boundaries of each stage and the success criteria of each stage task system; wherein the system boundaries of each stage include at least the input and output of each stage system, and the success criteria of each stage task system include at least the criteria for the normal operating status of each stage task and the judgment conditions for the normal completion of all stage tasks.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the multi-stage task analysis method for an electric vehicle drive motor according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the multi-stage task analysis method for a drive motor of an electric vehicle according to any one of claims 1 to 5 is executed.

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