CN118052136A

CN118052136A - Method for circularly determining endurance test working conditions of whole vehicle in test field under user association condition

Info

Publication number: CN118052136A
Application number: CN202410151129.6A
Authority: CN
Inventors: 张志飞; 杨永乐; 贺岩松; 徐中明
Original assignee: Chongqing University
Current assignee: Chongqing University
Priority date: 2024-02-01
Filing date: 2024-02-01
Publication date: 2024-05-17

Abstract

The disclosure relates to the technical field of computers, in particular to a method for determining a cycle of a durability test working condition of a whole test field under a user association condition. The method comprises the following steps: constructing a user association model according to the user load data and the test field load data, and carrying out multi-objective solution on the user association model to obtain a non-inferior solution set; multi-index decision is carried out on the non-inferior solution set according to the decision index set, and a cycle solution of the durability test working condition of the whole vehicle in the associated test field is obtained; if the whole vehicle durability test working condition cyclic solution of the association test field does not meet the whole vehicle durability test requirement, carrying out multi-objective solution on the user association model again and/or reconstructing the user association model until the whole vehicle durability test working condition cyclic solution of the association test field meets the whole vehicle durability test requirement, and obtaining the whole vehicle durability test working condition cyclic specification of the test field. The method and the device can improve the accuracy of construction of the user association model and the applicability of the test field whole vehicle durability test working condition cycle obtained by solving.

Description

Method for circularly determining endurance test working conditions of whole vehicle in test field under user association condition

Technical Field

The disclosure relates to the technical field of computers, in particular to a method for determining a cycle of a durability test working condition of a whole test field under a user association condition.

Background

The development of the durability of the whole automobile is an important link in the product research and development process, and the road test of a test field is one of main means for testing the durability and reliability of the whole automobile. However, the existing automobile test field specifications are formulated according to experience, and cannot truly represent the road condition of actual users, so that the failure modes of automobile parts in the test field are greatly different from the failure modes of the automobile parts in actual use of users.

In the related art, with the proposal of an equivalent association model (user association model) of a user-test field, an effective solution is found. However, for the construction and solution computation of the user-associated model, further improvements are still needed.

Disclosure of Invention

The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.

Therefore, a first object of the present disclosure is to provide a method for determining a test field vehicle durability test condition cycle under a user association condition, so as to improve the accuracy of construction of a user association model and the applicability of the test field vehicle durability test condition cycle obtained by solving.

The second purpose of the disclosure is to provide a test field whole vehicle durability test working condition circulation determining device under the user association condition.

A third object of the present disclosure is to propose an electronic device.

A fourth object of the present disclosure is to propose a computer readable storage medium.

A fifth object of the present disclosure is to propose a computer programme product.

To achieve the above objective, an embodiment of a first aspect of the present disclosure provides a method for determining a cycle of a durability test condition of a whole vehicle in a test field under a user-related condition, including:

constructing a user association model according to the user load data and the test field load data, and carrying out multi-objective solution on the user association model to obtain a non-inferior solution set;

performing multi-index decision on the non-inferior solution set according to the decision index set to obtain a cycle solution of the endurance test working condition of the whole vehicle in the associated test field;

And if the cycle solution of the whole vehicle durability test working condition of the association test field does not meet the requirement of the whole vehicle durability test, carrying out multi-objective solution and/or reconstruction on the user association model again until the cycle solution of the whole vehicle durability test working condition of the association test field meets the requirement of the whole vehicle durability test, and obtaining the cycle specification of the whole vehicle durability test working condition of the test field.

Optionally, before the building of the user association model according to the user load data and the test field load data, the method further comprises:

acquiring user working condition road spectrum data and associated test field working condition road spectrum data;

carrying out load spectrum analysis processing on the user working condition road spectrum data to obtain user load data;

And carrying out load spectrum analysis processing on the associated test field working condition road spectrum data to obtain test field load data.

Optionally, the constructing a user association model according to the user load data and the test field load data includes:

Determining user working condition pseudo damage corresponding to each durability test associated channel in the whole vehicle durability test associated channel according to the user load data to obtain a user working condition pseudo damage set;

Determining test field working condition pseudo damage corresponding to each endurance test association channel according to the test field load data to obtain a test field working condition pseudo damage set, wherein the test field working condition pseudo damage comprises test field working condition unit pseudo damage corresponding to each test field working condition combination in the test field working condition combination set;

And taking the circulation times corresponding to each test field working condition combination as an unknown variable, and constructing a user association model according to the user working condition pseudo-damage set, the test field working condition pseudo-damage set and the circulation times.

Optionally, the constructing a user association model according to the user working condition pseudo-damage set, the test field working condition pseudo-damage set and the cycle number includes:

performing full life cycle extrapolation on each user working condition pseudo damage in the user working condition pseudo damage set to obtain a user target mileage pseudo damage set;

And constructing a user association model according to the user target mileage pseudo-damage set, the test field working condition pseudo-damage set and the cycle times.

Optionally, the performing multi-objective solution on the user association model to obtain a non-inferior solution set includes:

Converting the user association model into a multi-objective optimization problem model;

And taking the circulation times as an optimization variable, taking the minimum damage difference between the user target mileage pseudo damage corresponding to each durability test associated channel and the test field working condition pseudo damage as an optimization target, and carrying out multi-target optimization solving on the multi-target optimization problem model to obtain a non-inferior solution set.

Optionally, the performing multi-index decision on the non-inferior solution set according to the decision index set to obtain a cycle solution of the endurance test working condition of the whole vehicle in the association test field, including:

performing index weight analysis on the decision index set to obtain index weight coefficients of each decision index in the decision index set;

constructing a comprehensive evaluation decision matrix according to the decision index set and the non-inferior solution set;

Determining the comprehensive contribution degree corresponding to each non-inferior solution in the non-inferior solution set according to the index weight coefficient and the comprehensive evaluation decision matrix;

And determining a cycle solution of the durability test working condition of the whole vehicle in the association test field according to the comprehensive contribution degree, wherein the cycle solution of the durability test working condition of the whole vehicle in the association test field is the non-inferior solution with the highest comprehensive contribution degree in the non-inferior solution set.

Optionally, the performing index weight analysis on the decision index set to obtain an index weight coefficient of each decision index in the decision index set includes:

Constructing an evaluation matrix according to the decision index set, and carrying out data standardization processing on the evaluation matrix to obtain a standardized evaluation matrix;

Determining a variation coefficient and a conflict quantization index value of each decision index in the decision index set according to the standardized evaluation matrix;

Determining the index information quantity of each decision index according to the variation coefficient and the conflict quantization index value to obtain an index information quantity set corresponding to the decision index set;

And determining an index weight coefficient of each decision index in the decision index set according to the index information quantity set.

Optionally, the determining, according to the index weight coefficient and the comprehensive evaluation decision matrix, a comprehensive contribution degree corresponding to each non-inferior solution in the non-inferior solution set includes:

Regularizing the comprehensive evaluation decision matrix to obtain a comprehensive evaluation decision regular matrix;

Performing matrix weighting on the comprehensive evaluation decision regular matrix according to the index weight coefficient to obtain a comprehensive evaluation decision weighting matrix;

Determining an optimal solution set and a worst solution set corresponding to the comprehensive evaluation decision weighting matrix;

Determining a first Euclidean distance between each non-inferior solution in the non-inferior solution sets and the optimal solution set, and a second Euclidean distance between each non-inferior solution and the worst solution set;

And determining the comprehensive contribution degree corresponding to each non-inferior solution according to the first Euclidean distance and the second Euclidean distance.

Optionally, the method further comprises:

Determining damage matching information and load distribution information corresponding to the cycle solution of the durability test working condition of the whole vehicle in the association test field;

And if the damage matching information and the load distribution information meet the theoretical requirements of the acceleration test, determining that the cycle solution of the durability test working condition of the whole vehicle in the association test field meets the durability test requirements of the whole vehicle.

To achieve the above objective, an embodiment of a second aspect of the present disclosure provides a device for determining a cycle of a durability test condition of a whole vehicle in a test field under a user-related condition, including:

The model construction unit is used for constructing a user association model according to the user load data and the test field load data, and carrying out multi-objective solution on the user association model to obtain a non-inferior solution set;

The index decision unit is used for making multi-index decisions on the non-inferior solution sets according to the decision index sets to obtain a cycle solution of the endurance test working conditions of the whole vehicle in the associated test field;

And the standard verification unit is used for carrying out multi-objective solving and/or reconstructing the user association model again on the user association model if the whole vehicle durability test working condition cyclic solution of the association test field does not meet the whole vehicle durability test requirement until the whole vehicle durability test working condition cyclic solution of the association test field meets the whole vehicle durability test requirement, so as to obtain the whole vehicle durability test working condition cyclic standard of the test field.

To achieve the above object, an embodiment of a third aspect of the present disclosure provides an electronic device, including: a processor, a memory communicatively coupled to the processor;

The memory stores computer-executable instructions;

The processor executes computer-executable instructions stored in the memory to implement the method of any of the preceding aspects.

To achieve the above object, an embodiment of a fourth aspect of the present disclosure proposes a computer-readable storage medium having stored therein computer-executable instructions for implementing the method shown in any one of the foregoing first aspects when executed by a processor.

To achieve the above object, an embodiment of a fifth aspect of the present disclosure proposes a computer program product comprising a computer program which, when executed by a processor, implements the method as shown in any one of the preceding aspects.

In summary, the method, the device, the electronic equipment and the storage medium provided by the disclosure can improve the accuracy of constructing the user association model by relying on the user load data and the test field load data. And secondly, carrying out model solving by adopting a multi-objective solving method, and determining a test field whole vehicle durability test working condition circulation solution by adopting a multi-index decision, so that the test field whole vehicle durability test working condition circulation applicability can be improved, a test field whole vehicle durability test working condition circulation specification of related user working conditions with high practicability can be obtained, and technical support can be provided for test field whole vehicle durability verification.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a schematic flow chart of a method for determining cycle of endurance test conditions of a whole test field vehicle under user-related conditions according to an embodiment of the disclosure;

fig. 2 is a schematic illustration showing a pseudo damage of a test field working condition according to an embodiment of the disclosure;

fig. 3 is a schematic illustration of a user target mileage pseudo-damage provided in an embodiment of the present disclosure;

FIG. 4 is a schematic diagram showing a comprehensive contribution provided by an embodiment of the disclosure;

FIG. 5 is a schematic diagram showing a comparison of relative pseudo-damage ratios under a correlation calculation channel according to an embodiment of the present disclosure;

FIG. 6 is a schematic diagram illustrating a comparison of relative pseudo-damage ratios under an association verification channel according to an embodiment of the present disclosure;

FIG. 7 is a schematic diagram showing an amplitude-cumulative frequency distribution curve according to an embodiment of the present disclosure;

FIG. 8 is a schematic flow chart of a cycle determination of a test field vehicle durability test condition under user-related conditions provided by an embodiment of the present disclosure;

Fig. 9 is a schematic structural diagram of a device for determining cycle of endurance test conditions of a whole test field vehicle under user-related conditions according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.

In the related art, a user association model, also called a 'user-test field' equivalent association model, can equivalently associate the experience and experience of a user in a real driving environment, such as oil consumption, emission, noise, vibration and the like, with various performance parameters and results measured in a test field based on the equivalence between the actual use environment of the user and the environment of the test field. In this way, the data of the test field can be converted into a representation in the actual use environment, thereby better evaluating and predicting the performance of the vehicle.

In the related technology, a great deal of research is developed around a user association model, including the aspects of establishing a user target life cycle load spectrum, calculating a road strengthening coefficient of a test field and the like. Aiming at the research of a test field association calculation method, the application effect is still insufficient, and the main manifestation is as follows: the test field working condition cyclic solution obtained by the solving method has high dispersivity, zero solution exists, and the full utilization of the test field working condition is not facilitated; the situation that the reproduction of certain parts is excessive and the reproduction intensity of certain parts is insufficient often occurs; the working condition cyclic solving and selecting do not consider factors except damage such as test period, test mileage and the like, and have certain limitations.

In summary, for the construction and solving calculation of the user association model, further improvement and promotion are still needed.

The present disclosure is described in detail below with reference to specific examples.

In a first embodiment, as shown in fig. 1, fig. 1 is a flow chart of a method for determining a cycle of a test field vehicle durability test condition under a user-related condition according to an embodiment of the present disclosure, where the method may be implemented by a computer program and may be executed on a device for performing the cycle determination of the test field vehicle durability test condition under the user-related condition. The computer program may be integrated in the application or may run as a stand-alone tool class application.

The test field whole vehicle durability test working condition circulation determining device under the user association condition can be electronic equipment with a test field whole vehicle durability test working condition circulation determining function under the user association condition.

Specifically, the method for determining the cycle of the durability test working condition of the whole test field under the user association condition comprises the following steps:

s101, constructing a user association model according to user load data and test field load data, and carrying out multi-objective solution on the user association model to obtain a non-inferior solution set;

According to some embodiments, the vehicle load data refers to the load carrying capacity of the vehicle under different conditions, including static and dynamic loads. Static load refers to the load carrying conditions of the vehicle when stationary, including but not limited to the backup mass and the maximum allowable total mass. Dynamic loading refers to loading conditions during vehicle travel, including, but not limited to, automobile loading and check loading.

In some embodiments, the user load data refers to vehicle load data under user operating conditions. The user working condition refers to the environment and the running condition of the equipment in the actual use process, and specifically refers to the working condition of the user when the user actually uses the equipment.

In some embodiments, the test field load data refers to vehicle load data under test field conditions. The test field conditions include a variety of conditions that can simulate various environments and conditions that the product may encounter in actual use.

According to some embodiments, multi-objective solutions are used to find a set of solutions to meet as many objectives as possible when there are multiple objectives in solving a problem that need to be optimized or balanced at the same time.

In some embodiments, in a multi-objective decision problem, since there are multiple objectives that need to be optimized or balanced at the same time, there is no solution to meet all objectives at the same time. Rather than a set of inferior solutions, each solution is not inferior to the others, i.e., there is no solution that can improve all targets at the same time without losing other targets.

It is easy to understand that when the electronic equipment performs the cycle determination of the endurance test working condition of the whole test field under the user association condition, the electronic equipment can construct a user association model according to the user load data and the test field load data, and perform multi-objective solution on the user association model to obtain a non-inferior solution set.

S102, carrying out multi-index decision on the non-inferior solution set according to the decision index set to obtain a cycle solution of the endurance test working condition of the whole vehicle in the associated test field;

According to some embodiments, multi-index decisions, also referred to as multi-objective decisions, may be applied to handle decision problems containing multiple contradictory objectives. Multi-index decisions need to take into account more factors than single-target decisions and trade-off the interest-to-fraud relationship between individual factors.

The multi-index decision may be, for example, a method of approaching ideal solution ordering (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS).

In some embodiments, the set of decision indicators comprises at least one decision indicator. Decision metrics refer to criteria and metrics that are used in the process of multi-metric decision making on a set of non-inferior solutions to evaluate and compare the merits of different non-inferior solutions.

According to some embodiments, the cycle solution of the endurance test working condition of the whole vehicle in the association test field refers to an optimal solution selected from a non-inferior solution set after multi-index decision is performed on the non-inferior solution set according to a decision index set.

It is easy to understand that when the electronic device obtains the non-inferior solution set, the electronic device can make multi-index decisions on the non-inferior solution set according to the decision index set, and a cycle solution of the durability test working condition of the whole vehicle in the associated test field is obtained.

And S103, if the cycle solution of the whole vehicle durability test working condition of the association test field does not meet the requirement of the whole vehicle durability test, re-carrying out multi-objective solution on the user association model and/or reconstructing the user association model until the cycle solution of the whole vehicle durability test working condition of the association test field meets the requirement of the whole vehicle durability test, and obtaining the cycle specification of the whole vehicle durability test working condition of the test field.

According to some embodiments, the vehicle durability test requirements refer to requirements employed when determining whether a vehicle durability test condition cyclic solution for an associated test field can be used for a vehicle durability test. The cycle solution of the endurance test working conditions of the whole vehicle in the relevant test field meeting the endurance test requirement of the whole vehicle is the cycle specification of the endurance test working conditions of the whole vehicle in the test field.

In some embodiments, the vehicle durability test requirements include, but are not limited to, damage matching requirements and load distribution requirements.

It is easy to understand that when the electronic device obtains the complete vehicle durability test working condition cyclic solution of the association test field, if the electronic device judges that the complete vehicle durability test working condition cyclic solution of the association test field does not meet the complete vehicle durability test requirement, the electronic device can carry out multi-objective solution and/or reconstruct the user association model on the user association model again until the complete vehicle durability test working condition cyclic solution of the association test field meets the complete vehicle durability test requirement, and the complete vehicle durability test working condition cyclic specification of the test field is obtained.

In summary, according to the method provided by the embodiment, the user association model is built by relying on the user load data and the test field load data, so that the accuracy of building the user association model can be improved. And secondly, carrying out model solving by adopting a multi-objective solving method, and determining a test field whole vehicle durability test working condition circulation solution by adopting a multi-index decision, so that the test field whole vehicle durability test working condition circulation applicability can be improved, a test field whole vehicle durability test working condition circulation specification of related user working conditions with high practicability can be obtained, and technical support can be provided for test field whole vehicle durability verification.

The embodiment also provides another method for determining the cycle of the durability test working condition of the whole test field under the user association condition. The method may be performed by an electronic device.

Specifically, the method for determining the cycle of the durability test working condition of the whole test field under the user association condition can comprise the following steps:

S201, determining a durability test association channel of the whole vehicle;

According to some embodiments, when the durability test of the whole vehicle is performed, the test equipment is connected with the durability test related channel of the whole vehicle of the vehicle, so that test data can be collected and transmitted.

In some embodiments, the vehicle durability test association channel includes an association calculation channel and an association verification channel.

In some embodiments, the associated calculation channel includes, but is not limited to, a wheel center three component channel, a front suspension displacement channel, a rear suspension displacement channel. The correlation verification channels include, but are not limited to, a wheel center vertical acceleration channel, a tower vertical acceleration channel, a shock absorber displacement channel, and a suspension strain channel.

For example, in the running process of the vehicle, road surface excitation is transmitted to a chassis, a vehicle body and the like through wheels, so that the load applied to the wheels is the most direct feedback of the road surface excitation, and therefore, the three component forces of the wheel center, the displacement of the front suspension and the displacement signal of the rear suspension can be taken as associated calculation channels, and a total of Fx_LF、Fx_RF、Fx_LR、Fx_RR、Fy_LF、Fy_RF、Fy_LR、Fy_RR、Fz_LF、Fz_RF、Fz_LR、Fz_RR、Disp_CS_LF、Disp_CS_RF、Disp_LS_LR、Disp_LS_RR channels can be obtained. Secondly, other concerned channels including wheel center vertical acceleration, tower vertical acceleration, shock absorber displacement and suspension strain can be taken as associated verification channels, and the total number of the concerned channels is 17.

It is easy to understand that by setting the association calculation channel and the association verification channel, the complexity and the operation amount of the user association model calculation can be reduced, and the whole vehicle association effect can be ensured to meet the requirement.

S202, acquiring user working condition road spectrum data and associated test field working condition road spectrum data;

according to some embodiments, road spectrum data refers to various information recorded by the vehicle during travel, including, but not limited to, vehicle speed, acceleration, steering angle, brake pressure, chassis vibration, and the like. Road spectrum data can be collected by various sensors and recording devices and used to analyze various characteristics and properties during the travel of the vehicle.

In some embodiments, the user operating condition road spectrum data refers to road spectrum data under user operating conditions. The road spectrum data of the associated test field working condition refers to road spectrum data under the test field working condition.

The user working condition road spectrum data and the associated test field working condition road spectrum data can be data acquired by a sensor corresponding to a whole vehicle durability test associated channel installed on a test sample vehicle in road spectrum acquisition.

According to some embodiments, when the road spectrum data of the user working conditions are obtained, the classification and proportion of the user working conditions can be determined according to the user investigation model, and the road spectrum data collection of the working conditions of each user can be carried out.

For example, the user operating conditions may be classified into 4 categories, with road mileage ratios of 40%,30%,20% and 10% in order, and the user full life cycle target mileage set to 24 ten thousand kilometers.

According to some embodiments, when the road spectrum data of the working condition of the associated test field is obtained, proper working conditions of the test field can be selected and combined, and the road spectrum data of the working condition of the associated test field is collected.

In some embodiments, the selection and combination of test field conditions may be performed in conjunction with the type and use of the test vehicle, and the damage contribution and distribution conditions of each condition of the test field. Therefore, the number of the self-variables to be solved of the user association model can be effectively reduced, and the follow-up algorithm optimizing and solving is facilitated.

For example, 27 test field conditions can be selected in total and combined into 10 sub-conditions, namely R1, R2, R3, R4, R5, R6, R7, R8, R9 and R10, respectively, so as to obtain a test field condition combined set.

S203, carrying out load spectrum analysis processing on the road spectrum data of the user working condition to obtain user load data; carrying out load spectrum analysis processing on the road spectrum data of the working condition of the associated test field to obtain test field load data;

According to some embodiments, the load spectrum analysis processing is performed, so that the load change rule of the vehicle under different driving conditions can be known, the fatigue life and reliability of the vehicle can be estimated, and the basis is provided for the optimal design, performance estimation and fault prediction of the vehicle.

In some embodiments, the load spectrum analysis processing includes, but is not limited to, load data conversion, load signal preprocessing, load signal storage, and the like.

The load data conversion refers to data conversion of road spectrum data.

The load signal preprocessing refers to preprocessing the converted road spectrum data. Load signal preprocessing includes, but is not limited to, filtering, deburring, drift correction, and resampling processing.

The load signal storage means to store the load spectrum signal obtained after the load spectrum analysis processing. The load spectrum signal is both load data.

In some embodiments, load spectrum analysis processing can be performed on data collected by each channel under the whole vehicle durability test association channel in the user working condition road spectrum data and the associated test field working condition road spectrum data, so as to obtain a user load spectrum signal and an associated test field load spectrum signal.

S204, determining user working condition pseudo damage corresponding to each durability test association channel in the whole vehicle durability test association channel according to the user load data to obtain a user working condition pseudo damage set;

According to some embodiments, the pseudo-damage is used to evaluate fatigue damage of the material under cyclic loading. The pseudo damage is not a real physical damage, but a relative quantity, can reflect the potential damage capability of the road surface load, and is widely applied to the structural durability test field.

In some embodiments, the pseudo-injury D may be calculated according to the following equation:

Wherein n _i is the number of cycles of the ith stress level; n _i is the number of cycles to failure at the ith stress level; m is the total number of rain flow counting cycles.

According to some embodiments, the user operating condition pseudo-impairment refers to pseudo-impairment corresponding to user load data. And (3) substituting the user load data corresponding to each durability test associated channel into the formula (1) to obtain the user working condition pseudo damage corresponding to each durability test associated channel.

S205, determining the test field working condition pseudo damage corresponding to each endurance test associated channel according to the test field load data to obtain a test field working condition pseudo damage set;

According to some embodiments, the test field condition unit pseudo-injury includes a test field condition unit pseudo-injury corresponding to each test field condition combination in the set of test field condition combinations.

In some embodiments, the test field working condition unit pseudo damage corresponding to each test field working condition combination in the endurance test associated channel can be obtained by substituting test field load data corresponding to any endurance test associated channel into the formula (1), so that the test field working condition pseudo damage corresponding to each endurance test associated channel is obtained.

With a scenario example, fig. 2 is a schematic diagram showing a pseudo damage of a test field working condition unit according to an embodiment of the disclosure. As shown in FIG. 2, which illustrates the test field condition unit pseudo-impairment in test field condition combinations R1 through R10 for each of Fx_LF、Fx_RF、Fx_LR、Fx_RR、Fy_LF、Fy_RF、Fy_LR、Fy_RR、Fz_LF、Fz_RF、Fz_LR、Fz_RR、Disp_CS_LF、Disp_CS_RF、Disp_LS_LR、Disp_LS_RR of the 16 channel-associated calculation channels.

S206, taking the circulation times corresponding to each test field working condition combination as an unknown variable, and constructing a user association model according to the user working condition pseudo-damage set, the test field working condition pseudo-damage set and the circulation times;

According to some embodiments, a full life cycle extrapolation may be performed for each user operating condition pseudo-impairment in the set of user operating condition pseudo-impairment to obtain a set of user target mileage pseudo-impairment; and constructing a user association model according to the user target mileage pseudo-damage set, the test field working condition pseudo-damage set and the circulation times.

In some embodiments, performing full life cycle extrapolation refers to extrapolating the user operating condition spurious injury to a full life cycle target value, i.e., the user target mileage spurious injury, in accordance with the user full life cycle target mileage.

The target mileage of the whole life cycle of the user can be adjusted according to the actual application scene.

With a scenario example, fig. 3 is a schematic diagram illustrating a user target mileage pseudo-damage provided in an embodiment of the present disclosure. As shown in fig. 3, it shows that when the target mileage of the user in the whole life cycle is set to 24 ten thousand kilometers, the calculated target mileage is pseudo-damaged by the user corresponding to each of the Fx_LF、Fx_RF、Fx_LR、Fx_RR、Fy_LF、Fy_RF、Fy_LR、Fy_RR、Fz_LF、Fz_RF、Fz_LR、Fz_RR、Disp_CS_LF、Disp_CS_RF、Disp_LS_LR、Disp_LS_RR channel correlation calculation channels.

In some embodiments, the full life cycle extrapolation includes, but is not limited to, a mileage and quantile extrapolation based on different operating mode types.

According to some embodiments, the constructed user association model may be represented by the following formula:

Wherein D _mn represents pseudo damage of the key position corresponding to the durability test association channel m in the whole vehicle on the test field working condition combination n. Beta _n represents the number of cycles of the test field operating mode combination n. T _m represents the expected damage of the key position corresponding to the durability test association channel m corresponding to the target mileage of the whole life cycle of the driving user under the working condition of the user.

S207, converting the user association model into a multi-objective optimization problem model;

according to some embodiments, the transformed multi-objective optimization problem model may be represented as follows:

And f (beta: D, T) is an objective function, namely the damage difference between the user target mileage pseudo damage corresponding to each durability test associated channel and the test field working condition pseudo damage.

The cycle times beta are used as optimization variables, lb _j and ub _j are upper limit constraint and lower limit constraint of the optimization variables beta _j, namely boundary conditions of working condition combinations of all test fields; aeq _j·β_j＝beq_j is the linear equality constraint of the optimization variable beta _j, namely the linear relation among the working condition combinations of each test field; aeq _j·β_j≤b_j is a linear inequality constraint of the optimization variable beta _j.

S208, taking the cycle times as an optimization variable, taking the minimum damage difference between the user target mileage pseudo damage corresponding to each durability test associated channel and the test field working condition pseudo damage as an optimization target, and carrying out multi-target optimization solution on the multi-target optimization problem model to obtain a non-inferior solution set;

According to some embodiments, when performing multi-objective optimization solution to the multi-objective optimization problem model, for example, a Non-dominant ordered genetic algorithm (Non-dominated Sorting Genetic Algorithm, NSGA) may be used to perform optimization solution to obtain a set of Pareto Non-inferior solutions.

Taking a scene as an example, adopting NSGA-II algorithm to carry out optimization solution on the multi-objective optimization problem model corresponding to the figure 2 and the figure 3, and obtaining Pareto non-inferior solution set as shown in a table (1):

Watch (1)

S209, performing index weight analysis on the decision index set to obtain an index weight coefficient of each decision index in the decision index set;

According to some embodiments, the decision metrics in the decision metrics set include, but are not limited to, the average value Mm ₁ and standard deviation St ₁ of the relative pseudo-injury ratios of the associated calculation channels, the average value Mm ₂ and standard deviation St ₂ of the relative pseudo-injury ratios of the associated verification channels, the total test field durability test mileage Qq, and the total test field durability test duration Tt.

In some embodiments, the relative pseudo damage ratio is calculated as a ratio between the test field condition pseudo damage corresponding to the test field condition cyclic solution and the user target mileage pseudo damage under the same endurance test association channel.

The test field working condition circulating solution refers to any group of solutions obtained when the multi-objective optimization problem model is subjected to multi-objective optimization solution, and the test field working condition circulating solution can be, for example, a non-inferior solution. In some embodiments, the metric weight analysis refers to analyzing the weight of each decision metric in the set of decision metrics. The index weight analysis can adopt a method of correlation (CRITICAL INFERENCE Technique for Inter-Rater Reliability, CRITIC) among standards, and a CRITIC method for determining the weight coefficient of each index by combining the contrast strength and the conflict among indexes.

According to some embodiments, CRITIC methods, when employed, may include the steps of:

S2091, constructing an evaluation matrix according to the decision index set;

According to some embodiments, taking the ith decision index in the decision index set as an object to perform single-target decision to obtain a decision result corresponding to the ith decision index, and calculating to obtain a value x _ij corresponding to the jth decision index according to the decision result corresponding to the ith decision index. After completing single-target decision of each decision index in the decision index set in turn, an evaluation matrix X is formed according to all recorded values X _ij.

S2092, performing standardization processing on the evaluation matrix to obtain a standardized evaluation matrix;

According to some embodiments, when the data normalization processing is performed, the data normalization processing may be performed according to the positive and negative direction decision indexes of the decision indexes, so as to obtain a normalized value x' _ij in the normalized evaluation matrix.

For the forward index, the data normalization process may be performed according to the following formula:

wherein, for negative indicators, the data normalization process can be performed according to the following formula:

S2093, determining a variation coefficient and a conflict quantization index value of each decision index in the decision index set according to the standardized evaluation matrix;

according to some embodiments, the coefficient of variation for each decision index may be calculated according to the following equation:

wherein V _j is the coefficient of variation of the j-th decision index, also called the standard deviation coefficient, sigma _j is the standard deviation of the j-th decision index, and x _j is the average number of the j-th decision index.

According to some embodiments, the collision quantization index value of each decision index may be determined according to a correlation coefficient between the decision indexes.

In some embodiments, the correlation coefficient r _ij may be determined according to:

where x _hi and x _hj are the values of the ith and jth decision indices of the h non-inferior solution in the set of non-inferior solutions, And/>Is the mean value of the ith decision index and the jth decision index in the w-th non-inferior solution.

In some embodiments, the collision quantification index value for each decision index may be determined according to the following equation:

Where l _j is the collision quantization index value of the j-th decision index.

S2094, determining the index information quantity of each decision index according to the variation coefficient and the conflict quantization index value to obtain an index information quantity set corresponding to the decision index set;

according to some embodiments, the amount of metric information for each decision metric may be determined according to the following equation:

C_j＝V_j·l_j,i≠j j＝1,2,…,y (9)

Wherein C _j is the index information amount of the j-th decision index.

And S2095, determining the index weight coefficient of each decision index in the decision index set according to the index information quantity set.

According to some embodiments, the metric weight coefficient for each decision metric may be determined according to the following equation:

Wherein ω _j is the index weight coefficient of the j-th decision index.

Taking a scene as an example, the index weight coefficient (weight) obtained by adopting the CRITIC method can be as shown in table (2):

Decision index	Mean value/Mm ₁	Standard deviation/St ₁	Mean value/Mm ₂	Standard deviation/St ₂	Test mileage/Qq	Test time/Tt
							Weight value	0.147	0.120	0.132	0.334	0.133	0.134

Watch (2)

S210, constructing a comprehensive evaluation decision matrix according to the decision index set and the non-inferior solution set;

according to some embodiments, the integrated evaluation decision matrix R constructed may be as follows:

where x _wy represents the value of the w-th set of non-inferior solutions at the y-th decision index.

S211, determining the comprehensive contribution degree corresponding to each non-inferior solution in the non-inferior solution set according to the index weight coefficient and the comprehensive evaluation decision matrix;

According to some embodiments, when determining the comprehensive contribution degree corresponding to each non-inferior solution in the non-inferior solution set according to the index weight coefficient and the comprehensive evaluation decision matrix, firstly, regularization processing may be performed on the comprehensive evaluation decision matrix to obtain a comprehensive evaluation decision regular matrix.

Wherein, the comprehensive evaluation decision regular matrix R' can be determined according to the following formula:

And then, carrying out matrix weighting on the comprehensive evaluation decision regular matrix according to the index weight coefficient to obtain a comprehensive evaluation decision weighting matrix.

Wherein, the comprehensive evaluation decision weighting matrix V can be determined according to the following formula:

V＝(v_ij)_w×y＝(ω_jm_ij)_w×y (13)

Then, an optimal solution set and a worst solution set corresponding to the comprehensive evaluation decision weighting matrix can be determined;

The obtained optimal solution set a ⁺ and the worst solution set a ^- may be represented by the following formulas:

next, a first euclidean distance between each non-inferior solution in the non-inferior solution set and the optimal solution set, and a second euclidean distance between each non-inferior solution and the optimal solution set may be determined;

wherein the i-th non-inferior solution corresponds to the first Euclidean distance And a second Euclidean distance/>Can be determined according to the following equation:

It should be noted that the number of the substrates, The smaller the value of (i) is, the closer the sequence corresponding to the i-th non-inferior solution is to the optimal solution set (positive ideal solution set), and the farther the sequence corresponding to the i-th non-inferior solution is to the optimal solution set (positive ideal solution set). /(I)The smaller the value of (i) is, the closer the sequence corresponding to the ith non-inferior solution is to the worst solution set (negative ideal solution set), and vice versa.

Finally, according to the first Euclidean distance and the second Euclidean distance, determining the comprehensive contribution degree corresponding to each non-inferior solution; wherein, the comprehensive contribution degree T _i can be determined according to the following formula:

With a scenario as an example, fig. 4 is a schematic diagram showing a comprehensive contribution provided by an embodiment of the disclosure. As shown in FIG. 4, which shows the overall contribution of each non-inferior solution in Table (1) based on the GA-CRITIC-TOPSIS solution.

S212, determining a cycle solution of the durability test working condition of the whole vehicle in the associated test field according to the comprehensive contribution degree;

According to some embodiments, the cycle solution of the endurance test working condition of the whole vehicle in the association test field is the non-inferior solution with the highest comprehensive contribution degree in the non-inferior solution set.

By way of example of one scenario, the 11 th non-inferior solution in FIG. 4 has a highest overall contribution of 0.926. Therefore, the 11 th non-inferior solution can be determined as a cycle solution of the durability test working condition of the whole vehicle in the associated test field, and specific parameters and corresponding numerical values of each decision index are shown in a table (3):

Watch (3)

It should be noted that, the solution obtained by solving the table (1) based on the conventional least square method (Least Squares Method, LSM) is shown in the table (4):

Working conditions of	x₁	x₂	x₃	x₄	x₅	x₆	x₇	x₈	x₉	x₁₀
											Number of cycles	14	5000	5000	500	0	0	320	233	0	0

Watch (4)

It is easy to understand that, as shown in table (3) and table (4), compared with the conventional method for solving the related test field whole vehicle durability test working condition cyclic solution, the related test field whole vehicle durability test working condition cyclic solution obtained by solving based on the GA-CRITIC-TOPSIS solving method is more reasonable and has no zero solution.

S213, determining damage matching information and load distribution information corresponding to the cycle solution of the durability test working condition of the whole vehicle in the associated test field;

According to some embodiments, the impairment matching information comprises a relative pseudo-impairment ratio associated with the computation channel and a relative pseudo-impairment ratio associated with the verification channel. With an example of a scenario, fig. 5 is a schematic diagram illustrating comparison of relative pseudo-injury ratios under a correlation calculation channel according to an embodiment of the present disclosure. As shown in fig. 5, which shows the relative pseudo-injury ratios based on the GA-CRITIC-TOPSIS solution and LSM solution, respectively, for the Fx_LF、Fx_RF、Fx_LR、Fx_RR、Fy_LF、Fy_RF、Fy_LR、Fy_RR、Fz_LF、Fz_RF、Fz_LR、Fz_RR、Disp_CS_LF、Disp_CS_RF、Disp_LS_LR、Disp_LS_RR these 16 correlated computation channels. Fig. 6 is a schematic diagram illustrating comparison of relative pseudo-damage ratios under an association verification channel according to an embodiment of the present disclosure. As shown in fig. 6, which shows the relative pseudo-injury ratios based on the GA-CRITIC-TOPSIS solution and LSM solution, respectively, for the Acc_WC_LF_Z、Acc_WC_RF_Z、Acc_WC_LR_Z、Acc_WC_RR_Z、Acc_TS_LF_Z、Acc_TS_RF_Z、Acc_TS_LR_Z、Acc_TS_RR_Z、Disp_Shock_LR、Disp_Shock_RR、Strain_Stab_Bar、Strain_LS_LR_1、Strain_LS_LR_2、Strain_LS_LR_3、Strain_LS_RR_1、Strain_LS_RR_2、Strain_LS_RR_3 associated verification channels.

Compared with an LSM solution, the damage matching effect of the cyclic solution of the durability test working condition of the whole vehicle in the relevant test field obtained by solving based on the GA-CRITIC-TOPSIS solution is better.

According to some embodiments, the load distribution information may be represented, for example, as an amplitude-cumulative frequency distribution curve.

Taking a scenario as an example, fig. 7 is a schematic diagram showing an amplitude-cumulative frequency distribution curve according to an embodiment of the disclosure. As shown in FIG. 7, the load distribution information obtained by the GA-CRITIC-TOPSIS solution and the LSM solution is analyzed for a plurality of times, and compared with the LSM solution, the load distribution information obtained by the GA-CRITIC-TOPSIS solution is closer to a user target and can more truly represent the actual user road condition.

S214, if the damage matching information and the load distribution information meet the theoretical requirements of the acceleration test, determining that the cycle solution of the whole vehicle durability test working condition of the associated test field meets the requirements of the whole vehicle durability test;

According to some embodiments, the accelerated test employs a test environment that is more severe than the environment to which the device is subjected in normal use, exposing the product to high stress conditions for a short period of time, thereby accelerating the performance degradation of the product. When the acceleration test is carried out, proper test conditions and parameters are required to be selected, so that the accuracy and reliability of test results are ensured.

In some embodiments, the acceleration test theoretical requirement refers to a parameter selected when the vehicle is subjected to an acceleration test, namely, a requirement that the cycle solution of the durability test working condition of the whole vehicle in the associated test field needs to meet.

S215, if the cycle solution of the whole vehicle durability test working condition of the association test field does not meet the requirement of the whole vehicle durability test, re-carrying out multi-objective solution on the user association model and/or reconstructing the user association model until the cycle solution of the whole vehicle durability test working condition of the association test field meets the requirement of the whole vehicle durability test working condition, and obtaining the cycle specification of the whole vehicle durability test working condition of the test field;

S216, performing a test field whole vehicle durability test by adopting a test field whole vehicle durability test working condition circulation specification.

With a scenario as an example, fig. 8 is a schematic flow chart of a test field vehicle durability test condition cycle determination under a user-related condition according to an embodiment of the present disclosure. As shown in fig. 8, first, a damage equivalent correlation mathematical model is constructed from the user correlation model and the user and test field load data. And then, carrying out NSGA-II algorithm solution on the damage equivalent association mathematical model to obtain a Pareto non-inferior solution set. And then, acquiring an evaluation index, and carrying out CRITIC weight analysis on the evaluation index to obtain an analyzed evaluation index weight coefficient. And then, determining the TOPSIS optimal solution from the Pareto non-inferior solution set according to the analyzed evaluation index weight coefficient and the multi-index decision model. And then, determining whether the load damage and distribution corresponding to the TOPSIS optimal solution meet the requirements, if not, correcting the damage equivalent association mathematical model and reacquiring the TOPSIS optimal solution until the load damage and distribution corresponding to the TOPSIS optimal solution meet the requirements, and outputting the working condition cycle of the test field. And finally, performing a whole vehicle durability test according to the working condition circulation of the test field.

In summary, according to the method provided by the embodiment of the disclosure, by selecting a proper vehicle durability test association channel according to load data acquired by the user working condition and the test field working condition and setting the test field working condition combination, the user association model is constructed, and the construction accuracy of the user association model can be improved. The model solving and optimal solution determining are carried out by adopting the GA-CRITIC-TOPSIS method, so that the better test field whole vehicle durability test working condition cycle can be obtained relatively objectively, the applicability of the test field whole vehicle durability test working condition cycle can be improved, the test field whole vehicle durability test working condition cycle specification of the relevant user working condition with higher practicability can be obtained, and technical support can be provided for test field whole vehicle durability performance verification.

In order to achieve the above embodiment, the present disclosure further provides a device for determining a cycle of a test field vehicle durability test condition under a user-related condition.

As shown in fig. 2, the device 900 for determining the cycle of the endurance test working condition of the whole test field under the user-related condition includes:

The model construction unit 901 is used for constructing a user association model according to the user load data and the test field load data, and carrying out multi-objective solution on the user association model to obtain a non-inferior solution set;

The index decision unit 902 is configured to perform multi-index decision on the non-inferior solution set according to the decision index set, so as to obtain a cyclic solution of the endurance test condition of the whole vehicle in the association test field;

and the standard verification unit 903 is configured to, if the vehicle durability test condition cyclic solution of the association test field does not meet the vehicle durability test requirement, re-perform multi-objective solution and/or reconstruct the user association model on the user association model until the vehicle durability test condition cyclic solution of the association test field meets the vehicle durability test requirement, thereby obtaining the test field vehicle durability test condition cyclic standard.

Optionally, before building the user-associated model from the user load data and the test field load data, the model building unit 901 is further configured to:

carrying out load spectrum analysis processing on the road spectrum data of the user working condition to obtain user load data;

and carrying out load spectrum analysis processing on the road spectrum data of the working condition of the associated test field to obtain the load data of the test field.

Optionally, the model building unit 901 is configured to, when building a user association model according to the user load data and the test field load data, specifically:

Determining test field working condition pseudo damage corresponding to each endurance test associated channel according to the test field load data to obtain a test field working condition pseudo damage set, wherein the test field working condition pseudo damage comprises test field working condition unit pseudo damage corresponding to each test field working condition combination in the test field working condition combination set;

Optionally, the model building unit 901 is configured to, when building a user association model according to the user working condition pseudo-damage set, the test field working condition pseudo-damage set, and the number of cycles, specifically be:

And constructing a user association model according to the user target mileage pseudo-damage set, the test field working condition pseudo-damage set and the circulation times.

Optionally, the model building unit 901 is configured to perform multi-objective solution on the user association model, and when obtaining the non-inferior solution set, the model building unit is specifically configured to:

And taking the circulation times as an optimization variable, taking the minimum damage difference between the user target mileage pseudo damage and the test field working condition pseudo damage corresponding to each durability test association channel as an optimization target, and carrying out multi-target optimization solution on the multi-target optimization problem model to obtain a non-inferior solution set.

Optionally, the index decision unit 902 is configured to perform multi-index decision on the non-inferior solution set according to the decision index set, and when obtaining the cycle solution of the endurance test condition of the whole vehicle in the association test field, the index decision unit is specifically configured to:

And determining a cycle solution of the durability test working condition of the whole vehicle in the associated test field according to the comprehensive contribution degree, wherein the cycle solution of the durability test working condition of the whole vehicle in the associated test field is a non-inferior solution with highest comprehensive contribution degree in the non-inferior solution set.

Optionally, the index decision unit 902 is configured to perform index weight analysis on the decision index set, and when obtaining an index weight coefficient of each decision index in the decision index set, the index decision unit is specifically configured to:

Optionally, the index decision unit 902 is configured to determine, according to the index weight coefficient and the comprehensive evaluation decision matrix, a comprehensive contribution degree corresponding to each non-inferior solution in the non-inferior solution set, specifically configured to:

Regularization treatment is carried out on the comprehensive evaluation decision matrix to obtain a comprehensive evaluation decision regular matrix;

matrix weighting is carried out on the comprehensive evaluation decision regular matrix according to the index weight coefficient, and a comprehensive evaluation decision weighting matrix is obtained;

Optionally, the specification verification unit 903 is further configured to:

Determining damage matching information and load distribution information corresponding to the cycle solution of the durability test working condition of the whole vehicle in the associated test field;

If the damage matching information and the load distribution information meet the theoretical requirements of the acceleration test, determining that the cycle solution of the durability test working condition of the whole vehicle in the associated test field meets the durability test requirements of the whole vehicle.

It should be noted that, the explanation of the embodiment of the method for determining the test field overall durability test condition cycle under the user-related condition is also applicable to the test field overall durability test condition cycle determining device under the user-related condition of the embodiment, and is not repeated here.

In summary, the device provided by the embodiment of the disclosure builds the user association model by relying on the user load data and the test field load data, so that the accuracy of building the user association model can be improved. And secondly, carrying out model solving by adopting a multi-objective solving method, and determining a test field whole vehicle durability test working condition circulation solution by adopting a multi-index decision, so that the applicability of the test field whole vehicle durability test working condition circulation determination can be improved, the test field whole vehicle durability test working condition circulation specification of the associated user working condition with high practicability can be obtained, and technical support can be provided for test field whole vehicle durability verification.

In order to achieve the above embodiments, the present disclosure further proposes an electronic device including: a processor, a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the methods provided by the previous embodiments.

In order to implement the above-described embodiments, the present disclosure also proposes a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement the methods provided by the foregoing embodiments.

To achieve the above embodiments, the present disclosure also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the foregoing embodiments.

The processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user involved in the present disclosure all conform to the regulations of the relevant laws and regulations and do not violate the public order colloquial.

It should be noted that personal information from users should be collected for legitimate and reasonable uses and not shared or sold outside of these legitimate uses. In addition, such collection/sharing should be performed after receiving user informed consent, including but not limited to informing the user to read user agreements/user notifications and signing agreements/authorizations including authorization-related user information before the user uses the functionality. In addition, any necessary steps are taken to safeguard and ensure access to such personal information data and to ensure that other persons having access to the personal information data adhere to their privacy policies and procedures.

The present disclosure contemplates embodiments that may provide a user with selective prevention of use or access to personal information data. That is, the present disclosure contemplates that hardware and/or software may be provided to prevent or block access to such personal information data. Once personal information data is no longer needed, risk can be minimized by limiting data collection and deleting data. In addition, personal identification is removed from such personal information, as applicable, to protect the privacy of the user.

In the foregoing descriptions of embodiments, descriptions of the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the disclosure. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present disclosure.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

Those of ordinary skill in the art will appreciate that all or part of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.

Furthermore, each functional unit in the embodiments of the present disclosure may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present disclosure have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the present disclosure, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the present disclosure.

Claims

1. The method for determining the cycle of the durability test working condition of the whole vehicle in the test field under the condition of user association is characterized by comprising the following steps:

2. The method of claim 1, wherein prior to said constructing a user association model from user payload data and test field payload data, the method further comprises:

3. The method of claim 1, wherein constructing a user association model from the user payload data and the test field payload data comprises:

4. The method of claim 3, wherein the constructing a user association model from the set of user-condition pseudo-injuries, the set of test-field-condition pseudo-injuries, and the number of cycles comprises:

5. The method of claim 4, wherein said performing a multi-objective solution to the user-associated model results in a non-inferior solution set, comprising:

6. The method of claim 1, wherein the performing multi-index decision on the non-inferior solution set according to the decision index set to obtain a cycle solution for the endurance test condition of the whole vehicle in the associated test field comprises:

7. The method of claim 6, wherein the performing an index weight analysis on the set of decision indexes to obtain an index weight coefficient of each decision index in the set of decision indexes comprises:

8. The method of claim 6, wherein the determining the aggregate contribution of each non-inferior solution in the set of non-inferior solutions based on the metric weight coefficients and the aggregate evaluation decision matrix comprises:

9. The method according to claim 1, wherein the method further comprises:

10. The utility model provides a test field whole car durability test operating mode circulation determining means under user's associated condition which characterized in that includes: