Method, apparatus and device for calculating proving-ground operating condition combination, and medium

Abstract

The present invention relates to the technical field of automobile durability tests. Disclosed are a method, apparatus and device for calculating a proving-ground operating condition combination, and a medium. The method comprises: collecting load spectrum data of each type of road surface of public roads, calculating relative pseudo damages and frequency damage spectra, and calculating the total target of the relative pseudo damages and the total target of the frequency damage spectra; compiling statistics on the relative pseudo damage and the frequency damage spectrum of each proving-ground operating condition, and the time required for the operation of each proving-ground operating condition; converting a relative pseudo damage matrix of each proving-ground operating condition into a contribution degree matrix, and outputting a set number of groups of solutions regarding expected targets; reducing the contribution degree matrixes, deleting the proving-ground operating condition, the number of repetitions of which is smaller than a set number, and then carrying out matching solving again, and outputting a set number of groups of solutions that satisfy the expected targets; and selecting as the optimal solution a proving-ground operating condition combination involving the shortest operation time. By means of the present application, a proving-ground operating condition combination, the precision of which meets requirements and which has a good execution performance, can be quickly obtained by means of calculation.

Description

A test field working condition combination calculation method, equipment, device and medium Technical Field

[0001] The present application relates to the technical field of automobile durability testing, and in particular to a method, equipment, device and medium for calculating a combination of working conditions in a test field. Background Art

[0002] To quickly verify vehicle durability and reliability, it is necessary to collect load spectrum data from public roads and proving grounds. Then, through calculation, the public road verification mileage target is converted into a certain mileage test verification on the proving ground, ultimately achieving the purpose of accelerated verification. The process of formulating proving ground specifications is called user association.

[0003] Automotive proving grounds typically consist of dozens of different road surfaces, such as cobblestones, Belgian roads, and washboards, to simulate the impact of various types of challenging public road conditions on vehicles. The core of user correlation is to determine different proving ground condition combinations (different road surfaces, vehicle speeds, operating methods, etc.) and the corresponding number of repetitions to achieve high correlation accuracy and ultimately achieve durability verification goals.

[0004] Currently, the calculation method for test field operating condition combinations is mainly based on principles such as linear damage accumulation theory and fatigue damage equivalent equivalence. There are two common methods: one is to rely on the experience of engineers, select common test field operating conditions in the test field as the basis, and manually adjust the number of cycles until the equivalent relationship of the target acquisition channel is within a certain range. However, due to the large number of cycle values ​​and combinations, the accuracy of the manually combined results is poor. The second method is to use optimization algorithms such as genetic algorithms for solution. This can quickly calculate the results, but because it does not consider constraints such as acquisition channel classification and running time, the calculation results are relatively discrete and have poor feasibility. Summary of the Invention

[0005] The present application provides a test field operating condition combination calculation method, equipment, device and medium, which can quickly calculate the test field operating condition combination with satisfactory accuracy and good executability.

[0006] In a first aspect, an embodiment of the present application provides a method for calculating a combination of test field operating conditions, the method comprising:

[0007] Based on multiple acquisition channels composed of sensors installed on the test vehicle, load spectrum data of various types of road surfaces on public roads are collected, relative pseudo-damage and frequency damage spectrum are calculated, and the total relative pseudo-damage target and frequency damage spectrum target are calculated based on the ratio of the total target mileage to each type of road surface.

[0008] Collect load spectrum data under various test field conditions on the test vehicle, connect the collected load spectrum data according to the driving route, and calculate the relative pseudo-damage and frequency damage spectrum of each test field condition, as well as the time required for the test field condition to run;

[0009] Establish and solve the correlation matrix equation to convert the relative pseudo-damage matrix of each test site working condition into a contribution matrix. Then, classify the correlation calculation acquisition channels and use a constrained improved genetic algorithm combined with fitness functions of different matching requirements to perform matching and output the solution for the expected target of the set number of groups.

[0010] According to the number of repetitions of the test field conditions, the contribution matrix is ​​reduced, and after deleting the test field conditions with a repetition number lower than the set number, the matching solution is performed again to output the set number of solutions that meet the expected goals;

[0011] Calculate whether the total running time of each group of solutions that meet the expected goals meets the verification cycle requirements. At the same time, verify whether the distribution of the frequency damage spectrum under the test field conditions and the public road conditions is consistent. Select the test field condition combination with the shortest running time as the optimal solution.

[0012] In conjunction with the first aspect, in one embodiment, the multiple acquisition channels formed by sensors installed on the test vehicle collect load spectrum data of various types of road surfaces on public roads, calculate relative pseudo-damage and frequency damage spectra, and calculate the total relative pseudo-damage target and the total frequency damage spectrum target based on the ratio of the total target mileage to each type of road surface, specifically:

[0013] A wheel six-component force sensor, a spring displacement sensor, a chassis component force sensor, and a wheel center acceleration sensor are installed on the test vehicle to form multiple acquisition channels for data collection;

[0014] Collect load spectrum data of various types of pavement on public roads and calculate relative pseudo-damage and frequency damage spectra;

[0015] According to the ratio of the total target mileage to each type of road surface, the total target of relative pseudo damage and the total target of frequency damage spectrum are calculated.

[0016] In conjunction with the first aspect, in one embodiment, the calculation of the relative pseudo-damage total target and the frequency damage spectrum total target is specifically:

[0017] Based on the linear cumulative damage theory, the relative pseudo-damage total target of each acquisition channel is calculated. Specifically:

[0018] P=[P1,P2…P m ] T

[0019] Where P represents the relative pseudo-damage target of each acquisition channel, P m represents the relative pseudo-damage value of the mth acquisition channel, m represents the total number of acquisition channels, and T represents the inversion of evidence;

[0020] After bandpass filtering the time domain signal in different frequency bands, the relative pseudo damage is calculated to characterize the distribution of the relative pseudo damage value in different frequency bands and obtain the overall target of the frequency damage spectrum. Specifically:

[0021] Where R represents the total target of the frequency damage spectrum, R km represents the frequency damage spectrum, R km The m in the equation represents the number of acquisition channels involved in the analysis, and R km The k in represents the number of segments of the frequency impairment spectrum.

[0022] In conjunction with the first aspect, in one embodiment, the correlation matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix, wherein the establishment of the correlation matrix equation is specifically as follows:

[0023] Using the calculated relative pseudo-damage and frequency damage spectrum of public roads as target values, select the test field conditions and determine the corresponding number of repetitions so that the cumulative damage and distribution of the test field condition cycle combination can reproduce the damage on public roads;

[0024] For the correlation between public road and test field conditions, the specific correlation matrix equation is:

[0025] Where D represents the matrix composed of relative pseudo damage values ​​under the test field working condition, that is, the relative pseudo damage matrix of the test field working condition, D mn represents the relative pseudo damage value of the mth acquisition channel of the nth test field working condition, N represents the number of test field working condition cycles vector, N n represents the number of cycles corresponding to the nth test field condition, P represents the target value vector, P m It represents the relative pseudo damage value of the mth acquisition channel under the target mileage of the public road.

[0026] In conjunction with the first aspect, in one embodiment, the correlation matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix. Specifically, converting the relative pseudo-damage matrix of each test field working condition into a contribution matrix is:

[0027] Taking the maximum vertical force value of the data collected on the public road as the benchmark, select the test field conditions whose extreme value does not exceed the maximum vertical force value of the public road;

[0028] Using the proving ground route and actual driving route requirements as constraints, the proving ground condition data is spliced, and multiple individual proving ground conditions in each area of ​​the proving ground are combined into a combined proving ground condition.

[0029] Normalize the correlation matrix equation and divide each acquisition channel in the matrix D by each acquisition channel in the target value vector P, so as to convert the relative pseudo-damage matrix of the test field working condition into a contribution matrix. Specifically:

[0030] Among them, C represents the contribution matrix, C mn It represents the relative pseudo-damage value of the mth acquisition channel of the nth combined test field working condition. The elements in the result vector obtained by multiplying the contribution matrix C by the test field working condition cycle number vector N are the ratios of the cumulative damage of the test field working condition to the total damage target under the public road.

[0031] In conjunction with the first aspect, in one implementation, the improved genetic algorithm is specifically:

[0032] Perform genetic algorithm settings, set the maximum genetic generation, crossover probability, mutation probability, set the value range and value interval of each variable in the test field working condition cycle number vector N, and use random assignment to output a set number of chromosomes as the initial population;

[0033] Set the fitness function, classify the collection channels of the correlation calculation according to their importance, and obtain the primary and secondary indicators;

[0034] Taking the maximum genetic generation as the stopping condition, the genetic algorithm is used to solve the problem respectively, and the optimal solution of each round is taken as a standby, and the optimal solution of each round meets the expected goal. The first set number of solutions that meet the expected goal are output, and the test field conditions that appear less than the set number in the first set number of solutions that meet the expected goal are eliminated to reduce the number of columns of the total target matrix of the frequency damage spectrum. This cycle is repeated until the set number of solutions that meet the expected goal are output.

[0035] In conjunction with the first aspect, in one embodiment, the fitness function is expressed as:

[0036] f=λ·k1+k2

[0037] Among them, f represents the fitness function, k1 represents the number of collection channels corresponding to the first-level indicator, k2 represents the number of collection channels corresponding to the second-level indicator, and λ represents the coefficient used to balance the achievement rates of the first-level and second-level indicators.

[0038] In a second aspect, an embodiment of the present application provides a device for calculating a combination of test field operating conditions, the device comprising:

[0039] A calculation module is used to collect load spectrum data of various types of road surfaces on public roads based on multiple acquisition channels formed by sensors installed on the test vehicle, calculate relative pseudo-damage and frequency damage spectrum, and calculate the total relative pseudo-damage target and the total frequency damage spectrum target based on the ratio of the total target mileage to each type of road surface;

[0040] The statistical module is used to collect load spectrum data under various test field conditions based on the test vehicle, connect the collected load spectrum data according to the driving route, and calculate the relative pseudo-damage and frequency damage spectrum of each test field condition, as well as the time required for the test field condition to run;

[0041] The first output module is used to establish and solve the correlation matrix equation to convert the relative pseudo-damage matrix of each test site working condition into a contribution matrix, classify the correlation calculation acquisition channels, and use a constrained improved genetic algorithm combined with fitness functions with different matching requirements to perform matching solutions and output the solution for the expected target of the set number of groups;

[0042] The second output module is used to reduce the contribution matrix according to the number of repetitions of the test field conditions, delete the test field conditions with a repetition number lower than the set number, and then perform matching and solving again to output a set number of solutions that meet the expected goals;

[0043] The selection module is used to calculate whether the total running time of each group of solutions that meet the expected goals meets the verification cycle requirements. At the same time, it verifies whether the distribution of the frequency damage spectrum under the test field conditions and the public road conditions is consistent, and selects the test field condition combination with the shortest running time as the optimal solution.

[0044] In a third aspect, an embodiment of the present application provides a test field operating condition combination calculation device, which includes a processor, a memory, and a test field operating condition combination calculation program stored in the memory and executable by the processor, wherein when the test field operating condition combination calculation program is executed by the processor, the steps of the above-mentioned test field operating condition combination calculation method are implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a test field operating condition combination calculation program is stored, wherein when the test field operating condition combination calculation program is executed by a processor, the steps of the above-mentioned test field operating condition combination calculation method are implemented.

[0046] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0047] Through the improved genetic algorithm and fitness function, a solution that meets the relative pseudo-damage accuracy requirements, the frequency domain damage spectrum accuracy requirements and the test time requirements is quickly solved, and the calculation of the test field condition combination associated with the public road condition and the test field is realized. The test field condition combination with satisfactory accuracy and good executability can be quickly calculated, and only the number of conditions needs to be adjusted during the solution process. The calculation method of this application has high applicability and is easy to promote and apply. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] FIG1 is a flow chart of the test field working condition combination calculation method of the present application;

[0049] FIG2 is a schematic diagram of the functional modules of the test field working condition combination calculation device of the present application;

[0050] FIG3 is a schematic diagram of the hardware structure of the test field working condition combination calculation device of the present application. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0053] On the first aspect, an embodiment of the present application provides a method for calculating a test field operating condition combination, which realizes the calculation of a test field operating condition combination associated with a public road operating condition and a test field, and can quickly calculate a test field operating condition combination that meets the accuracy requirements and has good executability.

[0054] In one embodiment, referring to FIG1 , FIG1 is a flow chart of a method for calculating a combination of test field operating conditions of the present application. As shown in FIG1 , the method for calculating a combination of test field operating conditions includes:

[0055] S1: Based on multiple acquisition channels composed of sensors installed on the test vehicle, load spectrum data of various types of road surfaces on public roads are collected, relative pseudo-damage and frequency damage spectrum are calculated, and the total relative pseudo-damage target and frequency damage spectrum target are calculated based on the ratio of the total target mileage to each type of road surface;

[0056] Furthermore, in one embodiment, based on multiple acquisition channels formed by sensors installed on the test vehicle, load spectrum data of various types of road surfaces on public roads are collected, relative pseudo-damage and frequency damage spectrum are calculated, and based on the ratio of the total target mileage to each type of road surface, the total relative pseudo-damage target and the total frequency damage spectrum target are calculated, specifically:

[0057] S101: Install a wheel six-component force sensor, a spring displacement sensor, a chassis component force sensor, and a wheel center acceleration sensor on the test vehicle to form multiple acquisition channels for data acquisition;

[0058] S102: collecting load spectrum data of various types of road surfaces on public roads, and calculating relative pseudo damage and frequency damage spectra;

[0059] S103: Calculate the total relative pseudo-damage target and the total frequency damage spectrum target based on the ratio of the total target mileage to each type of road surface.

[0060] Specifically, the test vehicle is equipped with six-component wheel force sensors, spring displacement sensors, chassis component force sensors, wheel center acceleration sensors, and other sensors to collect load spectrum data for various types of road surfaces on public roads. The relative pseudo-damage and frequency damage spectra are then calculated. Based on the total target mileage and the ratio of each road type, the total relative pseudo-damage target and the total frequency damage spectrum target are calculated. During implementation, load spectrum data totaling 1,000 kilometers of four road types (urban roads, national and provincial highways, poor roads, and expressways / urban ring roads) can be collected. The relative pseudo-damage and frequency damage distribution of the load spectrum data are calculated. Based on the total target mileage of 240,000 kilometers and the ratio of the four road types included, the user's total relative pseudo-damage target and frequency damage distribution target are calculated.

[0061] S2: Collect load spectrum data under various test field conditions on the test vehicle, connect the collected load spectrum data according to the driving route, and calculate the relative pseudo-damage and frequency damage spectrum of each test field condition, as well as the time required for the test field condition to run;

[0062] In the specific implementation process, using a specific proving ground as an example, 43 proving ground conditions, encompassing various vehicle speeds and operating scenarios, were collected. Based on factors such as route constraints, these 43 proving ground condition data were combined into 22 coherent and efficient proving ground condition combinations. For these 22 proving ground conditions, relative pseudo-damage and frequency damage spectra were calculated, and the run time was statistically analyzed, resulting in a data set consisting of 22 files. For the proving ground condition data, in addition to the relative pseudo-damage and frequency damage spectra for public road conditions, the run time for each condition was also statistically analyzed to assess the proving ground's standard run time, i.e., test efficiency.

[0063] That is, during the collection phase associated with user operating conditions (the user operating conditions in this application refer to public road conditions), a test vehicle must be prepared, and sensors for the six-component wheel force, spring displacement, chassis component force, wheel center acceleration, and other sensors must be installed on the vehicle chassis and body, totaling m collection channels. Load spectrum data for public roads is collected based on the types of public roads defined by vehicle durability (such as urban, highway, national and provincial roads, etc.). The same vehicle is subsequently used to carry out load spectrum collection at the test site, collecting test site conditions at different vehicle speeds and different operations (uniform speed, acceleration, deceleration, serpentine, etc.).

[0064] The core part of user working condition association is the data processing stage, in which the public road data and test field working condition data collected in the above part need to be processed separately. Specifically, the relative pseudo damage total target and the frequency damage spectrum total target are calculated as follows:

[0065] a: Based on the linear cumulative damage theory, the relative pseudo damage target of each acquisition channel is calculated. Specifically:

[0066] P=[P1,P2…P m ] T Where P represents the relative pseudo-damage target of each acquisition channel, P m represents the relative pseudo-damage value of the mth acquisition channel, m represents the total number of acquisition channels, and T represents the inversion of evidence;

[0067] First, for public road data, the total relative pseudo-damage target for each acquisition channel is calculated based on the linear cumulative damage theory (i.e., the relative pseudo-damage under the user's target mileage, such as 240,000 kilometers).

[0068] b: Calculate the relative pseudo damage after bandpass filtering the time domain signal in different frequency bands, characterize the distribution of relative pseudo damage values ​​in different frequency bands, and obtain the overall target of the frequency damage spectrum. Specifically:

[0069] Where R represents the total target of the frequency damage spectrum, R km represents the frequency damage spectrum, R km The m in the equation represents the number of acquisition channels involved in the analysis, and R km The k in the _{k}_{\partial_span} represents the number of segments of the frequency damage spectrum, such as the upper bounce frequency band and the unsprung bounce frequency band. If the relative pseudo-damage matching effect of five frequency bands is important, then k is equal to 5.

[0070] S3: Establish and solve the correlation matrix equation to convert the relative pseudo-damage matrix of each test site working condition into a contribution matrix, classify the correlation calculation acquisition channels, and use a constrained improved genetic algorithm combined with fitness functions of different matching requirements to perform matching and solve, and output the solution for the expected target of the set number of groups;

[0071] In this application, an association matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix. The establishment of the association matrix equation is specifically as follows:

[0072] S301: Using the calculated relative pseudo-damage and frequency damage spectrum of a public road as target values, a test field operating condition is selected and the corresponding number of repetitions is determined so that the cumulative damage and distribution of the test field operating condition cycle combination can reproduce the damage on a public road;

[0073] S302: For the correlation between public roads and test site working conditions, the specific correlation matrix equation is:

[0074] Where D represents the matrix composed of relative pseudo damage values ​​under the test field working condition, that is, the relative pseudo damage matrix of the test field working condition, D mn represents the relative pseudo damage value of the mth acquisition channel of the nth test field working condition, N represents the number of test field working condition cycles vector, N n represents the number of cycles corresponding to the nth test field condition, P represents the target value vector, P m represents the relative pseudo-damage value of the mth acquisition channel under the target mileage of the public road. The correlation matrix equation is the multi-objective optimization problem for solving the vector N.

[0075] Existing approaches typically use a constructor to transform the correlation matrix equation into a mathematical model with the vector N as the design variable. However, these methods emphasize the optimal theoretical solution while failing to consider the feasibility of the calculated results. In practice, while maintaining accuracy, engineering practices strive to minimize the number of operating conditions and the test cycle, thereby shortening the verification cycle.

[0076] The test field working condition combination calculation method proposed in this application is used to quickly calculate the working condition combination that meets the accuracy requirements and has high executability in the data part. Specifically, a correlation matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix. Among them, for converting the relative pseudo-damage matrix of each test field working condition into a contribution matrix, specifically:

[0077] S311: Using the maximum vertical force value of the data collected on the public road as a benchmark, select test field conditions whose extreme values ​​do not exceed the maximum vertical force value of the public road from the test field conditions;

[0078] S312: Using the proving ground route and actual driving route requirements as constraints, and adhering to the principles of reducing "empty runs" and preserving features as much as possible, the proving ground condition data is spliced, combining multiple individual proving ground conditions in various areas of the proving ground into a combined proving ground condition.

[0079] For example, if three types of road surfaces in the test site are on the same driving route and all satisfy step S311, the three types of road surfaces can be connected. In this way, multiple independent operating conditions in each area of ​​the test site can be combined into a "small cycle" operating condition.

[0080] S313: Normalize the correlation matrix equation and divide each acquisition channel in the matrix D by each acquisition channel in the target value vector P, thereby converting the relative pseudo-damage matrix of the test field working condition into a contribution matrix. Specifically:

[0081] Among them, C represents the contribution matrix, C mn It represents the relative pseudo-damage value of the mth acquisition channel of the nth combined test field working condition. Each element in the result vector obtained by multiplying the contribution matrix C by the test field working condition cycle number vector N is the ratio of the cumulative damage of the test field working condition to the total damage target on the public road. The closer it is to 1, the better the correlation between the user working condition.

[0082] S4: According to the number of repetitions of the test field conditions, the contribution matrix is ​​reduced, and after deleting the test field conditions with a repetition number lower than the set number, the matching solution is performed again to output the set number of solutions that meet the expected target;

[0083] Furthermore, in one embodiment, the improved genetic algorithm is specifically:

[0084] A: Perform genetic algorithm settings, set the maximum genetic generation, crossover probability, mutation probability, set the value range and value interval of each variable in the test field working condition cycle number vector N, and use random assignment to output a set number of chromosomes as the initial population;

[0085] For example, the maximum genetic generation is set to 5000, the crossover probability is 0.5, the mutation probability is 0.005, the value range of each variable in the vector N is [0, 2000], the value interval is 50, and 100 chromosomes are generated as the initial population by random assignment.

[0086] B: Set the fitness function and classify the collection channels for correlation calculation according to their importance to obtain primary and secondary indicators;

[0087] The collection channels used in the correlation calculation are graded according to their importance. Specifically, the four wheel center vertical forces are used as primary indicators, with matching results required to be within the range of 0.8-1.2. The wheel center longitudinal force, lateral force, spring and shock absorber displacement, and steering rod axial force are used as secondary indicators, with matching results required to be within the range of 0.5-2.0. The number of collection channels with primary indicators falling between 0.8-1.2 is denoted as k1, and the number of collection channels with secondary indicators falling between 0.5-2.0 is denoted as k2.

[0088] The expression of the fitness function is:

[0089] f=λ·k1+k2

[0090] Where f represents the fitness function, k1 represents the number of acquisition channels corresponding to the primary indicator, k2 represents the number of acquisition channels corresponding to the secondary indicator, and λ represents the coefficient used to balance the compliance rates of the primary and secondary indicators. If all primary indicators meet the standards with high accuracy, but some secondary indicators have acquisition channels that do not meet the standards, then λ is adjusted to <1, otherwise, λ is adjusted to >1.

[0091] C: Using the maximum genetic generation number as the stopping condition, use the genetic algorithm to solve the problem respectively, take the optimal solution of each round as reserve, and ensure that the optimal solution of each round meets the expected target. Output the solutions that meet the expected target for the first set number of groups. Eliminate the test field conditions that appear less than the set number of times in the solutions that meet the expected target in the first set number of groups, thereby reducing the number of columns of the total target matrix of the frequency damage spectrum. Repeat this cycle until the solutions that meet the expected target for the set number of groups are output.

[0092] Specifically, the genetic algorithm is used to solve the problem with the maximum genetic generation number as the stopping condition, and the optimal solution of each round is taken as a backup. The optimal solution of each round meets the expected goal, and 50 groups of solutions that meet the expected goal are output. Then, on the premise of ensuring that the above steps can output 50 groups of solutions that meet the expected goal, the test field conditions that appear less than 100 times in the 50 groups of solution vectors are eliminated to reduce the number of columns of the total target matrix of the frequency damage spectrum. This cycle is repeated until 20 groups of solutions that meet the expected goal are output.

[0093] Afterwards, the total running time of the above 20 sets of solutions is calculated to see whether they meet the verification cycle requirements. At the same time, the distribution of the frequency damage spectrum under the test field conditions and the public road conditions is verified to be consistent. This allows the optimal combination to be screened out. The correlation calculation accuracy of this combination meets the technical requirements while also meeting the verification cycle and frequency damage spectrum requirements. By eliminating test field conditions that occur less frequently than the set number, the number of elements in the vector N, that is, the number of test field condition combinations, can be effectively reduced, making the final output condition cycle more feasible.

[0094] In the specific implementation process, if a total of 50 channels are collected, the dimension of the relative pseudo-damage matrix is ​​50×22. 21 collection channels are selected to calculate the fitness function, and the remaining 39 collection channels are used as reference channels. The four wheel center vertical forces are used as the first-level indicators, and the matching results are required to be within the range of 0.8-1.2; the wheel center longitudinal force, lateral force, spring and shock absorber displacement, steering rod axial force and other collection channels are used as second-level indicators, and the matching results are required to be within the range of 0.5-2.0. The number of collection channels with the first-level indicators falling within the range of 0.8-1.2 is recorded as k1, and the number of collection channels with the second-level indicators falling within the range of 0.5-2.0 is recorded as k2. The fitness function can be expressed as f=λ·k1+k2, and the maximum genetic generation number of 5000 is used as the stopping condition. 20 sets of solutions that meet the expected goals are output as input for the next step. Among them, the output partial solutions (10 groups) are shown in Table 1 below.

[0095] Table 1

[0096] The relevant collection channels for primary and secondary indicators are shown in Table 2 below.

[0097] Table 2

[0098] Then, based on the number of repetitions of the test field conditions, the contribution matrix is ​​reduced. After deleting the test field conditions with less than 100 repetitions, the matching and solution are performed again, and 20 sets of solutions that meet the expected goals are output again.

[0099] S5: Calculate whether the total running time of each group of solutions that meet the expected goals meets the verification cycle requirements. At the same time, verify whether the distribution of the frequency damage spectrum under the test site conditions and the public road conditions is consistent. Select the test site condition combination with the shortest running time as the optimal solution.

[0100] For example, 10 groups of solutions that meet the frequency damage spectrum and relative pseudo-damage targets are finally output, and the test field condition combination with the shortest running time is finally selected as the optimal solution, as shown in Table 3 below.

[0101] Table 3

[0102] The test field operating condition combination calculation method of the embodiment of the present application uses an improved genetic algorithm and fitness function to quickly solve a solution that meets the relative pseudo-damage accuracy requirements, the frequency domain damage spectrum accuracy requirements, and the test duration requirements, and realizes the calculation of the test field operating condition combination associated with the public road operating condition and the test field. It can quickly calculate the test field operating condition combination that meets the accuracy requirements and has good executability, and only the number of operating conditions needs to be adjusted during the solution process. The calculation method of the present application has high applicability and is easy to promote and apply.

[0103] In a second aspect, an embodiment of the present application also provides a test field working condition combination calculation device.

[0104] In one embodiment, referring to Figure 2, which is a schematic diagram of the functional modules of the test field operating condition combination calculation device of the present application, the test field operating condition combination calculation device includes a calculation module, a statistics module, a first output module, a second output module, and a selection module.

[0105] The calculation module is used to collect load spectrum data of various types of road surfaces on public roads based on multiple acquisition channels composed of sensors installed on the test prototype, calculate relative pseudo-damage and frequency damage spectrum, and calculate the total target of relative pseudo-damage and total target of frequency damage spectrum according to the ratio of total target mileage to various types of road surfaces; the statistical module is used to collect load spectrum data under various test field conditions of the test field based on the test prototype, and connect the collected load spectrum data according to the driving route, and count the relative pseudo-damage, frequency damage spectrum and the time required for the test field conditions to run; the first output module is used to establish the correlation matrix equation and solve it to convert the relative pseudo-damage matrix of each test field condition into a contribution matrix. The contribution matrix is ​​used to classify the associated calculation acquisition channels, and a constrained improved genetic algorithm is used in combination with fitness functions of different matching requirements to perform matching solutions, and output solutions for the expected targets of a set number of groups; the second output module is used to reduce the contribution matrix according to the number of repetitions of the test field conditions, and after deleting the test field conditions with a number of repetitions lower than the set number, perform matching solutions again to output solutions for a set number of groups that meet the expected targets; the selection module is used to calculate whether the total running time of each group of solutions that meet the expected targets meets the verification cycle requirements, and at the same time verify whether the distribution of the frequency damage spectrum under the test field conditions and that under the public road conditions is consistent, and select the test field condition combination with the shortest running time as the optimal solution.

[0106] In a third aspect, an embodiment of the present application provides a test field working condition combination calculation device, which can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0107] 3, which is a schematic diagram of the hardware structure of the test field working condition combination calculation device involved in the embodiment of the present application. In the embodiment of the present application, the test field working condition combination calculation device may include a processor, a memory, a communication interface, and a communication bus.

[0108] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0109] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, used to interconnect components within the test field working condition combination computing device, as well as interfaces used to interconnect the test field working condition combination computing device with other devices (such as other computing devices or user devices). Physical interfaces can be Ethernet, fiber, or ATM interfaces; user devices can be displays, keyboards, and other devices.

[0110] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0111] The processor may be a general-purpose processor, which may call a test field operating condition combination calculation program stored in a memory and execute the test field operating condition combination calculation method provided in an embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the test field operating condition combination calculation program is called may refer to the various embodiments of the test field operating condition combination calculation method of the present application, and will not be repeated here.

[0112] Those skilled in the art will understand that the hardware structure shown in FIG3 does not constitute a limitation on the present application, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0113] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0114] The computer-readable storage medium of the present application stores a test field operating condition combination calculation program, wherein when the test field operating condition combination calculation program is executed by a processor, the steps of the test field operating condition combination calculation method as described above are implemented.

[0115] Among them, the method implemented when the test field working condition combination calculation program is executed can refer to the various embodiments of the test field working condition combination calculation method of this application, and will not be repeated here.

[0116] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0117] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0118] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0119] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0120] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0121] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0122] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for calculating the combination of test field working conditions, characterized in that: The test field working condition combination calculation method includes: Based on multiple acquisition channels composed of sensors installed on the test vehicle, load spectrum data of various types of road surfaces on public roads are collected, relative pseudo-damage and frequency damage spectrum are calculated, and the total relative pseudo-damage target and frequency damage spectrum target are calculated based on the ratio of the total target mileage to each type of road surface. Collect load spectrum data under various test field conditions on the test vehicle, connect the collected load spectrum data according to the driving route, and calculate the relative pseudo-damage and frequency damage spectrum of each test field condition, as well as the time required for the test field condition to run; Establish and solve the correlation matrix equation to convert the relative pseudo-damage matrix of each test site working condition into a contribution matrix. Then, classify the correlation calculation acquisition channels and use a constrained improved genetic algorithm combined with fitness functions of different matching requirements to perform matching and output the solution for the expected target of the set number of groups. According to the number of repetitions of the test field conditions, the contribution matrix is ​​reduced, and after deleting the test field conditions with a repetition number lower than the set number, the matching solution is performed again to output the set number of solutions that meet the expected goals; Calculate whether the total running time of each group of solutions that meet the expected goals meets the verification cycle requirements. At the same time, verify whether the distribution of the frequency damage spectrum under the test field conditions and the public road conditions is consistent. Select the test field condition combination with the shortest running time as the optimal solution.

2. A test field operating condition combination calculation method according to claim 1, characterized in that: The multiple acquisition channels formed by the sensors installed on the test vehicle collect load spectrum data of various types of road surfaces on public roads, calculate relative pseudo-damage and frequency damage spectrum, and calculate the total relative pseudo-damage target and the total frequency damage spectrum target based on the ratio of the total target mileage to each type of road surface. Specifically, A wheel six-component force sensor, a spring displacement sensor, a chassis component force sensor, and a wheel center acceleration sensor are installed on the test vehicle to form multiple acquisition channels for data collection; Collect load spectrum data of various types of pavement on public roads and calculate relative pseudo-damage and frequency damage spectra; According to the ratio of the total target mileage to each type of road surface, the total target of relative pseudo damage and the total target of frequency damage spectrum are calculated.

3. A test field operating condition combination calculation method according to claim 2, characterized in that: The calculation of the relative pseudo-damage total target and the frequency damage spectrum total target is specifically as follows: Based on the linear cumulative damage theory, the relative pseudo-damage total target of each acquisition channel is calculated. Specifically: P=[P1,P2…P m ] T Where P represents the relative pseudo-damage target of each acquisition channel, P m represents the relative pseudo-damage value of the mth acquisition channel, m represents the total number of acquisition channels, and T represents the inversion of evidence; After bandpass filtering the time domain signal in different frequency bands, the relative pseudo damage is calculated to characterize the distribution of the relative pseudo damage value in different frequency bands and obtain the overall target of the frequency damage spectrum. Specifically: Where R represents the total target of the frequency damage spectrum, R km represents the frequency damage spectrum, R km The m in the equation represents the number of acquisition channels involved in the analysis, and R km The k in represents the number of segments of the frequency impairment spectrum.

4. A test field operating condition combination calculation method according to claim 2, characterized in that: The correlation matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix. Specifically, the correlation matrix equation is established as follows: Using the calculated relative pseudo-damage and frequency damage spectrum of public roads as target values, select the test field conditions and determine the corresponding number of repetitions so that the cumulative damage and distribution of the test field condition cycle combination can reproduce the damage on public roads; For the correlation between public road and test field conditions, the specific correlation matrix equation is: Where D represents the matrix composed of relative pseudo damage values ​​under the test field working condition, that is, the relative pseudo damage matrix of the test field working condition, D mn represents the relative pseudo damage value of the mth acquisition channel of the nth test field working condition, N represents the number of test field working condition cycles vector, N n represents the number of cycles corresponding to the nth test field condition, P represents the target value vector, P m It represents the relative pseudo damage value of the mth acquisition channel under the target mileage of the public road.

5. A test field operating condition combination calculation method according to claim 4, characterized in that: The correlation matrix equation is established and solved to convert the relative pseudo-damage matrix of each test field working condition into a contribution matrix. Specifically, the relative pseudo-damage matrix of each test field working condition is converted into a contribution matrix as follows: Taking the maximum vertical force value of the data collected on the public road as the benchmark, select the test field conditions whose extreme value does not exceed the maximum vertical force value of the public road; Using the proving ground route and actual driving route requirements as constraints, the proving ground condition data is spliced, and multiple individual proving ground conditions in each area of ​​the proving ground are combined into a combined proving ground condition. Normalize the correlation matrix equation and divide each acquisition channel in the matrix D by each acquisition channel in the target value vector P, so as to convert the relative pseudo-damage matrix of the test field working condition into a contribution matrix. Specifically: Among them, C represents the contribution matrix, C mn It represents the relative pseudo-damage value of the mth acquisition channel of the nth combined test field working condition. The elements in the result vector obtained by multiplying the contribution matrix C by the test field working condition cycle number vector N are the ratios of the cumulative damage of the test field working condition to the total damage target under the public road.

6. A test field operating condition combination calculation method according to claim 5, characterized in that: The improved genetic algorithm is specifically: Perform genetic algorithm settings, set the maximum genetic generation, crossover probability, mutation probability, set the value range and value interval of each variable in the test field working condition cycle number vector N, and use random assignment to output a set number of chromosomes as the initial population; Set the fitness function, classify the collection channels of the correlation calculation according to their importance, and obtain the primary and secondary indicators; Taking the maximum genetic generation as the stopping condition, the genetic algorithm is used to solve the problem respectively, and the optimal solution of each round is taken as a standby, and the optimal solution of each round meets the expected goal. The first set number of solutions that meet the expected goal are output, and the test field conditions that appear less than the set number in the first set number of solutions that meet the expected goal are eliminated to reduce the number of columns of the total target matrix of the frequency damage spectrum. This cycle is repeated until the set number of solutions that meet the expected goal are output.

7. A test field operating condition combination calculation method according to claim 5, characterized in that: The fitness function is expressed as: f = λ·k1+k2 Among them, f represents the fitness function, k1 represents the number of collection channels corresponding to the first-level indicator, k2 represents the number of collection channels corresponding to the second-level indicator, and λ represents the coefficient used to balance the achievement rates of the first-level and second-level indicators.

8. A test field working condition combination calculation device, characterized in that: The test field working condition combination calculation device includes: A calculation module is used to collect load spectrum data of various types of road surfaces on public roads based on multiple acquisition channels formed by sensors installed on the test vehicle, calculate relative pseudo-damage and frequency damage spectrum, and calculate the total relative pseudo-damage target and the total frequency damage spectrum target based on the ratio of the total target mileage to each type of road surface; The statistical module is used to collect load spectrum data under various test field conditions based on the test vehicle, connect the collected load spectrum data according to the driving route, and calculate the relative pseudo-damage and frequency damage spectrum of each test field condition, as well as the time required for the test field condition to run; The first output module is used to establish and solve the correlation matrix equation to convert the relative pseudo-damage matrix of each test site working condition into a contribution matrix, classify the correlation calculation acquisition channels, and use a constrained improved genetic algorithm combined with fitness functions with different matching requirements to perform matching solutions and output the solution for the expected target of the set number of groups; The second output module is used to reduce the contribution matrix according to the number of repetitions of the test field conditions, delete the test field conditions with a repetition number lower than the set number, and then perform matching and solving again to output a set number of solutions that meet the expected goals; The selection module is used to calculate whether the total running time of each group of solutions that meet the expected goals meets the verification cycle requirements. At the same time, it verifies whether the distribution of the frequency damage spectrum under the test field conditions and the public road conditions is consistent, and selects the test field condition combination with the shortest running time as the optimal solution.

9. A test field working condition combination calculation device, characterized in that: The test field operating condition combination calculation device includes a processor, a memory, and a test field operating condition combination calculation program stored in the memory and executable by the processor, wherein when the test field operating condition combination calculation program is executed by the processor, the steps of the test field operating condition combination calculation method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a test field operating condition combination calculation program, wherein when the test field operating condition combination calculation program is executed by a processor, the steps of the test field operating condition combination calculation method according to any one of claims 1 to 7 are implemented.

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