Engine robustness optimization method, device, computer equipment and storage medium
By screening and optimizing the key impact parameters of engine performance emissions, and formulating adjustment plans to narrow the boundary range, the problem of cost and robustness balance in engine production is solved, achieving dual optimization of performance and cost.
Patent Information
- Application Number
- CN202411583618.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-07
- Publication Date
- 2025-04-08
AI Technical Summary
In the process of mass production of engines, how to balance production costs and product robustness, especially when facing higher requirements for emission standards upgrades and performance fluctuations.
By determining multiple impact parameters of engine performance emissions and their boundary ranges, select target parameters that have a greater impact on performance emissions, formulate adjustment plans to narrow the boundary range of target parameters, and combine optimization based on the cost and effect of multiple adjustment plans to determine the target boundary range to achieve a balance of robustness and cost.
The goal of reducing production costs while improving engine performance and emission robustness is achieved, ensuring the balance of product performance and cost.
Smart Images
Figure CN119783310A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to an engine robustness optimization method, device, computer equipment and storage medium. Background Art
[0002] Due to production consistency, changes in environmental conditions, aging of components during use, etc., the performance emissions of mass-produced engines will be scattered within a certain range. With the upgrading of emission standards, the control of the fluctuation range of engine performance emissions is becoming more and more stringent, which puts higher requirements on the robustness of design and development and vehicle matching.
[0003] By controlling the boundary range of key influencing factors affecting the engine's performance emissions and controlling the dispersion distribution of performance emissions within a smaller range, the robustness of the engine's performance emissions can be improved; however, controlling the boundaries of key influencing factors within a smaller range will increase production costs.
[0004] Therefore, there is an urgent need for a boundary range adjustment scheme that balances the key influencing factors of production cost and product robustness for mass production of engines. Summary of the invention
[0005] In view of this, the present disclosure proposes an engine robustness optimization method, apparatus, computer equipment and storage medium to solve the problem of how to balance production cost and product robustness during engine mass production.
[0006] The first embodiment of the present disclosure provides an engine robustness optimization method, the method comprising:
[0007] Determine multiple influencing parameters of engine performance emissions and the boundary range of each influencing parameter;
[0008] Filtering out a plurality of target parameters from the plurality of influencing parameters; each target parameter refers to an influencing parameter whose influence on performance emissions is greater than a preset threshold among the plurality of influencing parameters;
[0009] For any one of the plurality of target parameters, determining at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme; any adjustment scheme is used to indicate adjusting a corresponding component of the engine so that a boundary range of the target parameter is reduced;
[0010] Determining target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and a first cost amount increased by each adjustment scheme;
[0011] The engine is mass-produced according to the target boundary ranges of the plurality of target parameters.
[0012] The disclosed embodiment determines an adjustment scheme for narrowing the boundary range of the target parameters, as well as the cost increase corresponding to the scheme, and determines the target boundary range of multiple target parameters based on all adjustment schemes corresponding to the multiple target parameters, as well as the first cost amount increased by each adjustment scheme, thereby achieving the purpose of reducing the generation cost while improving the robustness of engine performance emissions.
[0013] In the embodiment of the present disclosure, determining the target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme includes:
[0014] Combining all adjustment schemes corresponding to the multiple target parameters to obtain multiple scheme combinations; wherein the multiple schemes contained in each scheme combination correspond one-to-one to the multiple target parameters;
[0015] Performing simulation calculations on engine performance emissions according to the boundary ranges of the multiple target parameters in each combination, and obtaining first simulation results corresponding to each combination;
[0016] Screening out a target combination from the multiple combinations according to the multiple first simulation results corresponding to the multiple combinations and the second cost amount increased by each combination;
[0017] The boundary ranges of the multiple target parameters in the target combination are used as the target boundary ranges of the multiple target parameters.
[0018] In the embodiment of the present disclosure, a plurality of target parameters are screened out from the plurality of influencing parameters, including:
[0019] Combining the multiple influencing parameters under different setting values to obtain multiple samples;
[0020] Performing simulation calculations on engine performance emissions according to the multiple samples to obtain multiple performance emission results corresponding to the multiple samples;
[0021] Performing statistical analysis on the multiple performance emission results to obtain ranking results of the multiple influencing parameters; the ranking results are ranked according to the proportion of influence of different influencing parameters on performance emissions;
[0022] The influence parameter whose influence ratio in the sorting result is greater than the preset threshold is used as the target parameter.
[0023] In the embodiment of the present disclosure, the first simulation result is a performance emission normal distribution diagram; according to the plurality of first simulation results corresponding to the plurality of combinations and the second cost amount increased by each combination, a target combination is screened out from the plurality of combinations, including:
[0024] Determine the corresponding standard deviation based on the performance emission normal distribution diagram corresponding to each combination;
[0025] Constructing a correlation graph according to a plurality of standard deviations corresponding to a plurality of combinations and a second cost amount added to each combination; the correlation graph is used to represent the correlation distribution between the second cost amount and the standard deviation of the plurality of combinations;
[0026] The combination in the correlation graph that meets the preset conditions is used as the target combination.
[0027] In the embodiment of the present disclosure, the preset condition includes a first condition and a second condition; taking the combination in the correlation graph that satisfies the preset condition as the target combination includes:
[0028] The combination in the correlation graph that satisfies both the first condition and the second condition is taken as the target combination; wherein the first condition refers to that the standard deviation is greater than a preset standard deviation, and the second condition refers to that the second cost amount is the lowest.
[0029] In the embodiment of the present disclosure, all adjustment schemes corresponding to the multiple target parameters are combined to obtain multiple scheme combinations, including:
[0030] For any one of the multiple target parameters, taking any one of the at least one adjustment scheme corresponding to the target parameter as a target scheme;
[0031] The multiple target solutions corresponding to the multiple target parameters are combined to obtain a solution combination.
[0032] In the embodiment of the present disclosure, before determining a plurality of influencing parameters of engine performance and emission, the method further includes:
[0033] Establishing an initial model; the initial model is used to simulate the performance of engine performance emissions under changes in different influencing parameters;
[0034] Adjusting at least one influencing parameter in the initial model, and using the initial model after the parameter adjustment to perform simulation calculations on engine performance emissions to obtain a second simulation result;
[0035] If the second simulation result is the same as the actual test result, the initial model is used as the target model; the target model is used for simulation calculation of engine performance emission;
[0036] If the second simulation result is different from the actual test result, the initial model is optimized.
[0037] An embodiment of a second aspect of the present disclosure provides an engine robustness optimization device, comprising:
[0038] A parameter determination module, used to determine multiple influencing parameters of engine performance emissions and a boundary range of each influencing parameter;
[0039] A parameter screening module, used to screen out a plurality of target parameters from the plurality of influencing parameters; each target parameter refers to an influencing parameter whose influence on performance emission accounts for a proportion greater than a preset threshold among the plurality of influencing parameters;
[0040] a scheme determination module, for determining, for any one of the plurality of target parameters, at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme; any adjustment scheme is used to indicate adjusting a corresponding component of the engine so that a boundary range of the target parameter is reduced;
[0041] A target boundary range determination module, configured to determine the target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme;
[0042] A batch production module is used to carry out batch production of engines according to target boundary ranges of the multiple target parameters.
[0043] An embodiment of the third aspect of the present disclosure provides an electronic device, which includes a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the engine robustness optimization method described in the first aspect by executing the computer instructions.
[0044] An embodiment of the fourth aspect of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the engine robustness optimization method described in the first aspect.
[0045] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present disclosure. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.
[0047] In the attached picture:
[0048] Figure 1 A schematic diagram of a flow chart of an engine robustness optimization method provided by an embodiment of the present disclosure is shown;
[0049] Figure 2 A schematic diagram showing a flow chart of another engine robustness optimization method provided by an embodiment of the present disclosure;
[0050] Figure 3 A schematic diagram showing the ranking of the influence ratios of different influencing parameters on performance emissions provided by an embodiment of the present disclosure;
[0051] Figure 4 A schematic diagram showing a flow chart of another engine robustness optimization method provided by an embodiment of the present disclosure;
[0052] Figure 5 A schematic diagram showing distribution of multiple performance emission results provided by an embodiment of the present disclosure is shown;
[0053] Figure 6 A schematic diagram showing the distribution of performance emission results under different standard deviations provided by an embodiment of the present disclosure;
[0054] Figure 7 A schematic diagram showing the correlation between cost and standard deviation provided by an embodiment of the present disclosure;
[0055] Figure 8 A schematic structural diagram of an engine robustness optimization device provided by an embodiment of the present disclosure is shown;
[0056] Fig. 9 A schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure is shown;
[0057] Fig.10 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0058] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0059] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.
[0060] The following describes relevant terms involved in the embodiments of the present disclosure.
[0061] Robustness: refers to the ability of a system to remain stable and reliable under different circumstances.
[0062] Top dead center: When the piston moves in the cylinder, the highest position of the piston top in the cylinder is called the top dead center. The deviation of the top dead center will be reflected in the injection advance angle.
[0063] Injector flow coefficient: The injector flow will fluctuate within a certain range. The injector flow is a, the flow coefficient is b, and the actual flow of the injector is a*(1+b).
[0064] Compression ratio: The ratio of the cylinder volume when the piston is at the bottom dead center to the cylinder volume when the piston is at the top dead center.
[0065] According to an embodiment of the present disclosure, an embodiment of an engine robustness optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0066] In this embodiment, a method for optimizing engine robustness is provided. Figure 1 is a flow chart of an engine robustness optimization method according to an embodiment of the present disclosure, such as Figure 1 As shown, the process includes the following steps:
[0067] Step S101, determining multiple influencing parameters of engine performance emission and a boundary range of each influencing parameter.
[0068] In the disclosed embodiment, multiple influencing parameters affecting engine performance and emissions, as well as the value range of each influencing parameter, can be determined based on actual data in the production process, wherein the influencing parameters include but are not limited to top dead center, compression ratio, injector flow coefficient, intercooler pressure drop, and exhaust back pressure.
[0069] Step S102: Filter out a plurality of target parameters from the plurality of influencing parameters.
[0070] In the embodiment of the present disclosure, each target parameter refers to an influencing parameter whose influence on performance emission is greater than a preset threshold among the multiple influencing parameters. The preset threshold can be set according to actual conditions and is not specifically limited here.
[0071] In some specific embodiments, the above step S102 includes steps S1021 to S1024:
[0072] Step S1021: Combine the multiple influencing parameters under different setting values to obtain multiple samples.
[0073] In the embodiment of the present disclosure, step S1021 is described by the following specific example:
[0074] When there are three influencing parameters, namely, influencing parameter A, influencing parameter B and influencing parameter C; influencing parameter A includes three set values, namely, A1, A2 and A3; influencing parameter B includes two set values, namely, B1 and B2; influencing parameter C includes three set values, namely, C1, C2 and C3, 3*2*3=18 samples can be obtained by combination. Among them, the multiple set values included in each sample correspond to the multiple influencing parameters one by one, for example, the sample can include A1, B1, C1, and the sample can also include A2, B1, C2.
[0075] Step S1022: performing simulation calculations of engine performance emissions according to the multiple samples to obtain multiple performance emission results corresponding to the multiple samples.
[0076] In the disclosed embodiment, the simulation calculation is performed based on a virtual test bed (VTB) and a median engine virtual calibration model. The median engine virtual calibration model can be established using CruiseM software, and then the specific values of the influencing parameters (i.e., the above-mentioned set values) can be set in the median engine virtual calibration model. Finally, the median virtual calibration model is compiled into a Simulink model and integrated into the virtual calibration bed for simulation calculation, for example Figure 2 shown.
[0077] In the disclosed embodiment, the performance emission result refers to the performance emission parameters of the simulated engine, for example, NOx specific emission; wherein the simulated engine is obtained by simulating according to the set values of multiple influencing parameters in the sample.
[0078] Step S1023: Perform statistical analysis based on the multiple performance emission results to obtain ranking results of the multiple influencing parameters.
[0079] In the embodiment of the present disclosure, the ranking results are ranked according to the influence ratio of different influencing parameters on performance emissions, for example Figure 3 shown.
[0080] Step S1024: taking the influence parameter whose influence ratio in the sorting result is greater than the preset threshold as the target parameter.
[0081] In the embodiment of the present disclosure, the preset threshold can be set to 12%. Figure 3 It can be seen that the target parameters include top dead center, compression ratio and injector flow coefficient.
[0082] In some specific embodiments, a preset number (for example, 3) of influencing parameters with the largest influence ratio in the sorting results may be used as the target parameters, for example Figure 4 "Select three variables with greater sensitivity" in the above. Sensitivity can be understood as the above-mentioned impact ratio.
[0083] In some specific embodiments, the method further includes: performing statistical analysis based on the multiple performance emission results to obtain a distribution diagram of the performance emission results, such as Figure 5 shown.
[0084] In some specific embodiments, the discreteness of the distribution of engine performance emission results can be reduced by reducing the upper and lower limits of the influencing parameters. The discreteness can be characterized by the standard deviation. The smaller the standard deviation, the better the product consistency. The schematic diagram of the performance emission result distribution with standard deviation = 1 and standard deviation = 2 is shown in FIG. Figure 6 As shown in the figure, the smaller the boundary of the influencing parameter is, the more difficult it is to control the production accuracy and the higher the cost is.
[0085] Step S103: for any one of the multiple target parameters, determine at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme.
[0086] The above adjustment plan can be understood as Figure 4 The first cost can be understood as the “feasible solution” Figure 4 “The amount of additional cost required for each feasible option”.
[0087] In the embodiment of the present disclosure, any adjustment scheme is used to indicate that the corresponding components of the engine are adjusted so that the boundary range of the target parameter is reduced. For example, by adjusting component 1, adjusting component 2 or adjusting component 3, the boundary range of target parameter 1 can be changed from "0.5-1.5" to "0.3-1", wherein adjusting component 1, adjusting component 2 and adjusting component 3 can each be used as an adjustment scheme. The first cost amount can be understood as the cost required to adjust the corresponding components of the engine.
[0088] Step S104: determining target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme.
[0089] In some specific embodiments, the above step S104 includes steps S1041 to S1044:
[0090] Step S1041: Combining all adjustment schemes corresponding to the multiple target parameters to obtain multiple scheme combinations.
[0091] In the embodiment of the present disclosure, the multiple schemes included in each scheme combination correspond one-to-one to the multiple target parameters. For example, scheme combination 1 includes adjustment scheme 1, adjustment scheme 2 and adjustment scheme 3, wherein adjustment scheme 1 is used to narrow the boundary range of target parameter 1, adjustment scheme 2 is used to narrow the boundary range of target parameter 2, and adjustment scheme 3 is used to narrow the boundary range of target parameter 3.
[0092] In the embodiment of the present disclosure, all adjustment schemes corresponding to multiple target parameters can be combined to obtain multiple scheme combinations through the following steps a1-a2:
[0093] Step a1, for any one of the multiple target parameters, taking any one of the at least one adjustment scheme corresponding to the target parameter as a target scheme;
[0094] Step a2, combining multiple target solutions corresponding to the multiple target parameters one by one to obtain a solution combination.
[0095] The above steps a1 to a2 are explained with reference to a specific example: Assume that there are three target parameters, namely target parameter 1, target parameter 2 and target parameter 3; target parameter 1 corresponds to three adjustment schemes (e.g. a1, a2 and a3), target parameter 2 corresponds to two adjustment schemes (e.g. b1 and b2), and target parameter 3 corresponds to two adjustment schemes (e.g. c1 and c2), then 3*2*4=24 scheme combinations can be combined. Among them, a1, b1 and c1 can be used as a scheme combination.
[0096] Step S1042, performing simulation calculations of engine performance emissions according to the boundary ranges of the multiple target parameters in each combination, and obtaining a first simulation result corresponding to each combination.
[0097] In the disclosed embodiment, each combination includes multiple adjustment schemes corresponding to multiple target parameters one by one, each adjustment scheme can narrow the boundary range of the corresponding target parameter, and the boundary range of multiple target parameters can be determined through each combination; the boundary range of multiple target parameters is used to perform simulation calculations of engine performance emissions to obtain the first simulation result corresponding to each combination.
[0098] Step S1043: Filter out a target combination from the multiple combinations according to the multiple first simulation results corresponding to the multiple combinations and the second cost amount increased by each combination.
[0099] In the embodiment of the present disclosure, the first simulation result refers to a performance emission normal distribution diagram, which includes multiple sets of NOx (g / kWh) data, such as Figure 5 shown.
[0100] In some specific embodiments, the above step S1043 includes steps S201 to S203:
[0101] Step S201, determining the corresponding standard deviation according to the performance emission normal distribution diagram corresponding to each combination.
[0102] In the embodiment of the present disclosure, the standard deviation can be calculated by the following formula:
[0103]
[0104] Wherein, Z represents the standard deviation, x represents each group of data in the performance emission normal distribution diagram, μ represents the mean of multiple groups of data, and n represents the number of groups of data.
[0105] Step S202: construct a correlation graph according to the multiple standard deviations corresponding to the multiple combinations and the second cost amount increased by each combination.
[0106] In the embodiment of the present disclosure, the second cost amount refers to the sum of the first cost amounts corresponding to the multiple adjustment schemes included in the corresponding combination. The correlation graph is used to represent the correlation distribution between the second cost amounts and the standard deviations of the multiple combinations, for example Figure 7 As shown, the second cost amount of each combination is used as the X-axis and the standard deviation is used as the Y-axis.
[0107] Step S203: taking the combination in the correlation graph that meets the preset condition as the target combination.
[0108] In the embodiment of the present disclosure, the preset condition includes a first condition and a second condition, wherein the first condition refers to that the standard deviation is greater than the preset standard deviation, and the second condition refers to that the second cost amount is the lowest. A combination that satisfies both the first condition and the second condition in the correlation graph can be used as the target combination. The preset standard deviation can be set according to actual conditions and is not specifically limited here.
[0109] Step S1044: Using the boundary ranges of the multiple target parameters in the target combination as the target boundary ranges of the multiple target parameters.
[0110] Step S105 , performing mass production of engines according to the target boundary ranges of the multiple target parameters.
[0111] In the disclosed embodiment, by mass producing engines according to target boundary ranges of multiple target parameters, it is possible to reduce the boundary ranges of influencing parameters while controlling production costs, thereby improving the robustness of engine performance emissions.
[0112] In some specific embodiments, before the above step S101, the method further includes:
[0113] Step S301, establishing an initial model.
[0114] In the disclosed embodiment, the initial model is used to simulate the performance of engine emissions under different influencing parameters, and is established using CruiseM software.
[0115] Step S302, adjusting at least one influencing parameter in the initial model, and using the initial model after parameter adjustment to perform simulation calculations on engine performance emissions to obtain a second simulation result.
[0116] Step S303, if the second simulation result is the same as the actual test result, the initial model is used as the target model; the target model is used for simulation calculation of engine performance emission;
[0117] Step S304: If the second simulation result is different from the actual test result, the initial model is optimized.
[0118] In the embodiment of the present disclosure, at least one influencing parameter of the initial model is adjusted, and then the simulation results of the comparator are compared to see whether they are consistent with the actual test results. If they are consistent, the validity of the initial model is determined. If not, the initial model needs to be optimized.
[0119] The embodiments of the present disclosure have the following technical effects:
[0120] 1. Use virtual calibration method to conduct robustness research and find the key variable parameters that affect the robustness of diesel engine performance and emission. There is no measurement error, good repeatability, easier variable control, and can reduce test expenses and test costs.
[0121] 2. Seek feasible solutions to reduce the boundary range of key variable parameters, combine feasible solutions of different variable parameters, generate samples, and obtain the scattered distribution of diesel engine performance emissions under different samples.
[0122] 3. The robustness of diesel engine performance emissions is characterized by standard deviation, and the correlation between cost increase and standard deviation under different combinations is obtained. Under a given standard deviation, the combination of key variable parameters with the lowest cost can be found, which not only improves the robustness of diesel engine performance emissions, but also achieves the lowest cost.
[0123] Corresponding to the implementation of the above engine robustness optimization method, the embodiment of the present disclosure also provides an engine robustness optimization device for executing the above Figures 1 to 7 The engine robustness optimization method described in any of the illustrated embodiments. Figure 8 As shown, the engine robustness optimization device includes:
[0124] A parameter determination module, used to determine multiple influencing parameters of engine performance emissions and a boundary range of each influencing parameter;
[0125] A parameter screening module, used to screen out a plurality of target parameters from the plurality of influencing parameters; each target parameter refers to an influencing parameter whose influence on performance emission accounts for a proportion greater than a preset threshold among the plurality of influencing parameters;
[0126] a scheme determination module, for determining, for any one of the plurality of target parameters, at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme; any adjustment scheme is used to indicate adjusting a corresponding component of the engine so that a boundary range of the target parameter is reduced;
[0127] A target boundary range determination module, configured to determine the target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme;
[0128] A batch production module is used to carry out batch production of engines according to target boundary ranges of the multiple target parameters.
[0129] Optionally, the target boundary range determination module is also used to: combine all adjustment schemes corresponding to the multiple target parameters to obtain multiple scheme combinations; wherein the multiple schemes contained in each scheme combination correspond one-to-one to the multiple target parameters; perform simulation calculations of engine performance emissions according to the boundary range of the multiple target parameters under each combination to obtain a first simulation result corresponding to each combination; select a target combination from the multiple combinations according to the multiple first simulation results corresponding to the multiple combinations and the second cost amount increased by each combination; and use the boundary range of the multiple target parameters in the target combination as the target boundary range of the multiple target parameters.
[0130] Optionally, the parameter screening module is also used to: combine the multiple influencing parameters under different set values to obtain multiple samples; perform simulation calculations of engine performance emissions based on the multiple samples to obtain multiple performance emission results that correspond to the multiple samples one by one; perform statistical analysis based on the multiple performance emission results to obtain ranking results of the multiple influencing parameters; the ranking results are ranked according to the proportion of influence of different influencing parameters on performance emissions; and the influencing parameter in the ranking results whose influence proportion is greater than the preset threshold is used as the target parameter.
[0131] Optionally, the target boundary range determination module is also used to: determine the corresponding standard deviation based on the performance emission normal distribution graph corresponding to each combination; construct a correlation graph based on multiple standard deviations corresponding to multiple combinations and the second cost amount added to each combination; the correlation graph is used to represent the correlation distribution between the second cost amount and the standard deviation of the multiple combinations; and take the combination in the correlation graph that meets preset conditions as the target combination.
[0132] Optionally, the target boundary range determination module is also used to: take the combination in the correlation graph that satisfies both the first condition and the second condition as the target combination; wherein the first condition refers to a standard deviation greater than a preset standard deviation, and the second condition refers to a lowest second cost amount.
[0133] Optionally, the target boundary range determination module is also used to: for any target parameter among multiple target parameters, take any adjustment scheme among at least one adjustment scheme corresponding to the target parameter as a target scheme; and combine multiple target schemes corresponding to the multiple target parameters one by one to obtain a scheme combination.
[0134] Optionally, the device also includes: a model building module, used to build an initial model; the initial model is used to simulate the performance of engine performance emissions under different changes in influencing parameters; at least one influencing parameter in the initial model is adjusted, and the initial model after the parameter adjustment is used to simulate the engine performance emissions to obtain a second simulation result; if the second simulation result is the same as the actual test result, the initial model is used as the target model; the target model is used for simulation calculations of engine performance emissions; if the second simulation result is different from the actual test result, the initial model is optimized.
[0135] The engine robustness optimization device provided by the above-mentioned embodiment of the present disclosure and the engine robustness optimization method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0136] The disclosed embodiment also provides an electronic device to execute the above engine robustness optimization method. Fig. 9 , which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. Fig. 9 As shown, the electronic device 9 includes: a processor 900, a memory 901, a bus 902 and a communication interface 903, wherein the processor 900, the communication interface 903 and the memory 901 are connected via the bus 902; the memory 901 stores a computer program that can be run on the processor 900, and the processor 900 executes the aforementioned Figures 1 to 7 An engine robustness optimization method is provided in any of the illustrated embodiments.
[0137] The memory 901 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 903 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.
[0138] The bus 902 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 901 is used to store programs. After receiving the execution instruction, the processor 900 executes the program. Figures 1 to 7 The engine robustness optimization method disclosed in any of the illustrated embodiments may be applied to the processor 900 or implemented by the processor 900 .
[0139] The processor 900 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 900 or the instruction in the form of software. The above processor 900 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 901, and the processor 900 reads the information in the memory 901 and completes the steps of the above method in combination with its hardware.
[0140] The electronic device provided by the embodiment of the present disclosure and the engine robustness optimization method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented by them.
[0141] The present disclosure also provides a computer-readable storage medium corresponding to the engine robustness optimization method provided in the above embodiment. Fig.10 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the engine robustness optimization method provided in any of the aforementioned embodiments is executed.
[0142] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.
[0143] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the engine robustness optimization method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0144] It should be noted that:
[0145] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.
[0146] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.
[0147] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.
[0148] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A method for optimizing engine robustness, characterized in that: The method comprises: Determine multiple influencing parameters of engine performance emissions and the boundary range of each influencing parameter; Filtering out a plurality of target parameters from the plurality of influencing parameters; each target parameter refers to an influencing parameter whose influence on performance emissions is greater than a preset threshold among the plurality of influencing parameters; For any one of the plurality of target parameters, determining at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme; any adjustment scheme is used to indicate adjusting a corresponding component of the engine so that a boundary range of the target parameter is reduced; Determining target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and a first cost amount increased by each adjustment scheme; The engine is mass-produced according to the target boundary ranges of the plurality of target parameters.
2. The method according to claim 1, characterized in that Determining target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme includes: Combining all adjustment schemes corresponding to the multiple target parameters to obtain multiple scheme combinations; wherein the multiple schemes contained in each scheme combination correspond one-to-one to the multiple target parameters; Performing simulation calculations on engine performance emissions according to the boundary ranges of the multiple target parameters in each combination, and obtaining first simulation results corresponding to each combination; Screening out a target combination from the multiple combinations according to the multiple first simulation results corresponding to the multiple combinations and the second cost amount increased by each combination; The boundary ranges of the multiple target parameters in the target combination are used as the target boundary ranges of the multiple target parameters.
3. The method according to claim 1 or 2, characterized in that: A plurality of target parameters are screened out from the plurality of influencing parameters, including: Combining the multiple influencing parameters under different setting values to obtain multiple samples; Performing simulation calculations on engine performance emissions according to the multiple samples to obtain multiple performance emission results corresponding to the multiple samples; Performing statistical analysis on the multiple performance emission results to obtain ranking results of the multiple influencing parameters; the ranking results are ranked according to the proportion of influence of different influencing parameters on performance emissions; The influence parameter whose influence ratio in the sorting result is greater than the preset threshold is used as the target parameter.
4. The method according to claim 2, characterized in that: The first simulation result is a performance emission normal distribution diagram; according to the multiple first simulation results corresponding to the multiple combinations and the second cost amount increased by each combination, a target combination is screened out from the multiple combinations, including: Determine the corresponding standard deviation based on the performance emission normal distribution diagram corresponding to each combination; Constructing a correlation graph according to a plurality of standard deviations corresponding to a plurality of combinations and a second cost amount added to each combination; the correlation graph is used to represent the correlation distribution between the second cost amount and the standard deviation of the plurality of combinations; The combination in the correlation graph that meets the preset conditions is used as the target combination.
5. The method according to claim 4, characterized in that The preset conditions include a first condition and a second condition; Taking the combination in the correlation graph that meets the preset conditions as the target combination includes: The combination in the correlation graph that satisfies both the first condition and the second condition is taken as the target combination; wherein the first condition refers to that the standard deviation is greater than a preset standard deviation, and the second condition refers to that the second cost amount is the lowest.
6. The method according to claim 2, characterized in that Combining all adjustment schemes corresponding to the multiple target parameters to obtain multiple scheme combinations, including: For any one of the multiple target parameters, taking any one of the at least one adjustment scheme corresponding to the target parameter as a target scheme; The multiple target solutions corresponding to the multiple target parameters are combined to obtain a solution combination.
7. The method according to claim 1 or 2, characterized in that: Before determining a plurality of influencing parameters of engine performance emissions, the method further comprises: Establishing an initial model; the initial model is used to simulate the performance of engine performance emissions under changes in different influencing parameters; Adjusting at least one influencing parameter in the initial model, and using the initial model after the parameter adjustment to perform simulation calculations on engine performance emissions to obtain a second simulation result; If the second simulation result is the same as the actual test result, the initial model is used as the target model; the target model is used for simulation calculation of engine performance emission; If the second simulation result is different from the actual test result, the initial model is optimized.
8. An engine robustness optimization device, characterized in that: The device comprises: A parameter determination module, used to determine multiple influencing parameters of engine performance emissions, and a boundary range of each influencing parameter; A parameter screening module, used to screen out a plurality of target parameters from the plurality of influencing parameters; each target parameter refers to an influencing parameter whose influence on performance emission accounts for a proportion greater than a preset threshold among the plurality of influencing parameters; a scheme determination module, for determining, for any one of the plurality of target parameters, at least one adjustment scheme corresponding to the target parameter and a first cost amount increased by each adjustment scheme; any adjustment scheme is used to indicate adjusting a corresponding component of the engine so that a boundary range of the target parameter is reduced; A target boundary range determination module, configured to determine the target boundary ranges of the multiple target parameters according to all adjustment schemes corresponding to the multiple target parameters and the first cost amount increased by each adjustment scheme; A batch production module is used to carry out batch production of engines according to target boundary ranges of the multiple target parameters.
9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the engine robustness optimization method according to any one of claims 1 to 7 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the engine robustness optimization method according to any one of claims 1 to 7.