Vehicle index data processing method and related equipment
By establishing a mapping relationship between index scores and index values, the problems of inaccurate data screening and fragmented evaluation in traditional vehicle dynamics performance development are solved, enabling quantitative conversion and precise setting of vehicle performance targets, and improving the efficiency of design, development and manufacturing.
Patent Information
- Application Number
- CN202511786306.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-17
AI Technical Summary
In traditional vehicle dynamics performance development, the lack of systematic data screening and standardized classification leads to a lack of precision in indicator setting, a disconnect between scoring and indicators, strong subjectivity, and difficulty in achieving precise target setting for vehicle performance.
By obtaining the target indicator scores of vehicles, determining the indicator type, obtaining the statistical characteristic values of the measured values, establishing the mapping relationship between indicator scores and indicator values, realizing the quantitative conversion of target indicator values, and providing a unified indicator evaluation standard by combining data cleaning and cost weight decomposition.
It improves the efficiency of vehicle design, development, manufacturing, and maintenance processes, ensures the accuracy and consistency of vehicle performance targets, reduces subjective bias, and supports the systematic and precise development of vehicle performance.
Smart Images
Figure CN121683218A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automotive technology, and in particular to a method and related equipment for processing vehicle indicator data. Background Technology
[0002] In the development of vehicle dynamics performance, the traditional objective performance targets for core dimensions such as stability, handling, ride comfort, and braking have long relied on a "competitor benchmarking + experience-based judgment" model: defining the target range by referring to objective test data of vehicles in the same class and combining it with engineers' experience accumulated from past projects. This approach suffers from significant subjectivity; inexperienced engineers, in an effort to cover the performance levels of most competitors, tend to set target ranges that converge towards the industry average, easily falling into the "averaging trap." Simultaneously, the lack of a systematic screening, standardized cleaning, and structured classification mechanism for benchmarking data makes it impossible to effectively eliminate significantly different data, making it extremely difficult to guarantee data quality and directly impacting the accuracy of target setting.
[0003] In addition, in the existing performance development process, besides engineering simulation (such as ADAMS multibody simulation, CarSim dynamic simulation, etc.), there is also a method to establish a subjective and objective performance scoring system based on comprehensive scoring. In this method, "scoring" runs through the entire system, but there is a separation between scoring and indicators. The systematic mapping and correlation between indicator scores and indicators is one of the core issues that needs to be solved, namely, the target decomposition system from scores to indicators. Summary of the Invention
[0004] To address at least one of the aforementioned technical problems, the present invention aims to provide a method and related equipment for processing vehicle index data.
[0005] On one hand, embodiments of the present invention include a vehicle indicator data processing method, the vehicle indicator data processing method comprising the following steps: Obtain the target indicator score for the vehicle; Obtain measured values for multiple indicators; the measured values of the indicators correspond to the same indicator item as the target indicator score. Determine the indicator type corresponding to the indicator item; Based on the indicator type, obtain the statistical characteristic values of the measured values of each indicator; Based on the statistical characteristic values, determine the index score-index value mapping relationship corresponding to the index item; The target indicator value is determined based on the target indicator score and the mapping relationship between the indicator score and the indicator value.
[0006] Furthermore, the target indicator score includes: Obtain comprehensive performance ratings for competing vehicles; Based on the implementation cost of the aforementioned indicator items, the overall performance score is decomposed to obtain the target indicator score corresponding to each indicator item.
[0007] Further, determining the indicator type corresponding to the indicator item includes: Based on the inherent characteristics of the indicator items, the indicator type is determined; the indicator type is either a numerical type or an interval type, the numerical type is a type in which the quality of the indicator value is monotonically correlated with the size of the indicator value, and the interval type is a type in which the quality of the indicator value is correlated with the deviation of the indicator value from the mean.
[0008] Further, obtaining the statistical characteristic values of the measured values of each indicator according to the indicator type includes: When the indicator type is a numerical type, multiple quantiles of the measured values of each indicator are obtained as the statistical feature values. When the indicator type is an interval type, the mean and standard deviation of the measured values of each indicator are obtained as the statistical feature values.
[0009] Further, determining the index score-index value mapping relationship corresponding to the index item based on the statistical feature value includes: Based on the statistical characteristic values, the range of the measured values of each indicator is divided to obtain multiple numerical intervals; For the index values located at the boundaries of each of the aforementioned numerical intervals, assign corresponding index scores respectively; For the index values located within each of the aforementioned numerical ranges, corresponding index scores are assigned through interpolation. The mapping relationship between indicator score and indicator value is determined based on the value of each indicator and the corresponding indicator score.
[0010] Further, determining the target indicator value based on the target indicator score and the indicator score-indicator value mapping relationship includes: Using the target indicator score as the indicator score, the search is performed according to the indicator score-indicator value mapping relationship; The found index value is used as the target index value.
[0011] Furthermore, the indicator data processing method also includes: Before obtaining the statistical characteristic values of the measured values of each of the aforementioned indicators, the measured values of each of the aforementioned indicators are cleaned.
[0012] Furthermore, the indicator data processing method also includes: Vehicle development is based on the stated target metrics.
[0013] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute the vehicle index data processing method in the embodiments.
[0014] On the other hand, embodiments of the present invention also include a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the vehicle index data processing method in the embodiments.
[0015] The beneficial effects of this invention are as follows: The vehicle indicator data processing method in the embodiments can obtain the target indicator value corresponding to the target indicator score of the vehicle. Since the target indicator score represents the intuitive effect of the indicator item and can quantitatively represent the subjective user experience of the vehicle, while the target indicator value is the value of the indicator item in terms of engineering technology, the vehicle indicator data processing method in the embodiments can use the target indicator score obtained through vehicle competitor analysis, customer demand collection, etc., as the overall target and convert it into a clear technical parameter such as the target indicator value. This is conducive to clarifying the goals and directions of technical personnel in the process of vehicle design and development, production and manufacturing, and later maintenance, and is conducive to improving the efficiency of these processes, thereby obtaining a satisfactory vehicle product. Attached Figure Description
[0016] Figure 1 This is a schematic diagram illustrating the steps of the vehicle indicator data processing method in the embodiment; Figure 2 This is a schematic diagram illustrating the principle of step S401 in the embodiment; Figure 3 This is a schematic diagram illustrating the principle of step S402 in the embodiment; Figure 4 This is a schematic diagram illustrating the principle of performing steps S501-S504 on numerical index items in the embodiment. Figure 5 This is a schematic diagram illustrating the principle of performing steps S501-S504 on interval-type index items in the embodiment. Detailed Implementation
[0017] Terminology Explanation: Indicator items: Parameters that describe the performance, characteristics or state of a vehicle from specific aspects during the design, development, production, manufacturing and maintenance of the vehicle, such as body roll gradient (describes the degree of body roll of the vehicle) and linear understeer (describes the degree to which the turning radius increases with the increase of vehicle speed when the vehicle is turning in a steady state). Indicator values: The objective numerical values of indicator items in engineering technology, such as the specific values of body roll gradient and understeer in the linear zone. Actual measured values of indicators: Indicator values obtained through actual measurement, such as the specific values of body roll gradient and linear understeer in finished or concept vehicles that have been designed, manufactured, or maintained. Indicator scores: Indicator items are expressed in an intuitive and easy-to-understand form, such as a ten-point scale or a hundred-point scale. There is a corresponding relationship between the indicator score and the indicator value. Target score: The score that the vehicle is expected to achieve after the design, development, manufacturing, and maintenance processes are completed.
[0018] In this embodiment, as Figure 1 As shown, the vehicle indicator data processing method includes the following steps: S1. Obtain the target indicator score; S2. Obtain measured values for multiple indicators; S3. Determine the indicator type corresponding to the indicator item; S4. Based on the indicator type, obtain the statistical characteristic values of the measured values of each indicator; S5. Based on the statistical characteristic values, determine the mapping relationship between the indicator score and the indicator value corresponding to the indicator item; S6. Determine the target indicator value based on the target indicator score and the mapping relationship between the indicator score and the indicator value.
[0019] In this embodiment, a computer can be used to perform steps S1-S6.
[0020] Since a vehicle generally has multiple different indicator items such as indicator item 1 (e.g., body roll gradient) and indicator item 2 (e.g., understeer in the linear zone), each indicator item can be determined by executing steps S1-S6 to determine the corresponding target indicator value. In this embodiment, unless otherwise specified, one specific indicator item, such as indicator item 1, will be used as an example for explanation.
[0021] When performing step S1, you can choose to set the target score of indicator item 1 based on the expected results of vehicle design, development, manufacturing, and maintenance.
[0022] When performing step S1, you can also select the performance of competing vehicles (e.g., vehicles designed, developed, manufactured, and maintained by competitors of the automakers performing steps S1-S6) in terms of their performance on indicator 1 during the design, development, manufacturing, and maintenance stages. This performance is usually expressed as a performance score. For example, an automaker performing steps S1-S6 might obtain a performance score of 85 for indicator 1 for a certain competing vehicle. When performing step S1, you can choose the industry-standard conversion model of "project performance score → user perception score → target indicator score" to convert it into the target indicator score for indicator 1, or use the project performance score itself as the target indicator score.
[0023] When performing step S1, it may not be easy to obtain the performance of competing vehicles in specific performance metrics. Relatively speaking, it's easier to obtain the overall performance of competing vehicles through market research, user surveys, and sales feedback, which is usually expressed as a comprehensive performance score. For example, a car manufacturer performing steps S1-S6 might obtain a comprehensive performance score of 78 points for a certain competing vehicle. In this case, when performing step S1, which is to obtain the target score, the following steps can be performed: S101. Obtain a comprehensive performance rating of competing vehicles; S102. Based on the implementation cost of the indicator items, decompose the comprehensive performance score to obtain the target indicator score corresponding to the indicator items.
[0024] In step S101, the overall performance score of the vehicle competitors represents the overall performance score of the vehicle competitors, which can be regarded as the comprehensive result of the performance of the vehicle competitors on multiple indicator items such as indicator item 1, indicator item 2, ... indicator item n.
[0025] In step S102, the comprehensive performance score obtained in step S101 is... The score of indicator item 1 is considered as the indicator score. Indicator score for indicator item 2 ...Indicator score of indicator item n The weighted sum is obtained by weighting the values of indicator item 1. The weight of indicator item 1 can be determined by its implementation cost [specifically, it can be the cost incurred in design, development, manufacturing, and maintenance to improve (if the quality of indicator item 1 is positively correlated with its measured value) or decrease (if the quality of indicator item 1 is negatively correlated with its measured value) the unit measured value of indicator item 1]. It is negatively correlated with the implementation cost of indicator item 1; the higher the implementation cost of indicator item 1, the higher its weight. The smaller the value, the greater the weight of indicator item 2. Negatively correlated with the implementation cost of indicator item 2... Weight of indicator item n It is negatively correlated with the implementation cost of indicator item n, thus obtaining = + +……+ (1) In this embodiment, the goal is to minimize the overall cost of the vehicle, thus setting constraints.
[0026] in It is a negative correlation function, specifically a reciprocal function, i.e., the constraint condition is... (2)
[0027] Under the constraints of formulas (1) and (2), the index score can be determined. Indicator score for indicator item 2 ...Indicator score of indicator item n The size of the value, thereby enabling a comprehensive performance score. The decomposition yields the target indicator score corresponding to indicator item 1. .
[0028] In step S2, the car manufacturers who have completed steps S1-S6 can conduct actual measurements of indicator item 1 on multiple vehicles (specifically, hundreds or thousands of vehicles) that they have designed, developed, manufactured, maintained, and put into operation, thereby obtaining the actual measured values of multiple indicator items 1.
[0029] Specifically, the exact values of the body roll gradient can be measured for multiple vehicles to obtain the measured values of multiple indicator items 1.
[0030] After performing step S2, the measured values of multiple indicator items 1 can be converted into scatter plots or other formats to visualize the discrete distribution of each indicator's measured values. This visually presents the clustering characteristics and outliers of each indicator's measured values. Data cleaning is then performed on the measured values of each indicator to remove extreme interference data, obtaining high-quality cleaned data and ensuring the validity of the measured values. In subsequent steps S3-S6, the measured values of indicator items 1 refer to the multiple measured values of the indicators after data cleaning.
[0031] When performing step S3, the index type of index item 1 can be determined based on its inherent characteristics. In this embodiment, index item 1 is specifically the vehicle body roll gradient. The vehicle body roll gradient is a type of index whose numerical value is monotonically related to its performance. That is, the smaller the index value of a vehicle's vehicle body roll gradient, the better the vehicle's performance in this aspect; conversely, the larger the index value of the vehicle body roll gradient, the worse the vehicle's performance in this aspect. Based on the aforementioned inherent characteristics of the vehicle body roll gradient index item, i.e., index item 1, the index type of index item 1 is determined to be a numerical type.
[0032] In this embodiment, in addition to index item 1, index item 3 is also a numerical type, specifically the resonant frequency. Specifically, the resonant frequency is a type of index whose numerical value is monotonically related to its quality; that is, the larger the resonant frequency index value of a vehicle, the better its performance in that aspect, and conversely, the smaller the resonant frequency index value, the worse its performance in that aspect. Based on the inherent characteristics of the resonant frequency index item, i.e., index item 3, the index type of index item 3 is determined to be numerical.
[0033] For other indicator items, they may be classified as other indicator types. For example, in this embodiment, indicator item 2 is specifically linear understeer. Linear understeer is a type of indicator whose numerical quality is related to the deviation of the numerical value from the mean. That is, the smaller the absolute value of the linear understeer indicator value of a vehicle compared with the mean of the linear understeer indicator values of multiple vehicles of the same type (e.g., with the same or similar data in terms of model, wheelbase, price, etc.), the better the performance of this vehicle in terms of linear understeer. Conversely, the larger the deviation of the linear understeer indicator value relative to the mean, the worse the performance of this vehicle in terms of linear understeer. Based on the above-mentioned inherent characteristics of linear understeer, i.e., indicator item 2, indicator item 2 is determined to be an interval type.
[0034] In this embodiment, when performing step S4, which is to obtain the statistical characteristic values of the measured values of each indicator according to the indicator type, the following steps can be performed: S401. When the indicator type is numerical, obtain multiple quantiles of the measured values of each indicator as statistical feature values; S402. When the indicator type is an interval type, obtain the mean and standard deviation of the measured values of each indicator as statistical feature values.
[0035] In this embodiment, since the index type of index item 1 (vehicle roll gradient) is numerical, for the multiple measured values of index item 1 obtained by executing step S2, step S401 is selected to be executed.
[0036] The principle of step S401 is as follows: Figure 2 As shown. (Refer to...) Figure 2 The measured values of multiple indicators for indicator item 1 were statistically analyzed to obtain multiple quantiles, such as the maximum value, third-quarter quantile, half-quarter quantile, quarter-quarter quantile, and minimum value, which were used as the statistical characteristic values of indicator item 1.
[0037] In this embodiment, since the index type of index item 2 (linear region insufficient turning degree) is an interval type, for the multiple measured values of index item 2 obtained by executing step S2, step S402 is selected to be executed.
[0038] The principle of step S402 is as follows: Figure 3 As shown. (Refer to...) Figure 3 The measured values of multiple indicators of indicator item 2 are assumed to follow a normal distribution. The measured values of multiple indicators of indicator item 2 are statistically analyzed to obtain their mean and standard deviation, which are used as the statistical characteristic values of indicator item 2.
[0039] In this embodiment, when performing step S5, which is to determine the mapping relationship between the indicator score and the indicator value corresponding to the indicator item based on the statistical feature value, the following steps can be performed: S501. Based on statistical characteristic values, the range of measured values of each indicator is divided to obtain multiple numerical intervals; S502. Assign corresponding index scores to the index values located at the boundaries of each numerical range; S503. For the index values located within each numerical range, assign the corresponding index scores through interpolation. S504. Determine the mapping relationship between indicator score and indicator value based on the value of each indicator and the corresponding indicator score.
[0040] For numerical indicators such as indicator item 1, the principle of executing steps S501-S504 is as follows: Figure 4 As shown.
[0041] Reference Figure 4 In step S501, the range of the measured values of each indicator of indicator item 1 is divided into multiple numerical intervals such as [minimum value, quarter position], [quarter position, three-quarter position] and [three-quarter position, maximum value].
[0042] Reference Figure 4In step S502, the boundaries of each numerical interval, namely the minimum value, quarter point, third point, and maximum value, are assigned index scores of 100, 80, 60, and 0 respectively.
[0043] In step S503, taking the numerical interval [quarter point, three-quarter point] as an example, since the index scores corresponding to the two boundaries of the known numerical interval are 80 points and 60 points respectively, the index scores corresponding to the measured values of each index within the numerical interval can be determined by linear interpolation.
[0044] In step S504, all numerical intervals are processed in the same way as in step S503. Therefore, both the boundary and the measured value of the index within the numerical interval are assigned index scores, thereby establishing a mapping relationship between index scores and index values.
[0045] For indicator items of interval type, such as indicator item 2, the principle of executing steps S501-S504 is as follows: Figure 5 As shown.
[0046] Reference Figure 5 In step S501, the range of measured values of each indicator in indicator item 2 is divided into multiple numerical intervals such as [mean - 2.5 standard deviation, mean - 1.5 standard deviation], [mean - 1.5 standard deviation, mean - 0.5 standard deviation], [mean - 0.5 standard deviation, mean + 0.5 standard deviation], [mean + 0.5 standard deviation, mean + 1.5 standard deviation], and [mean + 1.5 standard deviation, mean + 2.5 standard deviation].
[0047] Reference Figure 5 In step S502, for the boundaries of each numerical interval, namely the mean -2.5 standard deviation, mean -1.5 standard deviation, mean -0.5 standard deviation, mean +0.5 standard deviation, mean +1.5 standard deviation and mean +2.5 standard deviation, respectively, 60, 80, 100, 80 and 60 index scores are assigned. The symmetrical boundaries at both ends of the mean are assigned the same index score.
[0048] In step S503, taking the numerical range of [mean - 1.5 standard deviation, mean - 0.5 standard deviation] as an example, since the index scores corresponding to the two boundaries of the known numerical range are 80 and 100 respectively, the index scores corresponding to the measured values of each index within the numerical range can be determined by linear interpolation.
[0049] In step S504, all numerical intervals are processed in the same way as in step S503. Therefore, both the boundary and the measured value of the index within the numerical interval are assigned index scores, thereby establishing a mapping relationship between index scores and index values.
[0050] In step S6, the target indicator score obtained in step S1 is used as the exponential score in the indicator score-indicator value mapping relationship, which can be mapped to the corresponding indicator value, thereby obtaining the target indicator value.
[0051] In this embodiment, by executing steps S1-S6, the target indicator value corresponding to the vehicle's target indicator score can be obtained. Since the target indicator score represents the intuitive effect of the indicator item and can quantitatively represent the subjective user experience of the vehicle, while the target indicator value is the value that the indicator item possesses in terms of engineering technology, executing steps S1-S6 can use the target indicator score obtained through vehicle competitor analysis, customer demand collection, etc., as the overall target and transform it into a clear technical parameter such as the target indicator value. This is beneficial to clarifying the goals and directions of technical personnel in the process of vehicle design and development, production and manufacturing, and later maintenance, and is conducive to improving the efficiency of these processes, thereby obtaining a satisfactory vehicle product.
[0052] The vehicle indicator data processing method in this embodiment addresses the issues of subjectivity and broad scope in setting engineering goals during existing vehicle dynamics performance development processes, as well as the disconnect between indicator scores and indicator data. It proposes a systematic solution. Specifically, by uniformly processing and filtering indicator data in a dynamically updated objective vehicle dynamics indicator database, it provides high-quality objective indicator data for subsequent engineering goal setting and the establishment of a mapping relationship between indicator scores and indicator values. Furthermore, it provides a method for mapping the relationship between indicator scores and indicator values, resolving the disconnect between scores and indicators—a core issue in the process of linking subjective scoring with objective indicators. This supports the implementation of a comprehensive vehicle dynamics scoring system and also utilizes the comprehensive scoring system for goal setting and decomposition. The engineering goals of objective indicators guide the subsequent development direction of overall vehicle performance and the optimization direction of component performance. This method reduces the subjectivity of traditional methods, providing more precise engineering goals than before, and mitigating the risk of deviation from the performance development direction in the early stages of project development. It uses easily understood scores to evaluate the merits of indicators, providing a unified evaluation standard for different indicators, thus supporting improved efficiency and goal achievement rates in vehicle dynamics performance development.
[0053] Specific implementation examples of setting / changing objective indicators: Taking a certain vehicle development as an example, based on the test data and simulation data of the benchmark vehicle, we find the different ranges of the corresponding vehicle class indicators, clearly define the indicator range of the benchmark vehicle and simulation data, and determine the target setting range of the indicator by combining the requirements of subjective performance being superior to the competitors and the vehicle positioning.
[0054] Specific implementation examples of objective indicator target decomposition: Taking a newly developed vehicle as an example, the vehicle is required to have a higher overall performance level and better stability than competing products. The comprehensive performance score of competing vehicles is 78 points. Based on the existing correlation method between comprehensive score, user perception score and indicator score in the industry, and the vehicle dynamics comprehensive evaluation system, the indicator score is calculated. According to the mapping relationship between indicator score and indicator range in this patent, the indicator score range is obtained.
[0055] A computer program that executes the vehicle index data processing method in this embodiment can be written into a computer device or storage medium. When the computer program is read out and run, the vehicle index data processing method and / or vehicle index data processing method in this embodiment will be executed, thereby achieving the same technical effect as the vehicle index data processing method and / or vehicle index data processing method in the embodiment.
[0056] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a," "an," and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing particular embodiments and is not intended to limit the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.
[0057] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of the invention.
[0058] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).
[0059] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or otherwise obviously contradict the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes a plurality of instructions executable by one or more processors.
[0060] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques of the invention, the invention also includes the computer itself.
[0061] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on the display.
[0062] The above are merely preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.
Claims
1. A vehicle index data processing method characterized by, The vehicle index data processing method comprises: obtaining a target index score of a vehicle; obtaining a plurality of index measured values; the index measured values correspond to the same index item as the target index score; determining an index type corresponding to the index item; obtaining a statistical characteristic value of each index measured value according to the index type; determining an index score-index value mapping relationship corresponding to the index item according to the statistical characteristic value; determining a target index value according to the target index score and the index score-index value mapping relationship.
2. The indicator data processing method of claim 1, wherein, The target index score comprises: obtaining a performance comprehensive score of a vehicle competitor; decomposing the performance comprehensive score according to an implementation cost of the index item to obtain the target index score corresponding to the index item.
3. The indicator data processing method of claim 1, wherein, The determination of the index type corresponding to the index item comprises: determining the index type according to inherent characteristics of the index item; the index type is a numerical type or an interval type; the numerical type is a type in which index value superiority and index value size are monotonically related; the interval type is a type in which index value superiority and deviation of index value size from a mean value are related.
4. The indicator data processing method of claim 3, wherein, The obtaining of the statistical characteristic value of each index measured value according to the index type comprises: when the index type is the numerical type, obtaining a plurality of quantile numbers of each index measured value as the statistical characteristic value; when the index type is the interval type, obtaining a mean value and a standard deviation of each index measured value as the statistical characteristic value.
5. The indicator data processing method of claim 4, wherein, The determination of the index score-index value mapping relationship corresponding to the index item according to the statistical characteristic value comprises: dividing a range in which each index measured value is located according to the statistical characteristic value to obtain a plurality of numerical intervals; respectively assigning an index score to an index value located at a boundary of each numerical interval; respectively assigning an index score to an index value located in an interior of each numerical interval through interpolation assignment; determining the index score-index value mapping relationship according to each index value and the corresponding index score.
6. The indicator data processing method of claim 5, wherein, The determination of the target index value according to the target index score and the index score-index value mapping relationship comprises: looking up according to the index score-index value mapping relationship with the target index score as an index score; taking the looked-up index value as the target index value.
7. The indicator data processing method of claim 1, wherein, The index data processing method further comprises: performing data cleaning on each index measured value before obtaining the statistical characteristic value of each index measured value.
8. The indicator data processing method according to any one of claims 1 to 7, characterized in that, The index data processing method further comprises: performing vehicle development according to the target index value.
9. A computer apparatus, comprising: The vehicle index data processing method comprises a memory and a processor; the memory is used to store at least one program; the processor is used to load the at least one program to execute the vehicle index data processing method according to any one of claims 1-8.
10. A computer readable storage medium having stored therein a program that is executable by a processor, characterized in that, The program executable by the processor is used to execute the vehicle index data processing method according to any one of claims 1-8 when executed by the processor.