A passenger car overall size design method based on statistical laws

Abstract

The present invention belongs to the field of automobile technology, specifically, it is a passenger car vehicle size design method based on statistical laws. It includes: step one, establishing an industry information database; step two, establishing a rough model; step three, establishing a concentration model; step four, establishing a discrete coefficient model; step five, establishing a single-level market mainstream model and a key benchmark model model, and comparing size / weight details; step six, establishing a single brand, a single model time series model, and conducting a brand development correlation analysis; step seven, establishing a normal distribution curve model; step eight, outputting size and weight parameter results. Based on big data and statistical laws, the present invention derives size models and sets size targets, analyzes the correlation between size and weight, and performs size decomposition calculations in combination with structural feasibility. The final defined size is the result of a comprehensive balance with weight and performance, which not only meets the current needs of single market models, but also meets market trends.

Description

Technical Field

[0001] The invention belongs to the technical field of automobiles, and in particular is a method for designing the dimensions of a passenger vehicle based on statistical laws. Background Art

[0002] The larger the vehicle is, the larger its space is, but the weight will inevitably increase. The three are interrelated and indirectly lead to reduced vehicle power, increased fuel consumption, and more difficult to ensure safety requirements. The correlation between size, weight, and performance requires us to first solve the problem of size setting in order to ensure that weight and performance are controllable. Summary of the invention

[0003] The present invention provides a passenger car whole vehicle size design method based on statistical laws. The present invention is based on big data and statistical laws to derive a size model and set a size target, analyze the correlation between size and weight, and perform size decomposition calculations in combination with structural feasibility. The final defined size is the result of a comprehensive balance between weight and performance, which not only meets the current needs of a single market model, but also conforms to market trends.

[0004] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0005] A passenger car size design method based on statistical laws comprises the following steps:

[0006] Step 1: Establish an industry information database;

[0007] Step 2: Establish a rough model;

[0008] Step 3: Establish a concentration model;

[0009] Step 4: Establish a discrete coefficient model;

[0010] Step 5: Establish models of mainstream models and key benchmark models in a single-level market, and compare size / weight details;

[0011] Step 6: Establish a single brand and single model time series model to conduct brand development correlation analysis;

[0012] Step 7: Establish a normal distribution curve model;

[0013] Step 8. Output the size and weight parameter results.

[0014] The information database described in step 1 includes information on the manufacturer, brand, model, vehicle level, year, energy type, body structure, price, engine displacement, external dimensions, tires, and weight of A0-C class vehicles.

[0015] The specific method of step 2 is as follows:

[0016] Preliminary data screening is carried out to filter out data on models of a certain segment level, establish a scatter plot model of size ratio distribution range, volume distribution range, and weight distribution range, abstract the size / weight ratio of each level of models in the corresponding level market, and confirm the overall market status.

[0017] The specific method of step three is as follows:

[0018] Improve the data logic of the preliminary screening in step 2, conduct mean and extreme value analysis, confirm the vehicle size and weight concentration range, and the degree of deviation from the mean;

[0019] And determine the dimensions with concentrated distribution in characteristics, and the corresponding dimensions of the corresponding level of vehicles are selected according to the concentration principle;

[0020] Specifically:

[0021] 31) The preliminary screening data is screened again, and curve fitting is performed to retain the size point set with adjacent size deviations within 2.5%. After two iterations of the operation, a new data point set area is formed;

[0022] 32) The new data point set is processed in layers. The new data point set area will form different concentrated areas for hatchbacks and sedans. Curve fitting is performed separately, and an elliptical area is formed radiating outward with the mean and the fitting curve as the center.

[0023] The specific method of step 4 is as follows:

[0024] Using statistical principles, we analyze the standard deviation and dispersion coefficient of size and weight, analyze the distribution range of dispersion coefficient, and confirm whether there is dispersion in the size and weight of a single model in a single market level; determine the size of the discrete distribution on the characteristics, and the corresponding size selection of the corresponding model of the corresponding level follows the discrete principle;

[0025] Standard Deviation

[0026] Mean

[0027]

[0028] The specific method of step five is as follows:

[0029] Determine whether the dimensions of the mainstream models and key benchmark models in step four are in the concentrated area, and perform feature analysis on models that are not in the concentrated area, that is, identify whether the mainstream benchmark models are in the concentrated area determined in step three, and conduct internal space dimension chain and weight comparison on benchmark models that are not in the concentrated area. Different dimensions have different analysis dimensions.

[0030] The specific method of step six is ​​as follows:

[0031] For the size that can draw the concentrated area model, confirm the size variable △ over time, solve the overall average variable, and preliminarily determine the size target of the vehicle model to be developed.

[0032] The specific method of step seven is as follows:

[0033] Evaluate the reasonable range of the size and weight targets of the vehicle to be developed, determine the development strategy of the vehicle to be developed, and adjust and compare parameters; the size target of the vehicle to be developed is a range, and the concentrated area model is cut by the horizontal and vertical coordinate lines passing through the mean point and the fitting curve to form six quadrants, and the size target range is determined according to the quadrant where the benchmark vehicle is located; the development strategy of the vehicle to be developed is: determine 2-3 numerical points on the normal model curve, respectively, by the mean + the standard deviation of the corresponding multiple, and modify the size target in step six according to this value to obtain the final data.

[0034] The beneficial effects of the present invention are:

[0035] Taking into account the current market situation and future trends, the layout space requirements and structural feasibility, based on big data and based on statistical laws, a more reasonable and feasible vehicle size target and a vehicle weight target with a better lightweight coefficient are determined in the early stages of development, thereby better improving the competitiveness of automotive products. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present invention and these drawings without creative work.

[0037] Figure 1 It is a schematic diagram of the concentration model;

[0038] Figure 2 It is a schematic diagram of the discrete coefficient model;

[0039] Figure 3 It is a schematic diagram of the normal curve model;

[0040] Figure 4 Flow chart of the present invention;

[0041] Figure 5 This is a schematic diagram of the elliptical area radiating outward from the mean and the fitting curve in step 3;

[0042] Figure 6It is a schematic diagram of the single size variables of each single model in each year;

[0043] Figure 7 A schematic diagram of a new contour obtained by performing a corresponding offset on the concentrated area in step 6;

[0044] Figure 8 The six-quadrant schematic diagram is formed after the concentrated area model in step six is ​​cut by the horizontal and vertical coordinate lines passing through the mean point and the fitting curve. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 4 A passenger car size design method based on statistical laws comprises the following steps:

[0047] Step 1: Establish an industry information database;

[0048] The information database includes information on the manufacturer, brand, model, vehicle level, year, energy type, body structure, price, engine displacement, external dimensions, tires, and weight of A0-C class vehicles.

[0049] Step 2: Establish a rough model;

[0050] Preliminary data screening: filter out data on a specific segmented level of vehicle, such as AO-level sedans; establish a scatter plot model of size ratio distribution range, volume distribution range, and weight distribution range, abstract the size / weight ratio of each level of vehicle in the corresponding level market, and confirm the overall market status.

[0051] Step 3: Establish a concentration model;

[0052] Improve the data logic of the preliminary screening in step 2, conduct mean and extreme value analysis, confirm the vehicle size and weight concentration range, and the degree of deviation from the mean;

[0053] Among them, the average, max, and min functions in EXCEL are used to calculate the mean and extreme value.

[0054] And determine the dimensions with concentrated distribution in characteristics, and the corresponding dimensions of the corresponding level of vehicles are selected according to the concentration principle;

[0055] Specifically:

[0056] 31) The preliminary screening data is screened again, and curve fitting is performed to retain the size point set with adjacent size deviations within 2.5%. After two iterations of the operation, a new data point set area is formed;

[0057] 32) Perform stratification on the new data point set, see Figure 5 ,Take AO-class sedan as an example, the new data point set area will form different ,concentration areas for hatchback and sedan cars, and curve fitting will be performed separately, and ,radiate outwards with the mean and the fitting curve as the center to form an ,elliptical area.

[0058] Step 4: Establish a discrete coefficient model;

[0059] Using statistical principles, analyze the standard deviation and dispersion coefficient of size and weight, analyze the distribution range of dispersion coefficient, and confirm whether the size and weight of a single model in a single market level are discrete, that is, after data iteration, a concentrated area cannot be formed or the data point density is ≤20 / ellipse; determine the size of discrete distribution on the feature, and the corresponding size selection of the corresponding model of the corresponding level follows the discrete principle;

[0060] Standard Deviation

[0061] Mean

[0062]

[0063] Step 5: Establish models of mainstream models and key benchmark models in a single-level market, and compare size / weight details;

[0064] Determine whether the dimensions of the mainstream models and key benchmark models in step four are in the concentrated area, and conduct feature analysis on models that are not in the concentrated area, that is, identify whether the mainstream benchmark models are in the concentrated area determined in step three, and conduct internal space dimension chain and weight comparison on the benchmark models that are not in the concentrated area. Different dimensions have different analysis dimensions, and the purpose is to clarify their special features and whether it is necessary to follow.

[0065] Step 6: Establish a single brand and single model time series model to conduct brand development correlation analysis;

[0066] For the size that can draw the concentrated area model, confirm the size variable △ over time, solve the overall average variable, and preliminarily determine the size target of the vehicle model to be developed.

[0067] Among them, the single size variable △ of each single model in each year is as follows Figure 6 The above variables are averaged by year, and the mean curve is made by year to obtain the overall average variable, so as to conduct trend analysis. The concentrated area is offset accordingly based on the above solution (original contour + overall average variable) to obtain a new contour, as shown in the contour diagram Figure 7 shown.

[0068] The size target of the model to be developed is a range. The concentrated area model is cut by the horizontal and vertical coordinate lines passing through the mean point and the fitting curve to form six quadrants. The size target range is determined according to the quadrant where the benchmark model is located, such as Figure 8 shown.

[0069] Step 7: Establish a normal distribution curve model;

[0070] Evaluate the reasonable range of the size and weight targets of the vehicle to be developed, determine the development strategy of the vehicle to be developed, and adjust and compare parameters; the size target of the vehicle to be developed is a range, and the concentrated area model is cut by the horizontal and vertical coordinate lines passing through the mean point and the fitting curve to form six quadrants, and the size target range is determined according to the quadrant where the benchmark vehicle is located; the development strategy of the vehicle to be developed is: determine 2-3 numerical points on the normal model curve, respectively, by the mean + the standard deviation of the corresponding multiple, and modify the size target in step six according to this value to obtain the final data.

[0071] Step 8. Output the size and weight parameter results.

[0072] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the protection scope of the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention. These simple variations all belong to the protection scope of the present invention.

[0073] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0074] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A passenger car size design method based on statistical laws, characterized in that: The following steps are involved: Step 1: Establish an industry information database; Step 2: Establish a rough model; Step 3: Establish a concentration model. The specific method is as follows: Improve the data logic of the preliminary screening in step 2, conduct mean and extreme value analysis, confirm the vehicle size and weight concentration range, and the degree of deviation from the mean; And determine the dimensions with concentrated distribution in characteristics, and the corresponding dimensions of the corresponding level of vehicles are selected according to the concentration principle; Specifically: 31) The preliminary screening data is screened again, and curve fitting is performed to retain the size point set with adjacent size deviations within 2.5%. After two iterations of the operation, a new data point set area is formed; 32) The new data point set is processed in layers. The new data point set area will form different concentrated areas for hatchbacks and sedans. Curve fitting is performed on each of them, and an elliptical area is formed radiating outward with the mean value and the fitting curve as the center; Step 4: Establish a discrete coefficient model; Step 5: Establish models of mainstream models and key benchmark models in a single-level market, and compare size / weight details; Step 6: Establish a single brand and single model time series model to conduct brand development correlation analysis; Step 7: Establish a normal distribution curve model. The specific method is as follows: Evaluate the reasonable range of size and weight targets of the models to be developed, determine the development strategy of the models to be developed, and make parameter adjustments and comparisons; The target size of the model to be developed is a range. The concentrated area model is cut by the horizontal and vertical coordinate lines passing through the mean point and the fitting curve to form six quadrants. The size target range is determined according to the quadrant where the benchmark model is located. The development strategy of the model to be developed is: determine 2-3 numerical points on the normal model curve, respectively, by the mean + the standard deviation of the corresponding multiple, and modify the size target in step six according to this value to obtain the final data; Step 8. Output the size and weight parameter results.

2. The method for designing the dimensions of a passenger vehicle based on statistical rules according to claim 1, characterized in that: The information database described in step 1 includes information on the manufacturer, brand, model, vehicle level, year, energy type, body structure, price, engine displacement, external dimensions, tires, and weight of A0-C class vehicles.

3. The method for designing the dimensions of a passenger vehicle based on statistical rules according to claim 1, characterized in that: The specific method of step 2 is as follows: Preliminary data screening is carried out to filter out data on models of a certain segment level, establish a scatter plot model of size ratio distribution range, volume distribution range, and weight distribution range, abstract the size / weight ratio of each level of models in the corresponding level market, and confirm the overall market status.

4. The method for designing the dimensions of a passenger vehicle based on statistical rules according to claim 1, characterized in that: The specific method of step 4 is as follows: Using statistical principles, we analyze the standard deviation and dispersion coefficient of size and weight, analyze the distribution range of dispersion coefficient, and confirm whether there is dispersion in the size and weight of a single model in a single market level; determine the size of the discrete distribution on the characteristics, and the corresponding size selection of the corresponding model of the corresponding level follows the discrete principle; 5. The method for designing the dimensions of a passenger vehicle based on statistical rules according to claim 1, characterized in that: The specific method of step five is as follows: Determine whether the dimensions of the mainstream models and key benchmark models in step four are in the concentrated area, and perform feature analysis on models that are not in the concentrated area, that is, identify whether the mainstream benchmark models are in the concentrated area determined in step three, and conduct internal space dimension chain and weight comparison on benchmark models that are not in the concentrated area. Different dimensions have different analysis dimensions.

6. The method for designing the dimensions of a passenger vehicle based on statistical rules according to claim 1, characterized in that: The specific method of step six is ​​as follows: For the size that can draw the concentrated area model, confirm the size variable △ over time, solve the overall average variable, and preliminarily determine the size target of the vehicle model to be developed.

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