Second-hand car performance detection method and system based on multi-source data analysis

Abstract

The invention discloses a second-hand vehicle performance detection method and system based on multi-source data analysis, and relates to the technical field of vehicle performance detection, and the main scheme is as follows: obtaining the basic information data of a second-hand vehicle, and obtaining the comprehensive information data of the second-hand vehicle from different detection channels according to the basic information data of the second-hand vehicle; analyzing and processing the comprehensive information data of the second-hand vehicle, and at least generating an automobile interior and exterior decoration comprehensive performance coefficient, a dynamic stability coefficient, a vehicle market price coefficient, an engine performance parameter, a brake system performance parameter and a core part condition coefficient; generating a second-hand vehicle performance detection report according to the basic information data of the second-hand vehicle and the analyzed data; the second-hand car performance detection report is translated into the selected target language through the integrated translation tool. According to the scheme, the second-hand car is comprehensively evaluated, various kinds of information are accurately presented, the transaction transparency is improved, multi-language translation is supported, and the internationalization level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle performance testing, and in particular to a second-hand car performance testing method and system based on multi-source data analysis. Background Art

[0002] With the continuous development of big data technology, the used car market has also ushered in a new data-driven era; traditional used car evaluations usually only rely on basic vehicle information and limited maintenance records, and lack the ability to obtain comprehensive vehicle information from multiple channels; this leads to the evaluation results being affected by incomplete information or data bias, reducing the accuracy and reliability of the evaluation; the comprehensive information of used vehicles is huge and complex, and direct manual analysis is not only time-consuming and labor-intensive, but also prone to errors; traditional evaluation methods lack unified and scientific evaluation standards, making it difficult to generate comprehensive and objective used car performance inspection reports; with the acceleration of the process of globalization, used car transactions have crossed national borders, and language barriers between different countries and regions have become a major problem in used car transactions. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the deficiencies in the prior art, the present invention provides a second-hand car performance testing method and system based on multi-source data analysis, which obtains basic information and comprehensive information of second-hand vehicles from multiple testing channels, solves the problem of single information source, and ensures the comprehensiveness and accuracy of evaluation data; analyzes the comprehensive information of second-hand vehicles to generate comprehensive performance coefficients of automobile interior and exterior decoration, dynamic stability coefficients, vehicle market price coefficients and core component condition coefficients, realizes rapid and accurate analysis of information, and improves evaluation efficiency and quality; generates a second-hand car performance test report, which covers appearance, interior, dynamic stability, market price and core components, provides comprehensive and objective evaluation results, and solves the problem of inconsistent evaluation standards; integrates translation tools to support translation of second-hand car performance test reports into a selected target language, breaks down language barriers, promotes the internationalization of second-hand car transactions, and improves the convenience and efficiency of transactions.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A second-hand car performance detection method based on multi-source data analysis, comprising:

[0007] Obtaining basic information data of used vehicles, and obtaining comprehensive information data of used vehicles from different testing channels based on the basic information data of used vehicles;

[0008] Analyze and process the comprehensive information data of used vehicles to generate at least the comprehensive performance coefficient of the interior and exterior decoration of the vehicle , dynamic stability coefficient , Vehicle market price coefficient , Engine performance parameters , Braking system performance parameters and core component condition factor ;

[0009] Generate a used car performance test report based on the basic information data of the used car and the data obtained from the above analysis;

[0010] Translate used car performance inspection reports into selected target languages ​​via integrated translation tools.

[0011] Preferably, in the above-mentioned used car performance detection method based on multi-source data analysis, the detection channels include: vehicle management systems, such as DMV or traffic police network, third-party data platforms, such as Guazi Used Cars, Yiche Used Cars and Renrenche Used Cars, 4S stores or authorized repair centers, insurance companies, vehicle inspection stations, information provided by car owners and manual inspections.

[0012] Preferably, in the above-mentioned second-hand vehicle performance detection method based on multi-source data analysis, the proportion coordination index is obtained by analyzing and processing the comprehensive information data of the second-hand vehicle: , paint surface smoothness , Interior functionality score , Interior integrity score , dynamic stability coefficient , Market Supply and Demand Index , Vehicle value retention rate , Vehicle value retention coefficient , Engine performance parameters , Braking system performance parameters ;

[0013] According to the proportional coordination index , paint surface smoothness , Interior functionality score and interior integrity score Generate comprehensive performance coefficient of automotive interior and exterior ; According to the market supply and demand index , Vehicle value retention rate , Vehicle value retention coefficient Generate vehicle market price coefficient ; According to engine performance parameters and brake system performance parameters Generate core component condition coefficients .

[0014] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the comprehensive performance coefficient of the interior and exterior decoration of the car is generated. The method is as follows:

[0015] The comprehensive information data of used vehicles includes at least the length of the vehicle , Vehicle width , Vehicle height and wheelbase , according to the vehicle length , Vehicle width , Vehicle height and wheelbase Calculate the proportional harmony index , the formula is as follows:

[0016]

[0017] in, for The weight coefficient is between 0.2 and 0.5; for The weight coefficient is between 0.1 and 0.4; for The weight coefficient is between 0.2 and 0.5; and ;

[0018] The comprehensive information data of used vehicles also includes the paint thickness at the measurement point and average paint thickness , according to the paint thickness at the measuring point and average paint thickness Calculate paint surface flatness , the formula is as follows:

[0019]

[0020] in, For different measurement points, The values ​​are 1, 2, 3... , is the total number of measurement points; It is The paint thickness at each measuring point;

[0021] The comprehensive information data of used vehicles also includes the number of normal functional parts and total number of functional components , according to the number of normal functional components and total number of functional components Calculate functional completeness , the formula is as follows:

[0022]

[0023] The comprehensive information data of used vehicles also includes the number of missing interior accessories And the total number of interior accessories , according to the number of missing interior accessories And the total number of interior accessories Calculate interior completeness , the formula is as follows:

[0024]

[0025] According to the proportional coordination index , paint surface smoothness , Interior functionality score and interior integrity score Calculate the comprehensive performance coefficient of automobile interior and exterior , based on the following formula:

[0026]

[0027] in, Proportional coordination index The weight coefficient is between 0.1 and 0.3; For paint surface flatness The weight coefficient is between 0.1 and 0.3; Rate feature completeness The weight coefficient is between 0.2 and 0.4; Rate interior completeness The weight coefficient is between 0.2 and 0.4; and

[0028] .

[0029] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the functional components include: a steering wheel, an instrument panel, an accelerator pedal, a brake pedal, a clutch pedal, a transmission lever, a seat and a seat belt.

[0030] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the dynamic stability coefficient is generated The method is as follows:

[0031] Comprehensive information data for used vehicles including maximum lateral acceleration , Gravitational acceleration , moment of inertia , angular acceleration , the total mass of the vehicle and the vehicle's center of gravity , calculate the dynamic stability coefficient based on the above data , the formula is as follows:

[0032]

[0033] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the vehicle market price coefficient is generated The method is as follows:

[0034] Second-hand vehicle comprehensive information data also includes model order volume , Model Production Volume , market price and the average market price , according to the order quantity , Production , market price and the average market price Calculate market supply and demand index , the formula is as follows:

[0035]

[0036] Used vehicle comprehensive information data also includes the original purchase price , according to the current market price of this model and original purchase price Calculate vehicle value retention rate , the formula is as follows:

[0037]

[0038] The comprehensive information data of used vehicles also includes the number of repairs , Vehicle age and mileage , according to the market supply and demand index , Original purchase price , Vehicle value retention rate , Vehicle maintenance times , Vehicle age and mileage Calculate the vehicle value retention coefficient , the formula is as follows:

[0039]

[0040] in, Market supply and demand index The weight coefficient is between 0.2 and 0.4; Maintenance times The weight coefficient is between 0.1 and 0.3; Number of vehicle repairs The weight coefficient is between 0.1 and 0.3; For mileage The weight coefficient is between 0.1 and 0.3;

[0041] According to the market supply and demand index , Vehicle value retention rate and vehicle value retention index Calculate vehicle market price coefficient , based on the following formula:

[0042]

[0043] in, Market supply and demand index The weight coefficient is between 0.3 and 0.5; Vehicle value retention rate The weight coefficient is between 0.2 and 0.4; The influence factor of vehicle value preservation The weight coefficient is between 0.2 and 0.4; and .

[0044] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the engine performance parameters are generated and calculated. The method is as follows:

[0045] The comprehensive information data of used vehicles also includes engine effective power ,time Total fuel consumed , Engine service life , Engine maintenance times and engine effective torque ;

[0046] According to the engine effective power ,time Total fuel consumed Calculate effective fuel consumption , the formula is as follows:

[0047]

[0048] According to the effective fuel consumption , Engine service life , Number of engine maintenance , Engine effective power and engine effective torque Calculate engine performance parameters , the method based on is as follows:

[0049]

[0050] in, for The weight coefficient is between 0.1 and 1.

[0051] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the brake system performance parameters are generated The method is as follows:

[0052] The comprehensive information data of used vehicles also includes the remaining thickness of the brake pads , Braking response time and brake disc wear thickness , cold braking distance ;Hot braking distance , bottom line distance , reference distance ; Based on cold braking distance ;Hot braking distance , bottom line distance , reference distance Calculate the Braking Performance Index , the formula is as follows:

[0053]

[0054] in, for The weight coefficient is between 0.6 and 0.7; for The weight coefficient is 0.3~0.4; and ;

[0055] According to the remaining thickness of the brake pad , Braking response time , Brake disc wear thickness and Braking Performance Index Calculate brake system performance parameters , the formula is as follows:

[0056]

[0057] Preferably, in the above-mentioned second-hand car performance detection method based on multi-source data analysis, the core component condition coefficient is generated The method is as follows:

[0058] According to engine performance parameters and brake system performance parameters Calculate the core component condition factor , the formula is as follows:

[0059]

[0060] in, Engine performance parameters The weight coefficient is between 0.5 and 0.7; Braking system performance parameters The weight coefficient is between 0.3 and 0.5.

[0061] Preferably, in the above-mentioned second-hand car performance testing method based on multi-source data analysis, the translation tool receives the second-hand car performance testing report and presets a language list and a translation tool list; these languages ​​include Chinese, English, French, German and other languages;

[0062] Select the target language and translation tool through the language list and translation tool list, and call the selected translation tool to convert the used car performance inspection report into the selected target language.

[0063] Preferably, in the above-mentioned used car performance testing method based on multi-source data analysis, the translation tools include: Google Translate, Baidu Translate, Microsoft Translate, Trados, MemoQ and SmartCat, etc.

[0064] The present invention also discloses a second-hand car performance detection system based on multi-source data analysis, which is used to implement the above performance detection method:

[0065] The data collection module is used to obtain the basic information data of the second-hand vehicle, and obtain the comprehensive information data of the second-hand vehicle from different detection channels based on the basic information data of the second-hand vehicle;

[0066] The data analysis module analyzes and processes the comprehensive information data of used vehicles and generates at least the comprehensive performance coefficient of the interior and exterior decoration of the vehicle. , dynamic stability coefficient , Vehicle market price coefficient , Engine performance parameters , Braking system performance parameters and core component condition factor ;

[0067] The performance evaluation module generates a second-hand vehicle performance test report based on the basic information data of the second-hand vehicle and the data obtained from the above analysis;

[0068] Language conversion module, which translates the used car performance inspection report into the selected target language through integrated translation tools.

[0069] (III) Beneficial effects

[0070] The present invention provides a second-hand car performance detection method and system based on multi-source data analysis, which has the following beneficial effects:

[0071] (1) Obtaining basic information data of used vehicles, such as vehicle brand, model, year, VIN code, etc., through methods provided by vehicle owners can ensure the accuracy and completeness of this information; this is the basis for subsequent data analysis and testing, and helps to improve the reliability and effectiveness of the entire process; by obtaining data from multiple authoritative channels, such as DMV, traffic police network, third-party data platforms, 4S stores, insurance companies, etc., the authenticity of the information can be mutually verified, thereby improving the reliability of the comprehensive information of used vehicles;

[0072] (2) By analyzing the comprehensive information data of used vehicles, we can obtain multiple key indicators, including the comprehensive performance coefficient of vehicle interior and exterior decoration, dynamic stability coefficient, vehicle market price coefficient, engine performance parameters, brake system performance parameters and core component condition coefficient. These multi-dimensional evaluations can comprehensively and accurately reflect the overall condition of used vehicles, avoiding the one-sidedness that may be caused by single-dimensional evaluation;

[0073] (3) By integrating the coefficients in a predetermined order, the report content is clearly organized, allowing readers to quickly grasp the key information. This integration method helps readers make a comprehensive evaluation of used cars. By comparing the scores of different coefficients, readers can have a more comprehensive understanding of the advantages and disadvantages of the vehicle and make a more comprehensive judgment.

[0074] (4) By integrating translation tools to receive and process used car performance inspection reports, the time and labor required for manual translation are greatly reduced, thereby significantly improving translation efficiency; the preset language list and translation tool list cover a variety of commonly used languages ​​and efficient translation tools, meeting the diverse needs of different user groups and improving the internationalization level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 The present invention is a schematic diagram of the steps of a second-hand car performance detection method and system based on multi-source data analysis. DETAILED DESCRIPTION

[0076] 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.

[0077] See also Figure 1 The present invention provides a second-hand car performance detection method based on multi-source data analysis, comprising the following steps:

[0078] Step 1: Obtain basic information data of used vehicles, and obtain comprehensive information data of used vehicles from different testing channels based on the basic information data of used vehicles;

[0079] Step one includes the following steps:

[0080] Step 101: Obtain basic information data of a used vehicle, such as vehicle brand, model, year, VIN code, vehicle inspection date and location, through the method provided by the vehicle owner;

[0081] Step 102: After the basic information is confirmed to be correct, the system conducts inspections through multiple inspection channels based on the input vehicle basic information data to obtain comprehensive information about the used vehicle. The inspection channels include DMV or traffic police network, third-party data platforms such as Guazi.com, Yiche.com and Renrenche.com, 4S stores or authorized repair centers, insurance companies, vehicle inspection stations, information provided by the owner, and manual inspections;

[0082] Combined with the contents of step 101 to step 102:

[0083] Obtaining basic information data of used vehicles through this method provided by the car owner, such as vehicle brand, model, year, VIN code, etc., can ensure the accuracy and completeness of this information; this is the basis for subsequent data analysis and testing, and helps to improve the reliability and effectiveness of the entire process; by obtaining data from multiple authoritative channels, such as DMV, traffic police network, third-party data platforms, 4S stores, insurance companies, etc., the authenticity of the information can be verified mutually, thereby improving the reliability of the comprehensive information of used vehicles.

[0084] Step 2: Analyze and process the comprehensive information data of used vehicles to generate at least the comprehensive performance coefficient of the vehicle interior and exterior, the dynamic stability coefficient, the vehicle market price coefficient, the engine performance parameters, the brake system performance parameters and the core component condition coefficient;

[0085] Step 2 includes the following:

[0086] Step 201: receiving comprehensive information of used vehicles;

[0087] Step 202: The second-hand vehicle comprehensive information data includes at least the vehicle length , Vehicle width , Vehicle height and wheelbase , according to the vehicle length , Vehicle width , Vehicle height and wheelbase Calculate the proportional harmony index , the formula is as follows:

[0088]

[0089] in, for The weight coefficient is between 0.2 and 0.5; for The weight coefficient is between 0.1 and 0.4; for The weight coefficient is between 0.2 and 0.5; and ;

[0090] The formula uses a scaling factor such as , , To emphasize the relative relationship between different size parameters; weight coefficients such as , , The setting is based on vehicle design aesthetics and statistical data analysis; different vehicle types and design aesthetic standards require different weight allocations; the proportion coordination index obtained by calculation It can be used to quantitatively evaluate the proportional coordination of different vehicles. The higher the value, the more harmonious the body proportions are and the more in line with the design aesthetic standards;

[0091] Step 203: The comprehensive information data of the used vehicle also includes the paint thickness at the measuring point and average paint thickness , according to the paint thickness at the measuring point and average paint thickness Calculate paint surface flatness , the formula is as follows:

[0092]

[0093] in, For different measurement points, The values ​​are 1, 2, 3... , is the total number of measurement points;

[0094] It is The paint thickness at each measuring point;

[0095] The principle of this formula is to quantify the unevenness of the surface by calculating the average value of the absolute deviation of the height of each measuring point from the average height; the smaller the deviation, the flatter the surface; the larger the deviation, the more uneven the surface;

[0096] Step 204: The second-hand vehicle comprehensive information data also includes the number of functional components that are functioning normally. and total number of functional components , according to the number of functional components that work normally and total number of functional components Calculate functional completeness , the formula is as follows:

[0097]

[0098] It should be noted that functional parts include steering wheels, instrument panels, accelerator pedals, brake pedals, clutch pedals, transmission levers, seats, and seat belts;

[0099] Step 205: The second-hand vehicle comprehensive information data also includes the number of missing interior accessories And the total number of interior accessories , according to the number of missing interior accessories And the total number of interior accessories Calculate interior completeness , the formula is as follows:

[0100]

[0101] Step 206: According to the proportional coordination index , paint surface smoothness , Interior functionality score and interior integrity score Calculate the comprehensive performance coefficient of automobile interior and exterior , based on the following formula:

[0102]

[0103] in, Proportional coordination index The weight coefficient is between 0.1 and 0.3; For paint surface flatness The weight coefficient is between 0.1 and 0.3; Rate feature completeness The weight coefficient is between 0.2 and 0.4; Rate interior completeness The weight coefficient is between 0.2 and 0.4; and ;

[0104] Step 207: The used vehicle comprehensive information data includes the maximum lateral acceleration , Gravitational acceleration , moment of inertia , angular acceleration , the total mass of the vehicle m and the height of the center of gravity of the vehicle y, the dynamic stability coefficient DSI is calculated based on the above data, and the formula is as follows:

[0105]

[0106] It is the maximum lateral acceleration that the vehicle can achieve without skidding or rolling over. is the moment of inertia of the vehicle in the lateral direction about its center of mass; is the angular acceleration of the vehicle's center of mass in the lateral direction; represents a standardized maximum lateral acceleration term, which reflects the vehicle's ability to resist sideslip. is a correction term that takes into account the vehicle's moment of inertia, mass, center of gravity height, and center of mass angular acceleration. This correction term measures the stability of the vehicle when facing roll or rotation; the difference between the two gives a quantitative indicator for evaluating the dynamic stability of the vehicle;

[0107] Step 208: The second-hand vehicle comprehensive information data also includes the model order quantity DS, model production quantity SS, market price and the market average price MT, the market supply and demand index MSI is calculated according to the order volume DS, production volume SS, market price T and the market average price MT, and the formula is as follows:

[0108]

[0109] The model order volume DS is the order volume within 5 years; the model production volume SS is the production volume within 5 years; the formula principle is to directly compare the ratio of order volume to production volume, MSI>1, indicating that supply exceeds demand; MSI<1, indicating that supply exceeds demand; when DS is much larger than SS, the supply-demand ratio is high, indicating that market demand is more vigorous than supply, that is, there is a situation of supply exceeding demand; at this time, theoretically, the value of MSI should increase, because the market has a high desire for the product and supply cannot meet the demand; on the contrary, when DS is much smaller than SS, the supply-demand ratio is low, indicating that market supply exceeds demand; at this time, the value of MSI will decrease accordingly, because there are a large number of unsold products in the market; when the market price is close to the market average price, the price deviation is close to 1, indicating that the market is in a relatively balanced state; the lower the price deviation, the greater the gap between the market price and the market average price, and the market may be in a non-equilibrium state;

[0110] Step 209: The used vehicle comprehensive information data also includes the original purchase price , based on the current market price T of the model and the original purchase price The formula for calculating the vehicle value retention rate VR is as follows:

[0111]

[0112] The formula works by directly comparing the current market value of the vehicle to its original purchase price; the higher the retention rate, the better the value of the vehicle is maintained, that is, the less the value of the vehicle loses during use;

[0113] Step 210: The second-hand vehicle comprehensive information data also includes the number of repairs U, the vehicle age I and the mileage X. According to the market supply and demand index MSI, the original purchase price , vehicle value retention rate VR, vehicle maintenance times U, vehicle service life I and mileage X are used to calculate the vehicle value retention coefficient PU, based on the following formula:

[0114]

[0115] Among them, P1 is the weight coefficient of the market supply and demand index MSI, ranging from 0.2 to 0.4; P2 is the number of maintenance times The weight coefficient is 0.1~0.3; P3 is the weight coefficient of vehicle maintenance times U, which is 0.1~0.3; P4 is the mileage The weight coefficient is between 0.1 and 0.3;

[0116] The formula aims to evaluate the value retention coefficient of a vehicle through the combined effect of multiple factors; the market supply and demand index reflects the current market supply and demand situation and has a direct impact on the value of the vehicle; the vehicle value retention rate directly measures the ability of the vehicle to maintain its value; the number of repairs, vehicle age and mileage are indicators of vehicle usage and wear and tear, which have a negative impact on the value retention coefficient;

[0117] Step 211: Calculate the vehicle market price coefficient VMP according to the market supply and demand index MSI, the vehicle value retention rate VR and the vehicle value retention impact index PU, according to the following formula:

[0118]

[0119] in, is the weight coefficient of the market supply and demand index MSI, ranging from 0.3 to 0.5; is the weight coefficient of the vehicle value retention rate VR, ranging from 0.2 to 0.4; is the weight coefficient of the vehicle value retention coefficient PU, ranging from 0.2 to 0.4; and ;

[0120] The calculation principle of the vehicle market price coefficient is to comprehensively consider the market supply and demand index, vehicle value retention rate and vehicle value retention impact index through weighted summation, assign reasonable weights to each of them, and add these impacts to obtain a comprehensive evaluation value to reflect the relative price level of the vehicle under current market conditions;

[0121] Step 212: The second-hand vehicle comprehensive information data includes the engine effective power ,time and the total amount of fuel consumed G, the engine age AGE, the number of engine maintenance times RT and the engine effective torque ET;

[0122] According to the engine effective power , time t and total fuel consumption G to calculate effective fuel consumption rate , the formula is as follows:

[0123]

[0124] The principle of the formula is based on the concepts of energy conservation and conversion efficiency; the engine generates heat energy by burning fuel, which is then converted into mechanical energy output; the effective fuel consumption rate measures the amount of fuel that the engine needs to consume in order to produce a certain amount of effective work during this conversion process;

[0125] Step 213: Based on the effective fuel consumption rate , engine age AGE, engine maintenance times RT, engine effective power The engine performance parameter ECPI is calculated based on the engine effective torque ET, and the method is as follows:

[0126]

[0127] in, for The weight coefficient is between 0.1 and 1;

[0128] The numerator directly reflects the power output capacity of the engine. The larger the product, the better the engine's performance in providing power and torque. The denominator is an indicator that comprehensively considers economy and reliability. The reduction in fuel consumption rate means that the engine is more economical and energy-saving during operation. At the same time, the increase in service life and maintenance times inevitably reflects the decline in engine reliability. This impact is weighted and adjusted through the weight coefficient to more accurately reflect the weight in the overall performance evaluation.

[0129] Step 214: The comprehensive information data of the used vehicle includes the remaining thickness TP of the brake pad, the braking response time TR, the wear thickness TD of the brake disc, and the cold braking distance. ;Hot braking distance , bottom line distance L, reference distance Z; based on cold braking distance ;Hot braking distance , bottom line distance L, reference distance The calculation of the braking performance index BI is based on the following formula:

[0130]

[0131] in, for The weight coefficient is between 0.6 and 0.7; for The weight coefficient is 0.3~0.4; and ;

[0132] The calculation principle of the braking index is based on the braking performance of the vehicle in cold and hot states; the cold braking distance refers to the distance required for the vehicle to decelerate from 100 kilometers per hour to 0 when the braking system has not yet heated up, such as after long-term driving or frequent braking; the hot braking distance is the distance required to decelerate from 100 kilometers per hour to 0 after the braking system has heated up; by comparing the braking distances in cold and hot states with the benchmark and bottom line distances, and weighted summing them according to their respective importance, an index that comprehensively reflects the braking performance of the vehicle is obtained; the higher the index, the better the braking performance of the vehicle in cold and hot states; the bottom line distance L is the theoretical braking distance for the vehicle to decelerate from 100 kilometers per hour to 0 when the average braking deceleration is 0.7g, which is used as the bottom line of braking performance; the benchmark distance Z is the theoretical braking distance for the vehicle to decelerate from 100 kilometers per hour to 0 when the average braking deceleration is 0.95g, which is used as an excellent benchmark for braking performance; g here is the acceleration of gravity;

[0133] Step 215: Calculate the brake system performance parameter BSEI according to the remaining thickness TP of the brake pad, the brake response time TR, the brake disc wear thickness TD and the brake performance index BI, according to the following formula:

[0134]

[0135] The principle of this formula is to calculate a comprehensive brake system performance index by combining the remaining thickness of the brake pad and the brake performance index as positive influencing factors, that is, the larger the value, the greater the contribution to the brake system performance, and the brake response time and the brake disc wear thickness as negative influencing factors, that is, the larger the value, the adverse effect on the brake system performance. The index is designed to quantify the comprehensive performance of the brake system in multiple key performance parameters, so as to quickly evaluate the overall performance of the brake system;

[0136] Step 216: Calculate the core component condition coefficient CIV according to the engine performance parameter ECPI and the brake system performance parameter BSEI, based on the following formula:

[0137]

[0138] in, is the weight coefficient of the engine performance parameter ECPI, ranging from 0.5 to 0.7; is the weight coefficient of the brake system performance parameter BSEI, and its value is 0.3~0.5;

[0139] Combined with the contents of step 201 to step 216:

[0140] By analyzing the comprehensive information data of used vehicles, we obtain several key indicators, including the comprehensive performance coefficient of vehicle interior and exterior, dynamic stability coefficient, vehicle market price coefficient, engine performance parameters, brake system performance parameters and core component condition coefficient. These multi-dimensional evaluations can comprehensively and accurately reflect the overall condition of used cars, avoiding the one-sidedness that may be caused by single-dimensional evaluation.

[0141] Step 3: Generate a used car performance test report based on the basic information data of the used car and the data obtained from the above analysis;

[0142] Step three includes the following steps:

[0143] Step 301: receiving basic information data of a used vehicle and data obtained by analyzing comprehensive information data of the used vehicle;

[0144] Step 302: Integrate the above data into a document in a predetermined order, and generate a used car performance test report after integration; the predetermined order is: basic information data of used vehicles first; data obtained from analysis of comprehensive information data of used vehicles last;

[0145] It should be noted that the data obtained from the analysis of the comprehensive information data of used vehicles include: proportion coordination index ML, paint surface smoothness PFL, interior functionality score IFS, interior integrity score IIC, comprehensive performance coefficient of automobile interior and exterior decoration AVQ, dynamic stability coefficient DSI, market supply and demand index MSI, vehicle value retention rate VR, vehicle value retention impact index PU, vehicle market price coefficient VMP, engine performance coefficient ECPI, brake system performance coefficient BSEI and core component condition coefficient CIV; the used vehicle performance inspection report at least includes: vehicle brand, model, year, VIN code, vehicle inspection date and place; proportion coordination index ML, paint surface smoothness PFL, interior functionality score IFS, interior integrity score IIC, comprehensive performance coefficient of automobile interior and exterior decoration AVQ, dynamic stability coefficient DSI, market supply and demand index MSI, vehicle value retention rate VR, vehicle value retention impact index PU, vehicle market price coefficient VMP, engine performance coefficient ECPI, brake system performance coefficient BSEI and core component condition coefficient CIV.

[0146] Combined with the contents of step 301 to step 302:

[0147] By integrating the coefficients in a predetermined order, the report content is clearly organized, allowing readers to quickly grasp key information; this integration method helps readers make a comprehensive evaluation of used cars; by comparing the scores of different coefficients, readers can have a more comprehensive understanding of the advantages and disadvantages of the vehicle, and thus make a more comprehensive judgment.

[0148] Step 4: Translate the used car performance inspection report into the selected target language through the integrated translation tool;

[0149] Step 4 includes the following steps:

[0150] Step 401: The translation tool receives the used car performance inspection report and presets a language list and a translation tool list;

[0151] Step 402: through the system interface, select the target language and translation tool from the preset language list and translation tool list, the languages ​​include Chinese, English, French, German and other languages; the translation tools include Google Translate, Baidu Translate, Microsoft Translate, Trados, MemoQ and SmartCat and the like;

[0152] Step 403: calling the selected translation tool to convert the used car performance inspection report into the selected target language;

[0153] Combined with the contents of step 401 to step 404:

[0154] By integrating translation tools to receive and process used car performance inspection reports, the time and labor required for manual translation are greatly reduced, thereby significantly improving translation efficiency; the preset language list and translation tool list cover a variety of commonly used languages ​​​​and efficient translation tools, meeting the diverse needs of different user groups and improving the level of internationalization.

[0155] On the other hand, the present invention also discloses a second-hand car performance detection system based on multi-source data analysis, which is used to implement the above performance detection method, including:

[0156] The data collection module is used to obtain the basic information data of the second-hand vehicle, and obtain the comprehensive information data of the second-hand vehicle from different detection channels based on the basic information data of the second-hand vehicle;

[0157] The data analysis module analyzes and processes the comprehensive information data of used vehicles, and generates at least the comprehensive performance coefficient AVQ of the vehicle interior and exterior, the dynamic stability coefficient DSI, the vehicle market price coefficient VMP, the engine performance parameter ECPI, the brake system performance parameter BSEI and the core component condition coefficient CIV;

[0158] The performance evaluation module generates a second-hand vehicle performance test report based on the basic information data of the second-hand vehicle and the data obtained from the above analysis;

[0159] Language conversion module, which translates the used car performance inspection report into the selected target language through integrated translation tools.

[0160] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A second-hand car performance detection method based on multi-source data analysis, characterized in that: The following steps are involved: Obtaining basic information data of used vehicles, and obtaining comprehensive information data of used vehicles from different testing channels based on the basic information data of used vehicles; Analyze and process the comprehensive information data of used vehicles to generate at least the comprehensive performance coefficient of the interior and exterior decoration of the vehicle , dynamic stability coefficient , Vehicle market price coefficient , Engine performance parameters , Braking system performance parameters and core component condition factor ; Generate a used car performance test report based on the basic information data of the used car and the data obtained from the above analysis; Translate used car performance inspection reports into selected target languages ​​via integrated translation tools.

2. A second-hand car performance detection method based on multi-source data analysis according to claim 1, characterized in that: By analyzing and processing the comprehensive information data of used vehicles, we can get the following proportion coordination index: , paint surface smoothness , Interior functionality score , Interior integrity score , dynamic stability coefficient , Market Supply and Demand Index , Vehicle value retention rate , Vehicle value retention coefficient , Engine performance parameters , Braking system performance parameters ; According to the proportional coordination index , paint surface smoothness , Interior functionality score and interior integrity score Generate comprehensive performance coefficient of automotive interior and exterior ; According to the market supply and demand index , Vehicle value retention rate , Vehicle value retention coefficient Generate vehicle market price coefficient ; According to engine performance parameters and brake system performance parameters Generate core component condition factors .

3. A second-hand car performance detection method based on multi-source data analysis according to claim 2, characterized in that: Generate comprehensive performance coefficient of automotive interior and exterior The method is as follows: The comprehensive information data of used vehicles includes at least the length of the vehicle , Vehicle width , Vehicle height and wheelbase , according to the vehicle length , Vehicle width , Vehicle height and wheelbase Calculate the proportional harmony index , the formula is as follows: in, for The weight coefficient is between 0.2 and 0.5; for The weight coefficient is between 0.1 and 0.4; for The weight coefficient is between 0.2 and 0.5; and ; The comprehensive information data of used vehicles also includes the paint thickness at the measurement point and average paint thickness , according to the paint thickness of the measuring point and average paint thickness Calculate paint surface flatness , the formula is as follows: in, For different measurement points, The values ​​are 1, 2, 3... , is the total number of measurement points; It is The paint thickness at each measuring point; The comprehensive information data of used vehicles also includes the number of normal functional parts and total number of functional components , according to the number of normal functional components and total number of functional components Calculate the functional completeness score , the formula is as follows: The comprehensive information data of used vehicles also includes the number of missing interior accessories And the total number of interior accessories , according to the number of missing interior accessories And the total number of interior accessories Calculating the interior completeness score , the formula is as follows: According to the proportional coordination index , paint surface smoothness , Interior functionality score and interior integrity score Calculate the comprehensive performance coefficient of automobile interior and exterior , based on the following formula: in, Proportional coordination index The weight coefficient is between 0.1 and 0.3; For paint surface flatness The weight coefficient is between 0.1 and 0.3; Rate feature completeness The weight coefficient is between 0.2 and 0.4; Rate interior completeness The weight coefficient is between 0.2 and 0.4; and .

4. A second-hand car performance detection method based on multi-source data analysis according to claim 3, characterized in that: Generate dynamic stability coefficient The method is as follows: The comprehensive information data of used vehicles also includes the maximum lateral acceleration , Gravitational acceleration , moment of inertia , angular acceleration , the total mass of the vehicle and the vehicle's center of gravity , calculate the dynamic stability coefficient based on the above data , the formula is as follows:

5. A second-hand car performance detection method based on multi-source data analysis according to claim 4, characterized in that: Generate vehicle market price coefficient The method is as follows: Second-hand vehicle comprehensive information data also includes model order volume , Model Production Volume , market price and the average market price , according to the order quantity , Production , market price and the average market price Calculate market supply and demand index , the formula is as follows: Used vehicle comprehensive information data also includes the original purchase price , according to market prices and original purchase price Calculate vehicle value retention rate , the formula is as follows: The comprehensive information data of used vehicles also includes the number of repairs , Vehicle age and mileage , according to the market supply and demand index , Original purchase price , Vehicle value retention rate , Vehicle maintenance times , Vehicle age and mileage Calculate the vehicle value retention coefficient , the formula is as follows: in, Market supply and demand index The weight coefficient is between 0.2 and 0.4; Maintenance times The weight coefficient is between 0.1 and 0.3; Number of vehicle repairs The weight coefficient is between 0.1 and 0.3; For mileage The weight coefficient is between 0.1 and 0.3; According to the market supply and demand index , Vehicle value retention rate and vehicle value retention index Calculate vehicle market price coefficient , based on the following formula: in, Market supply and demand index The weight coefficient is between 0.3 and 0.5; Vehicle value retention rate The weight coefficient is between 0.2 and 0.4; The influence factor of vehicle value preservation The weight coefficient is between 0.2 and 0.4; and .

6. A second-hand car performance detection method based on multi-source data analysis according to claim 5, characterized in that: Generate and calculate engine performance parameters The method is as follows: The comprehensive information data of used vehicles also includes engine effective power ,time Total fuel consumed , Engine service life , Number of engine maintenance and engine effective torque ; According to the engine effective power ,time Total fuel consumed Calculate effective fuel consumption , the formula is as follows: According to the effective fuel consumption , Engine service life , Number of engine maintenance , Engine effective power and engine effective torque Calculate engine performance parameters , the method based on is as follows: in, for The weight coefficient is between 0.1 and 1.

7. A second-hand car performance detection method based on multi-source data analysis according to claim 6, characterized in that: Generate brake system performance parameters The method is as follows: The comprehensive information data of used vehicles also includes the remaining thickness of the brake pads , Braking response time and brake disc wear thickness , cold braking distance ;Hot braking distance , bottom line distance , reference distance ; Based on cold braking distance ;Hot braking distance , bottom line distance , reference distance Calculate the Braking Performance Index , the formula is as follows: in, for The weight coefficient is between 0.6 and 0.7; for The weight coefficient is 0.3~0.4; and ; According to the remaining thickness of the brake pad , Braking response time , Brake disc wear thickness and Braking Performance Index Calculate brake system performance parameters , the formula is as follows:

8. A second-hand car performance detection method based on multi-source data analysis according to claim 7, characterized in that: Generate core component condition factors The method is as follows: According to engine performance parameters and brake system performance parameters Calculate the core component condition factor , the formula is as follows: in, Engine performance parameters The weight coefficient is between 0.5 and 0.7; Braking system performance parameters The weight coefficient is between 0.3 and 0.

5.

9. A second-hand car performance detection method based on multi-source data analysis according to claim 8, characterized in that: The translation tool receives the used car performance inspection report and presets a language list and a translation tool list; Select the target language and translation tool through the language list and translation tool list, and call the selected translation tool to convert the used car performance inspection report into the selected target language.

10. A second-hand car performance detection system based on multi-source data analysis, used to implement the second-hand car performance detection method according to any one of claims 1 to 9, characterized in that: The following steps are involved: The data collection module is used to obtain the basic information data of the second-hand vehicle, and obtain the comprehensive information data of the second-hand vehicle from different detection channels based on the basic information data of the second-hand vehicle; The data analysis module analyzes and processes the comprehensive information data of used vehicles and generates at least the comprehensive performance coefficient of the interior and exterior decoration of the vehicle. , dynamic stability coefficient , Vehicle market price coefficient , Engine performance parameters , Braking system performance parameters and core component condition factor ; The performance evaluation module generates a second-hand vehicle performance test report based on the basic information data of the second-hand vehicle and the data obtained from the above analysis; Language conversion module, which translates the used car performance inspection report into the selected target language through integrated translation tools.

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