Vehicle detection index scoring method and terminal

Abstract

The invention discloses a vehicle detection index scoring method and a terminal. The method comprises the following steps: acquiring vehicle detection index data and vehicle information data in unimodal distribution; grouping the vehicle detection index data according to a classification criterion selected from the vehicle information data and acquiring grouped data; performing distribution fitting calculation on each grouped data by using t distribution; obtaining a probability density function of each piece of grouped data according to a distribution fitting calculation result, and recording a vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; analyzing each piece of vehicle detection index data, and if the corresponding score is in an increase trend when the value of the vehicle detection index data increases, converting the vehicle detection index data into a vehicle score by taking a mark value as a full score point and combining a cumulative distribution function; and if the corresponding score is in a decreasing trend when the vehicle detection index data value is increased, converting the vehicle detection index data into the vehicle score by taking the mark value as a full score point and combining the residual distribution function.

Description

[0001] This case is a divisional application based on the invention patent with application date of 2024-09-04, application number 202411230532.4, and name “A scoring method and terminal for vehicle inspection indicators with unimodal distribution” as the parent case. Technical Field

[0002] The present invention relates to the field of vehicle performance evaluation, and in particular to a scoring method and a terminal for vehicle detection indicators with unimodal distribution. Background Art

[0003] Batteries are the core components of electric vehicles, and the quantitative scoring of vehicle inspection indicators is an important part of the inspection report. A reasonable scoring scheme is conducive to users, car dealers and users to intuitively evaluate the value and quality of vehicles. In vehicle performance evaluation and testing, it is necessary to quantify and horizontally compare the performance indicators of different vehicles, but the existing technology has the following problems: when processing unimodal distribution data, the existing scoring method often uses a simple linear transformation or a single distribution model, which fails to fully consider the actual distribution characteristics of the data, and the scoring results are difficult to reflect the true situation of the data. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a scoring method and terminal for vehicle detection indicators, so as to solve the problem that the vehicle detection indicator data with unimodal distribution cannot be quantified for horizontal comparison.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A scoring method for vehicle detection indicators comprises the following steps: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; S2, grouping the vehicle detection index data according to a classification standard selected from the vehicle information data and obtaining grouped data; S3, performing distribution fitting calculation on each of the grouped data using t distribution; S4, obtaining a probability density function of each of the grouped data according to the distribution fitting calculation result and recording the vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; S5. Analyze each of the vehicle detection index data. If the corresponding score shows an increasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the cumulative distribution function; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the residual distribution function.

[0006] In order to solve the above technical problems, another technical solution adopted by the present invention is: A vehicle detection index scoring terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor completes the following steps when executing the computer program: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; S2, grouping the vehicle detection index data according to a classification standard selected from the vehicle information data and obtaining grouped data; S3, performing distribution fitting calculation on each of the grouped data using t distribution; S4, obtaining a probability density function of each of the grouped data according to the distribution fitting calculation result and recording the vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; S5. Analyze each of the vehicle detection index data. If the corresponding score shows an increasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the cumulative distribution function; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the residual distribution function.

[0007] The beneficial effects of the present invention are as follows: the present invention adopts a scientific and reasonable fitting and scoring method for unimodal distribution of vehicle detection index data, solves the problem that traditional scoring methods are difficult to handle unimodal distribution data, improves the accuracy and objectivity of scoring, and has significant practical application value. Specifically, it is embodied in: (1) Accurately identify unimodal distribution: The t-distribution fitting method can accurately identify and fit unimodal distribution data, and obtain the probability density function and its highest point (marked value) of each group of data. This enables the scoring method to accurately reflect the distribution characteristics of the data and improve the accuracy of data analysis.

[0008] (2) Wide applicability: The scoring method of the present invention is applicable to a variety of vehicle detection index data with unimodal distribution, and can be widely used in different types of vehicle detection and evaluation scenarios. By converting vehicle detection index data into a unified score, the performance of different vehicles can be quantified and compared horizontally, thereby improving the comparability and analytical value of the data.

[0009] (3) Scientific and reasonable scoring method: By performing probability density function analysis on each grouped data, the highest point (marked value) is found as the benchmark point for scoring, and the scoring conversion is performed by combining the cumulative distribution function (CDF) and the residual distribution function (SF). This ensures that the scoring method can reflect the actual relationship between the vehicle inspection index data and the score, making the scoring result more objective and fair. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a scoring method for a vehicle detection index with unimodal distribution in an embodiment of the present invention; Figure 2 A specific flow chart of a scoring method for a vehicle detection index with unimodal distribution in an embodiment of the present invention; Figure 3 is a schematic diagram of a probability density function in an embodiment of the present invention; Figure 4 is a schematic diagram of a cumulative distribution function in an embodiment of the present invention; Figure 5 is a schematic diagram of a residual distribution function in an embodiment of the present invention; Figure 6 It is a schematic diagram of a scoring terminal for a vehicle detection index with a unimodal distribution in an embodiment of the present invention; Description of labels: 1. A scoring terminal for vehicle detection indicators with unimodal distribution; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0011] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0012] The relevant terms in the present invention are explained as follows: VIN code: Vehicle identification number.

[0013] t distribution: t distribution is a probability distribution in statistics, often used to infer population parameters in small sample cases.

[0014] Probability density function: The probability density function (PDF) of the t distribution describes the probability distribution of a random variable that follows the t distribution.

[0015] Cumulative Distribution Function: The Cumulative Distribution Function (CDF) of the t-distribution describes the probability that a random variable is less than or equal to a given value.

[0016] Survival function (SF) of t distribution describes the probability that a random variable is greater than a given value. For t distribution, its SF is the complement of the cumulative distribution function.

[0017] k-means: k-means is a commonly used clustering algorithm that is used to divide data points into K clusters with similar characteristics. This algorithm is widely used in data mining, machine learning, and pattern recognition.

[0018] Please refer to Figures 1 to 5 , a scoring method for a vehicle detection index with a unimodal distribution, comprising the steps of: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; S2, grouping the vehicle detection index data according to a classification standard selected from the vehicle information data and obtaining grouped data; S3, performing distribution fitting calculation on each of the grouped data using t distribution; S4, obtaining a probability density function of each of the grouped data according to the distribution fitting calculation result and recording the vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; S5. Analyze each of the vehicle detection index data. If the corresponding score shows an increasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the cumulative distribution function; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score using the mark value as the full score and in combination with the residual distribution function.

[0019] From the above description, it can be seen that the present invention adopts a scientific and reasonable fitting and scoring method for unimodal distribution of vehicle detection index data, solves the problem that traditional scoring methods are difficult to handle unimodal distribution data, improves the accuracy and objectivity of scoring, and has significant practical application value. Specifically, it is embodied in: (1) Accurately identify unimodal distribution: The t-distribution fitting method can accurately identify and fit unimodal distribution data, and obtain the probability density function and its highest point (marked value) of each group of data. This enables the scoring method to accurately reflect the distribution characteristics of the data and improve the accuracy of data analysis.

[0020] (2) Wide applicability: The scoring method of the present invention is applicable to a variety of vehicle detection index data with unimodal distribution, and can be widely used in different types of vehicle detection and evaluation scenarios. By converting vehicle detection index data into a unified score, the performance of different vehicles can be quantified and compared horizontally, thereby improving the comparability and analytical value of the data.

[0021] (3) Scientific and reasonable scoring method: By performing probability density function analysis on each grouped data, the highest point (marked value) is found as the benchmark point for scoring, and the scoring conversion is performed by combining the cumulative distribution function (CDF) and the residual distribution function (SF). This ensures that the scoring method can reflect the actual relationship between the vehicle inspection index data and the score, making the scoring result more objective and fair.

[0022] Please refer to Figures 4 to 5 In an embodiment of the present invention, the step S5 specifically includes the steps of: S51, analyzing each of the vehicle detection index data, if the corresponding score shows an increasing trend when the vehicle detection index data value increases, then proceeding to step S52; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, then proceeding to step S53; S52. Taking the mark value as the full score and combining it with the cumulative distribution function, a vehicle score calculation formula is obtained: ; In the formula, represents the vehicle score; p represents the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle detection index data is converted into a vehicle score using the vehicle score calculation formula; S53, taking the mark value as the full score and combining it with the residual distribution function to obtain the vehicle score calculation formula: ; In the formula, represents the vehicle score; p represents the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle scoring calculation formula is used to convert the vehicle detection index data into a vehicle score.

[0023] From the above description, it can be seen that in step S5, by analyzing the correlation between each vehicle detection index data and the score, it is automatically determined whether the data is positively correlated or negatively correlated, so as to select the corresponding score conversion method to ensure the accuracy of the score; the mark value is used as the full score point, and the score calculation formula of the cumulative distribution function or the residual distribution function is used, which is applicable to various vehicle detection index data, and effective score conversion can be performed regardless of its distribution characteristics.

[0024] In an embodiment of the present invention, the step S52 further includes the steps of: The input vehicle detection index data is judged. If the vehicle detection index data is greater than the mark value, the vehicle score is deemed to be full marks; if the vehicle detection index data is less than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

[0025] From the above description, it can be seen that in step S4, the vehicle detection index data value corresponding to the highest point in the probability density function is recorded as the mark value, that is, after the grouped data is calculated through distribution fitting, the highest point extracted from the calculation result represents the optimal value of this type of detection index value, and the optimal value is the full score point as the scoring limit. Under the above-mentioned positive correlation conditions, when the vehicle detection index data is greater than the mark value, it is also regarded as a full score. When the vehicle detection index data is less than the mark value, it is substituted into the vehicle scoring calculation formula to calculate the actual score; however, when a negative value appears according to the vehicle scoring calculation formula, the score is regarded as zero for the convenience of display and statistics.

[0026] In an embodiment of the present invention, the step S53 further includes the steps of: The input vehicle detection index data is judged. If the vehicle detection index data is less than the mark value, the vehicle score is deemed to be full marks; if the vehicle detection index data is greater than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

[0027] From the above description, it can be seen that in step S4, the vehicle detection index data value corresponding to the highest point in the probability density function is recorded as the mark value, that is, after the grouped data is calculated through distribution fitting, the highest point extracted from the calculation result represents the optimal value of this type of detection index value, and the optimal value is the full score point as the scoring limit. Under the above-mentioned negative correlation condition, when the vehicle detection index data is less than the mark value, it is also regarded as a full score. When the vehicle detection index data is greater than the mark value, it is substituted into the vehicle scoring calculation formula to calculate the actual score; however, when a negative value appears according to the vehicle scoring calculation formula, the score is regarded as zero for the convenience of display and statistics.

[0028] That is, when the test index data is greater than or less than the marked value, the full score and zero score are set reasonably to ensure the rationality and consistency of the scoring results.

[0029] In an embodiment of the present invention, the step S1 further comprises the steps of: The vehicle information data is extracted using the vehicle VIN code.

[0030] From the above description, it can be seen that extracting vehicle information data through the vehicle VIN code can accurately associate the detailed information of each vehicle and provide a reliable data basis for subsequent grouping and clustering.

[0031] Please refer to Figure 6 A scoring terminal 1 for a vehicle detection index with a unimodal distribution includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in a scoring method for a vehicle detection index with a unimodal distribution are completed.

[0032] The present invention provides a scoring method and terminal for vehicle detection indexes with unimodal distribution, which are mainly used to quantify vehicle detection index data with unimodal distribution and enable horizontal comparison between different vehicles.

[0033] Please refer to Figures 1 to 5 , Embodiment 1 of the present invention is: A scoring method for a unimodal distribution vehicle detection index comprises the steps of: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; S2, grouping the vehicle detection index data according to the classification standard selected from the vehicle information data and obtaining grouped data; S3, using t distribution to perform distribution fitting calculation on each grouped data; S4. Obtain the probability density function of each grouped data according to the distribution fitting calculation result and record the vehicle detection index data value corresponding to the highest point in the probability density function as the mark value; S5. Analyze each vehicle detection index data. If the corresponding score shows an increasing trend when the vehicle detection index data value increases, the marked value is used as the full score point and the cumulative distribution function is used to convert the vehicle detection index data into a vehicle score; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the marked value is used as the full score point and the residual distribution function is used to convert the vehicle detection index data into a vehicle score.

[0034] Specifically, in step S1, the vehicle information data is extracted using the vehicle VIN code.

[0035] From the above description, it can be seen that the present invention adopts a scientific and reasonable fitting and scoring method for unimodal distribution of vehicle detection index data, solves the problem that traditional scoring methods are difficult to handle unimodal distribution data, improves the accuracy and objectivity of scoring, and has significant practical application value. Specifically, it is embodied in: (1) Accurately identify unimodal distribution: The t-distribution fitting method can accurately identify and fit unimodal distribution data, and obtain the probability density function and its highest point (marked value) of each group of data. This enables the scoring method to accurately reflect the distribution characteristics of the data and improve the accuracy of data analysis.

[0036] (2) Wide applicability: The scoring method of the present invention is applicable to a variety of vehicle detection index data with unimodal distribution, and can be widely used in different types of vehicle detection and evaluation scenarios. By converting vehicle detection index data into a unified score, the performance of different vehicles can be quantified and compared horizontally, thereby improving the comparability and analytical value of the data.

[0037] (3) Scientific and reasonable scoring method: By performing probability density function analysis on each grouped data, the highest point (marked value) is found as the benchmark point for scoring, and the scoring conversion is performed by combining the cumulative distribution function (CDF) and the residual distribution function (SF). This ensures that the scoring method can reflect the actual relationship between the vehicle inspection index data and the score, making the scoring result more objective and fair.

[0038] Please refer to Figures 4 to 5 , Embodiment 2 of the present invention is: Based on the first embodiment, step S5 specifically includes the following steps: S51, analyzing each vehicle detection index data, if the corresponding score shows an increasing trend when the vehicle detection index data value increases, then proceed to step S52; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, then proceed to step S53; S52. Taking the mark value as the full score, combined with the cumulative distribution function, the vehicle score calculation formula is obtained: ; In the formula, represents the vehicle score; p represents the vehicle detection index data; a and b represent the parameters calculated based on the relationship between the mark value and the full score; The vehicle inspection index data is converted into a vehicle score using a vehicle score calculation formula; wherein, the input vehicle inspection index data is judged, if the vehicle inspection index data is greater than the mark value, the vehicle score is deemed to be full marks; if the vehicle inspection index data is less than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score, if the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

[0039] S53. Taking the mark value as the full score and combining it with the residual distribution function, the vehicle score calculation formula is obtained: ; In the formula, represents the vehicle score; p represents the vehicle detection index data; a and b represent the parameters calculated based on the relationship between the mark value and the full score; The vehicle score calculation formula is used to obtain the vehicle detection index data and convert it into a vehicle score; wherein, the input vehicle detection index data is judged, if the vehicle detection index data is less than the mark value, the vehicle score is regarded as full marks; if the vehicle detection index data is greater than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score, if the detected vehicle score is a negative value, the vehicle score is regarded as zero.

[0040] Specifically, the calculation examples of parameters a and b are as follows: Define the mark value p as the full score, and the full score is 100 points; select the median point according to the nature of the vehicle inspection index data and the vehicle information data, for example, define the score at 0.1 times p as 50 points, and bring the two sets of data into the vehicle score calculation formula to obtain the specific values ​​of a and b, and then get the corresponding vehicle score based on the actual input vehicle inspection index data.

[0041] Please refer to Figure 2 , Embodiment 3 of the present invention is: An example of the application of a scoring method for a unimodal distribution of vehicle detection indicators is as follows: S1. Extract corresponding vehicle inspection index data from the vehicle inspection order, and extract vehicle information data such as vehicle battery type, manufacturer, model, brand data, etc. according to the vehicle VIN code; S2. Grouping the index data according to the specified vehicle information classification field, where the classification field is selected from the vehicle information. In this example, the vehicle model is used as the classification field; The process is as follows: Get vehicle information and test index data based on VIN code:

[0042] Now, select "Model" as the classification field to group the inspection metric data for these vehicles by model: Grouping results: Vehicle Type: SUV VIN: 1A2B3C4D, test index (fuel consumption): 8.5 VIN: 3C4D5E6F, fuel consumption: 9.0 VIN: 5E6F7G8H, test index (fuel consumption): 7.8 Model: Sedan VIN: 2B3C4D5E, test index (fuel consumption): 6.2 VIN: 4D5E6F7G, fuel consumption: 6.5 VIN: 6F7G8H9I, test index (fuel consumption): 6.0 S3. According to each group of grouped data, use t distribution to perform distribution fitting calculation on the detection index; S5. Obtain the probability density function of each grouped data of the detection index from the distribution fitting calculation results. The detection index value corresponding to the point with the highest density is recorded as peak_loc (mark value), such as Figure 3 ; S6, please refer to Figure 4 , if the detection index value is larger, the better, it is converted into a score according to the following process: 1) Use the cumulative distribution function of the detection indicator for calculation, such as Figure 4 As shown; 2) According to the peak_loc obtained in step 4, obtain the corresponding cumulative probability, denoted as p; 3) Define the score at probability p as 100 points and the score at 0.1p as 50 points, according to the formula: ; Substitute the values ​​of coefficients a and b into the calculation to obtain the calculation formula for converting probability p to score. Then, the detection index value can be converted into cumulative probability first and then into score to complete the conversion of detection index value into score. 4) If the detection index value is greater than peak_loc, the score is fixed to the highest score of 100 points; if it is less than or equal to peak_loc, the score calculation formula is used to convert the cumulative probability corresponding to the detection index value into a calculated score. If score<0, the score is fixed to the lowest score of 0 points. Otherwise, the calculated converted score is taken as the final conversion score.

[0043] Please refer to Figure 5 , if the detection index value is as small as possible, it is converted into a score according to the following process: 1) Calculate using the residual distribution function of the detection indicator; 2) According to the peak_loc obtained in step 4, obtain the corresponding residual probability, denoted as p; 3) Define the score at probability p as 100 points and the score at 0.1p as 50 points, according to the formula: ; Substitute the values ​​of coefficients a and b into the calculation to obtain the calculation formula for converting probability p to score. Then, the detection index value can be converted into cumulative probability first and then into score to complete the conversion of detection index value into score. 4) If the detection index value is less than peak_loc, the score is fixed to the highest score of 100 points; if it is greater than or equal to peak_loc, the score calculation formula is used to convert the cumulative probability corresponding to the detection index value into a calculated score. If score<0, the score is fixed to the lowest score of 0 points. Otherwise, the calculated converted score is taken as the final conversion score.

[0044] Please refer to Figure 6 The fourth embodiment of the present invention is: a scoring terminal 1 for a vehicle detection index with a unimodal distribution, comprising a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in the scoring method for a vehicle detection index with a unimodal distribution in any one of embodiments one to three are completed.

[0045] In summary, the present invention adopts a scientific and reasonable fitting and scoring method for unimodal distribution of vehicle detection index data, solves the problem that traditional scoring methods are difficult to handle unimodal distribution data, improves the accuracy and objectivity of scoring, and has significant practical application value. Specifically, it is embodied in: (1) Accurately identify unimodal distribution: The t-distribution fitting method can accurately identify and fit unimodal distribution data, and obtain the probability density function and its highest point (marked value) of each group of data. This enables the scoring method to accurately reflect the distribution characteristics of the data and improve the accuracy of data analysis.

[0046] (2) Wide applicability: The scoring method of the present invention is applicable to a variety of vehicle detection index data with unimodal distribution, and can be widely used in different types of vehicle detection and evaluation scenarios. By converting vehicle detection index data into a unified score, the performance of different vehicles can be quantified and compared horizontally, thereby improving the comparability and analytical value of the data.

[0047] (3) Scientific and reasonable scoring method: By performing probability density function analysis on each grouped data, the highest point (marked value) is found as the benchmark point for scoring, and the scoring conversion is performed by combining the cumulative distribution function (CDF) and the residual distribution function (SF). This ensures that the scoring method can reflect the actual relationship between the vehicle inspection index data and the score, making the scoring result more objective and fair.

[0048] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A scoring method for vehicle detection indicators, characterized in that: Includes steps: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; the vehicle information data includes vehicle battery type, manufacturer, model and brand data; S2, grouping the vehicle detection index data according to a classification standard selected from the vehicle information data and obtaining grouped data; S3, performing distribution fitting calculation on each of the grouped data using t distribution; S4, obtaining a probability density function of each of the grouped data according to the distribution fitting calculation result and recording the vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; S5, analyzing each of the vehicle detection index data, if the corresponding score shows an increasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score by taking the marked value as the full score point and combining it with the cumulative distribution function; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score by taking the marked value as the full score point and combining it with the residual distribution function; The step S5 specifically comprises the following steps: S51, analyzing each of the vehicle detection index data, if the vehicle detection index data value is positively correlated with the score, proceeding to step S52; if the vehicle detection index data value is negatively correlated with the score, proceeding to step S53; S52. Combined with the cumulative distribution function, the score at the cumulative probability corresponding to the mark value is 100 points, and the score at 0.1 times the cumulative probability corresponding to the mark value is 50 points. Substitute them into the vehicle score calculation formula to calculate parameters a and b. The formula is as follows: ； In the formula, represents the vehicle score; p represents the probability corresponding to the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle detection index data is converted into a vehicle score using the vehicle score calculation formula; S53. Combine the residual distribution function, take the score at the residual probability corresponding to the mark value as 100 points, take the score at 0.1 times the residual probability corresponding to the mark value as 50 points, substitute into the vehicle score calculation formula to calculate the parameters a and b, the formula is as follows: ； In the formula, represents the vehicle score; p represents the probability corresponding to the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle detection index data is converted into a vehicle score using the vehicle score calculation formula.

2. A scoring method for vehicle detection indicators according to claim 1, characterized in that: The step S52 further comprises the steps of: The input vehicle detection index data is judged, and if the vehicle detection index data is greater than the mark value, the vehicle score is deemed to be full score; If the vehicle detection index data is less than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

3. A scoring method for vehicle detection indicators according to claim 1, characterized in that: The step S53 further comprises the steps of: The input vehicle detection index data is judged, and if the vehicle detection index data is less than the mark value, the vehicle score is deemed to be full score; If the vehicle detection index data is greater than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

4. A scoring method for vehicle detection indicators according to claim 1, characterized in that: The step S1 further comprises the steps of: The vehicle information data is extracted using the vehicle VIN code.

5. A vehicle detection index scoring terminal, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed: S1. Obtaining vehicle detection index data and vehicle information data with unimodal distribution; the vehicle information data includes vehicle battery type, manufacturer, model and brand data; S2, grouping the vehicle detection index data according to a classification standard selected from the vehicle information data and obtaining grouped data; S3, performing distribution fitting calculation on each of the grouped data using t distribution; S4, obtaining a probability density function of each of the grouped data according to the distribution fitting calculation result and recording the vehicle detection index data value corresponding to the highest point in the probability density function as a mark value; S5, analyzing each of the vehicle detection index data, if the corresponding score shows an increasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score by taking the marked value as the full score point and combining it with the cumulative distribution function; if the corresponding score shows a decreasing trend when the vehicle detection index data value increases, the vehicle detection index data is converted into a vehicle score by taking the marked value as the full score point and combining it with the residual distribution function; The step S5 specifically comprises the following steps: S51, analyzing each of the vehicle detection index data, if the vehicle detection index data value is positively correlated with the score, proceeding to step S52; if the vehicle detection index data value is negatively correlated with the score, proceeding to step S53; S52. Combined with the cumulative distribution function, the score at the cumulative probability corresponding to the mark value is 100 points, and the score at 0.1 times the cumulative probability corresponding to the mark value is 50 points. Substitute them into the vehicle score calculation formula to calculate parameters a and b. The formula is as follows: ； In the formula, represents the vehicle score; p represents the probability corresponding to the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle detection index data is converted into a vehicle score using the vehicle score calculation formula; S53. Combine the residual distribution function, take the score at the residual probability corresponding to the mark value as 100 points, take the score at 0.1 times the residual probability corresponding to the mark value as 50 points, substitute into the vehicle score calculation formula to calculate the parameters a and b, the formula is as follows: ； In the formula, represents the vehicle score; p represents the probability corresponding to the vehicle detection index data; a and b represent the parameters calculated according to the relationship between the mark value and the full score; The vehicle detection index data is converted into a vehicle score using the vehicle score calculation formula.

6. A vehicle detection index scoring terminal according to claim 5, characterized in that: The step S52 further comprises the steps of: The input vehicle detection index data is judged, and if the vehicle detection index data is greater than the mark value, the vehicle score is deemed to be full score; If the vehicle detection index data is less than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

7. The vehicle detection index scoring terminal according to claim 5, characterized in that: The step S53 further comprises the steps of: The input vehicle detection index data is judged, and if the vehicle detection index data is less than the mark value, the vehicle score is deemed to be full score; If the vehicle detection index data is greater than or equal to the mark value, the vehicle score calculation formula is used to calculate the actual vehicle score. If the detected vehicle score is a negative value, the vehicle score is deemed to be zero.

8. The vehicle detection index scoring terminal according to claim 5, characterized in that: The step S1 further comprises the steps of: The vehicle information data is extracted using the vehicle VIN code.

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