Smart park big data platform management system based on data analysis

Abstract

The invention relates to the technical field of smart park big data platform management, and particularly discloses a smart park big data platform management system based on data analysis, which comprises a response sensitivity analysis module, a braking performance analysis module, an oil consumption anomaly analysis module, a database and an enterprise production quality evaluation module. According to the method, the response sensitivity, the brake performance index and the fuel consumption abnormal index corresponding to each tested automobile are analyzed, and the comprehensive performance index corresponding to each tested automobile is calculated, so that the enterprise production quality evaluation index corresponding to each automobile production enterprise in the target automobile smart park is analyzed, the reliability and the consistency of the test result are ensured, and the test efficiency is improved. The comprehensive performance analysis comprehensiveness corresponding to the tested automobile is improved, the overall production level and technical strength of enterprises can be better reflected, a basis is provided for comparison and competition between the enterprises, and benign competition and technical progress between the automobile production enterprises can be promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of smart park big data platform management, and in particular to a smart park big data platform management system based on data analysis. Background Art

[0002] With the rapid development of the automobile industry, the Automobile Smart Park, as an important platform for displaying advanced manufacturing technology and intelligent management, has a vital task to evaluate the production quality of various automobile manufacturers. This involves comprehensive consideration of multiple dimensions such as production process, product quality, technological innovation capability, and after-sales service. In order to more accurately evaluate the production quality of each automobile manufacturer, it is necessary to evaluate the production quality of each automobile manufacturer in the Automobile Smart Park.

[0003] The existing production quality assessment methods for each automobile manufacturer in the automobile smart park still have the following problems: 1. At the level of brake sensitivity analysis, the brake sensitivity is not currently analyzed in combination with the braking distance, and only the brake reaction time parameter is considered, which reduces the comprehensiveness of the brake sensitivity analysis corresponding to the test vehicle, and thus cannot ensure the accuracy of the response sensitivity analysis corresponding to the test vehicle, and cannot ensure the driving safety of the driver.

[0004] 2. At the level of brake deviation analysis, currently only a rough observation is made to see whether the test vehicle is traveling along the prescribed path. The error between the actual driving trajectory and the test trajectory is not analyzed, and the reliability and consistency of the test results cannot be determined, which affects the assessment of vehicle safety and makes it difficult to propose effective optimization and improvement measures. At the same time, it limits the improvement of brake system performance and vehicle safety, and is not conducive to the standardization and normalization of braking performance. Summary of the invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a smart park big data platform management system based on data analysis is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a smart park big data platform management system based on data analysis, including: a response sensitivity analysis module, which is used to perform unmanned driving performance tests on each test car in the target automobile smart park, extract the type of each test car, collect the time points corresponding to each test car when it receives a starting signal and each start is successful, and collect the time points corresponding to each test car when it receives a braking signal and each complete stop, as well as the braking distance corresponding to each braking, and analyze the response sensitivity corresponding to each test car.

[0007] The braking performance analysis module is used to extract the test trajectory and estimated completion time corresponding to each test vehicle in each braking test, collect the actual driving trajectory and actual driving time corresponding to each test vehicle in each braking test, and collect the turning diameter corresponding to each turning point in each braking test of each test vehicle, and analyze the braking performance index corresponding to each test vehicle.

[0008] The fuel consumption abnormality analysis module is used to collect the fuel tank oil volume corresponding to each test car before and after each fuel consumption test, and analyze the fuel consumption abnormality index corresponding to each test car.

[0009] The database is used to store the standard starting reaction time, standard braking reaction time and standard braking distance corresponding to each type of test car, and store the corresponding fuel consumption of each type of test car in each historical fuel consumption test.

[0010] The enterprise production quality assessment module is used to calculate the comprehensive performance index corresponding to each test car, extract the test cars produced by each automobile manufacturer in the target automobile smart park, and analyze the enterprise production quality assessment index corresponding to each automobile manufacturer in the target automobile smart park.

[0011] Specifically, the response sensitivity corresponding to each test vehicle is analyzed, and the specific analysis process is as follows: A1, based on the time points corresponding to each test vehicle receiving the start signal and each successful start, calculate the start sensitivity β corresponding to each test vehicle i , where i represents the number of the test car, i=1, 2, ..., n.

[0012] A2. Calculate the braking sensitivity x of each test vehicle based on the time points corresponding to each time the test vehicle receives the brake signal and each time it stops completely, as well as the braking distance corresponding to each braking. i .

[0013] A3. Calculate the response sensitivity δ corresponding to each test vehicle i , Among them, β′ and χ′ represent the starting sensitivity and braking sensitivity of the setting reference respectively, a 1 and a 2 They respectively represent the weights of the response sensitivity evaluation corresponding to the set starting sensitivity and braking sensitivity.

[0014] Specifically, the starting sensitivity corresponding to each test vehicle is calculated as follows: B1. Subtract the time points corresponding to each time when each test vehicle receives a starting signal and each time when each vehicle starts successfully, to obtain the starting reaction time corresponding to each test vehicle at each start, and record it as T ij, where j represents the number of each start, j = 1, 2, ..., m.

[0015] B2. Extract the standard starting reaction time corresponding to each type of test car from the database, and match the standard starting reaction time corresponding to each test car according to the type of each test car, which is recorded as

[0016] B3. Calculate the starting sensitivity β corresponding to each test vehicle i , Wherein, m represents the number of starts.

[0017] Specifically, the calculation process of calculating the brake sensitivity corresponding to each test car is as follows: C1. Based on the time points corresponding to each time when each test car receives a brake signal and each time when it completely stops, calculate the reasonableness of the brake reaction time corresponding to each test car.

[0018] C2. The braking distance corresponding to each braking of each test car is recorded as L ig , where g represents the number of each brake, g = 1, 2, ..., r.

[0019] C3. Extract the standard braking distance corresponding to each type of test car from the database, and match the standard braking distance corresponding to each test car according to the type of each test car, recorded as

[0020] C4. Calculate the reasonableness of the braking distance corresponding to each test car θ i , Where r represents the number of braking times.

[0021] C5. Calculate the brake sensitivity X corresponding to each test car i , in, and θ′ respectively represent the reasonableness of the braking reaction time and braking distance of the reference setting, 3 and a 4 They respectively represent the weights of the braking sensitivity evaluation corresponding to the set rationality of the braking reaction time and the rationality of the braking distance.

[0022] Specifically, the braking performance index corresponding to each test vehicle is analyzed, and the specific analysis process is as follows: D1. Based on the actual driving trajectory and actual driving duration corresponding to each test vehicle in each braking test, the braking deviation index ρ corresponding to each test vehicle is calculated. i .

[0023] D2. Calculate the braking steering flexibility φ of each test vehicle based on the turning diameter of each turning point in each braking test.i .

[0024] D3. Calculate the braking performance index corresponding to each test vehicle Among them, ρ′ and φ′ represent the reference braking deviation index and braking steering flexibility, respectively, a 5 and a 6 They respectively represent the weights of the set braking deviation index and braking steering flexibility corresponding to the braking performance evaluation, and e represents a natural constant.

[0025] Specifically, the braking deviation index corresponding to each test vehicle is calculated, and the specific calculation process is as follows: E1, the test trajectory corresponding to each test vehicle in each braking test is overlapped and compared with the actual driving trajectory, and the overlapping trajectory length corresponding to each test vehicle in each braking test is obtained, which is recorded as Wherein, q represents the number of the braking test, q=1, 2, ..., z.

[0026] E2. Locate the length of the test track from the test track corresponding to each test vehicle in each braking test, and record it as

[0027] E3. The estimated completion time and actual driving time of each test vehicle in each braking test are recorded as and

[0028] E4. Calculate the braking deviation index ρ corresponding to each test vehicle i , Among them, K 1 and △T represent the overlapped trajectory length ratio and completion time deviation of the set reference, respectively. 7 and a 8 They respectively represent the weights of the braking deviation assessment corresponding to the set overlapping trajectory length ratio and completion time deviation, and z represents the number of braking tests.

[0029] Specifically, the analysis of the fuel consumption anomaly index corresponding to each test car is carried out as follows: F1. The corresponding fuel consumption of each type of test car in each historical fuel consumption test is extracted from the database, and according to the type of each test car, the corresponding fuel consumption of each test car in each historical fuel consumption test is matched, and the average is calculated to obtain the historical average fuel consumption of each test car in the fuel consumption test.

[0030] F2. Subtract the fuel tank oil volume of each test vehicle before and after each fuel consumption test to obtain the fuel consumption volume of each test vehicle in each fuel consumption test, and compare it with the historical average fuel consumption volume of each test vehicle in the fuel consumption test. If the fuel consumption volume of a test vehicle in a fuel consumption test is greater than the historical average fuel consumption volume, then the fuel consumption test is recorded as an abnormal fuel consumption test. The number of abnormal fuel consumption tests of each test vehicle is counted and recorded as τ. i .

[0031] F3. Extract the maximum value from the corresponding fuel consumption of each test vehicle in each fuel consumption test, denoted as σ i .

[0032] F4. Calculate the fuel consumption abnormality index ζ corresponding to each test vehicle i , Among them, τ′ and σ′ represent the number of abnormal fuel consumption tests and the amount of fuel consumed, respectively, b 1 and b 2 They respectively represent the weights of the abnormal fuel consumption assessment corresponding to the set number of abnormal fuel consumption tests and the amount of fuel consumed.

[0033] Specifically, the calculation formula of the comprehensive performance index corresponding to each test vehicle is: Among them, ψ i represents the comprehensive performance index corresponding to the i-th test car, b 3 , b 4 and b 5 They respectively represent the weights of the set response sensitivity, braking performance index and fuel consumption abnormality index corresponding to the comprehensive performance evaluation.

[0034] Specifically, the enterprise production quality evaluation index corresponding to each automobile manufacturer in the target automobile smart park is analyzed, and the specific analysis process is as follows: G1. Based on each test automobile produced by each automobile manufacturer in the target automobile smart park, the comprehensive performance index corresponding to each test automobile produced by each automobile manufacturer is obtained, and the average is calculated to obtain the average comprehensive performance index of the test automobiles produced by each automobile manufacturer, which is recorded as Wherein, f represents the number of the automobile manufacturer, f=1, 2, ..., 1.

[0035] G2. Compare the comprehensive performance index of each test vehicle produced by each automobile manufacturer with the comprehensive performance index of the set reference, count the number of test vehicles of each automobile manufacturer whose comprehensive performance index is greater than or equal to the comprehensive performance index of the set reference, and record it as the number of qualified vehicles, denoted as μ f .

[0036] G3. Calculate the enterprise production quality evaluation index ω corresponding to each automobile manufacturer in the target automobile smart park f , Where, ψ′ and μ′ represent the comprehensive performance index and the number of qualified cars of the reference respectively, b 6 and b 7 They respectively represent the weight of the set comprehensive performance index and the number of qualified cars corresponding to the enterprise's production quality assessment.

[0037] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention calculates the comprehensive performance index corresponding to each test vehicle by analyzing the response sensitivity, braking performance index and fuel consumption abnormality index corresponding to each test vehicle, thereby analyzing the enterprise production quality evaluation index corresponding to each automobile manufacturing enterprise in the target automobile smart park. This analysis is not affected by a single indicator and can better reflect the overall production level and technical strength of the enterprise. At the same time, it provides a basis for comparison and competition between enterprises, which helps to promote healthy competition and technological progress among automobile manufacturing enterprises.

[0038] (2) The present invention calculates the rationality of the braking reaction time and the rationality of the braking distance of each test car by combining the time points corresponding to each time the test car receives the brake signal and each time it stops completely, as well as the braking distance corresponding to each brake of each test car, thereby analyzing the braking sensitivity corresponding to each test car, improving the comprehensiveness of the braking sensitivity analysis corresponding to the test car, thereby ensuring the accuracy of the response sensitivity analysis corresponding to the test car and ensuring the driving safety of the driver.

[0039] (3) The present invention analyzes the braking deviation index corresponding to each test vehicle by combining the overlapping trajectory length between the test trajectory corresponding to each test vehicle in each braking test and the actual driving trajectory, thereby ensuring the reliability and consistency of the test results, thereby providing data support for the proposal of effective optimization and improvement measures, and at the same time helping to improve the performance of the braking system and the safety of the vehicle, and facilitating the standardization and normalization of braking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 It is a schematic diagram of the system module structure connection of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] See also Figure 1 As shown, the present invention provides a smart park big data platform management system based on data analysis, including: a response sensitivity analysis module, a braking performance analysis module, a fuel consumption abnormality analysis module, a database and an enterprise production quality assessment module.

[0044] The response sensitivity analysis module, the braking performance analysis module and the fuel consumption abnormality analysis module are all connected to the enterprise production quality assessment module, the response sensitivity analysis module is connected to the fuel consumption abnormality analysis module, and both the response sensitivity analysis module and the fuel consumption abnormality analysis module are connected to the database.

[0045] The response sensitivity analysis module is used to perform unmanned driving performance tests on each test car in the target automobile smart park, extract the type of each test car, collect the time points corresponding to each time the test car receives a start signal and each time the start is successful, and collect the time points corresponding to each time the test car receives a brake signal and each time it stops completely, as well as the braking distance corresponding to each braking, and analyze the response sensitivity corresponding to each test car.

[0046] It should be noted that the type of each test car is extracted from the test management background corresponding to the target automobile smart park. The method of collecting the time points corresponding to each time the test car receives the starting signal and each time the start is successful is: a signal receiving device is installed on each test car, and when a unified starting signal is issued in the test site, the signal receiving device will immediately record the current timestamp as the time point of receiving the starting signal, and a speed sensor is installed on each test car. When the speed of each test car reaches the preset speed corresponding to the successful start, the system will record the current timestamp as the time point of the successful start.

[0047] It should be noted that the method for collecting the time points corresponding to each time the test cars receive brake signals and each time they come to a complete stop is as follows: in the test environment, a unified brake signal transmitting device is set up. When the test car needs to brake, the device will send a brake signal. The test car is equipped with a corresponding signal receiving device, which can receive and record the reception time of the brake signal in real time. The speed sensor installed on the test car will continuously monitor the movement state of the vehicle. When the speed of the test car drops to zero, the sensor will immediately feedback this information. The data collected by the sensor will be transmitted to the data acquisition system in real time. The system will analyze and process these data. When it is confirmed that the test car has come to a complete stop, the current timestamp will be recorded as the time point of complete stop.

[0048] It should also be noted that the braking distance corresponding to each braking is collected in the following way: a laser rangefinder is installed on each test vehicle. These devices can measure the distance between the vehicle and a fixed point in real time. During the braking process, the device will continuously record the change in the distance between the vehicle and the reference point, thereby deriving the braking distance.

[0049] In a specific embodiment of the present invention, the response sensitivity corresponding to each test vehicle is analyzed, and the specific analysis process is as follows: A1, based on the time points corresponding to each time when each test vehicle receives a start signal and each time when the start is successful, calculate the start sensitivity β corresponding to each test vehicle i , where i represents the number of the test car, i=1, 2, ..., n.

[0050] In a specific embodiment of the present invention, the starting sensitivity corresponding to each test vehicle is calculated, and the specific calculation process is as follows: B1, subtracting the time points corresponding to each time the test vehicle receives the starting signal and each time the test vehicle starts successfully, to obtain the starting reaction time corresponding to each start of each test vehicle, and record it as T ij , where j represents the number of each start, j = 1, 2…m.

[0051] B2. Extract the standard starting reaction time corresponding to each type of test car from the database, and match the standard starting reaction time corresponding to each test car according to the type of each test car, which is recorded as

[0052] B3. Calculate the starting sensitivity β corresponding to each test vehicle i , Wherein, m represents the number of starts.

[0053] A2. Calculate the braking sensitivity X of each test vehicle based on the time points corresponding to each time the test vehicle receives the brake signal and each time it stops completely, as well as the braking distance corresponding to each braking. i .

[0054] In a specific embodiment of the present invention, the specific calculation process of calculating the brake sensitivity corresponding to each test car is as follows: C1. Based on the time points corresponding to each time the test car receives the brake signal and each time it stops completely, the reasonableness of the brake reaction time corresponding to each test car is calculated.

[0055] It should be noted that the specific calculation process of calculating the rationality of the braking reaction time corresponding to each test car is as follows: H1, subtracting the time points corresponding to each time the test car receives the brake signal and each time it stops completely, to obtain the braking reaction time corresponding to each test car at each braking, and record it as Wherein, S represents the number of each brake, s=1, 2, ..., x.

[0056] H2. Extract the standard braking reaction time corresponding to each type of test car from the database, and match the standard braking reaction time corresponding to each test car according to the type of each test car, recorded as

[0057] H3. Calculate the reasonableness of the braking reaction time corresponding to each test car Where x represents the number of brakes.

[0058] C2. The braking distance corresponding to each braking of each test car is recorded as L ig , where g represents the number of each brake, g = 1, 2, ..., r.

[0059] C3. Extract the standard braking distance corresponding to each type of test car from the database, and match the standard braking distance corresponding to each test car according to the type of each test car, recorded as

[0060] C4. Calculate the reasonableness of the braking distance corresponding to each test car θ i , Where r represents the number of braking times.

[0061] C5. Calculate the brake sensitivity X corresponding to each test car i , in, and θ′ respectively represent the reasonableness of the braking reaction time and braking distance of the reference setting, 3 and a 4 They respectively represent the weights of the braking sensitivity evaluation corresponding to the set rationality of the braking reaction time and the rationality of the braking distance.

[0062] In a specific embodiment of the present invention, a 3 The setting value is 50%, a 4The setting value is 50%. In emergency braking situations, a shorter braking reaction time can help the vehicle decelerate faster, thereby shortening the braking distance and reducing potential accident risks. In actual driving, a shorter braking distance can help the driver stop faster in an emergency, thereby avoiding or reducing accident losses. Therefore, when analyzing the braking sensitivity of each test car, the rationality of the braking reaction time and the rationality of the braking distance are equally important parameters.

[0063] The embodiment of the present invention calculates the rationality of the braking reaction time and the rationality of the braking distance corresponding to each test car by combining the time points corresponding to each time each test car receives a brake signal and each time it completely stops, as well as the braking distance corresponding to each brake of each test car, thereby analyzing the braking sensitivity corresponding to each test car, improving the comprehensiveness of the braking sensitivity analysis corresponding to the test car, thereby ensuring the accuracy of the response sensitivity analysis corresponding to the test car and ensuring the driving safety of the driver.

[0064] A3. Calculate the response sensitivity δ corresponding to each test vehicle i , Among them, β′ and X′ represent the starting sensitivity and braking sensitivity of the setting reference respectively, a 1 and a 2 They respectively represent the weights of the response sensitivity evaluation corresponding to the set starting sensitivity and braking sensitivity.

[0065] In a specific embodiment of the present invention, a 1 The setting value is 50%, a 2 The setting value is 50%. High starting sensitivity means that the vehicle can respond quickly and smoothly to the driver's starting command, which is important for improving driving experience and daily driving safety. High braking sensitivity can ensure that the vehicle can decelerate quickly in an emergency, thereby shortening the braking distance and improving driving safety. Therefore, when analyzing the corresponding response sensitivity of each test car, starting sensitivity and braking sensitivity are equally critical performance indicators.

[0066] The braking performance analysis module is used to extract the test trajectory and estimated completion time corresponding to each test vehicle in each braking test, collect the actual driving trajectory and actual driving time corresponding to each test vehicle in each braking test, and collect the turning diameter corresponding to each turning point of each test vehicle in each braking test, and analyze the braking performance index corresponding to each test vehicle.

[0067] It should be noted that the test trajectories and estimated completion times corresponding to each test car in each braking test are extracted from the test management background corresponding to the target automobile smart park. The actual driving trajectories corresponding to each test car in each braking test are captured by setting high-definition cameras on the test site to capture the motion trajectories of the test cars in the braking tests. Subsequently, the motion trajectories of the test cars can be extracted using image processing technology. The actual driving times corresponding to each test car in each braking test are collected by time sensors installed in each test car.

[0068] It should also be noted that the turning diameter corresponding to each turning point in each braking test of each test car is collected in the following manner: the trajectory of the front wheels of each test car during the turning process corresponding to each turning point in each braking test can be measured by a steering tester installed on the front wheels of each test car. When each test car turns, the tire will form a trajectory circle on the ground, and the diameter of the trajectory circle is the turning diameter of each test car. The image corresponding to the trajectory circle is extracted and uploaded to the image processing software, and the turning diameter of each test car is measured by the built-in measuring tool.

[0069] In a specific embodiment of the present invention, the analysis of the braking performance index corresponding to each test vehicle includes the following specific analysis process: D1, based on the actual driving trajectory and actual driving duration corresponding to each test vehicle in each braking test, calculating the braking deviation index ρ corresponding to each test vehicle i .

[0070] In a specific embodiment of the present invention, the specific calculation process of calculating the braking deviation index corresponding to each test vehicle is as follows: E1, overlapping and comparing the test trajectory corresponding to each test vehicle in each braking test with the actual driving trajectory, and obtaining the overlapping trajectory length corresponding to each test vehicle in each braking test, recorded as Wherein, q represents the number of the braking test, q=1, 2, ..., z.

[0071] E2. Locate the length of the test track from the test track corresponding to each test vehicle in each braking test, and record it as

[0072] E3. The estimated completion time and actual driving time of each test vehicle in each braking test are recorded as and

[0073] E4. Calculate the braking deviation index ρ corresponding to each test vehicle i , Among them, K 1 and △T represent the overlapped trajectory length ratio and completion time deviation of the set reference, respectively.7 and a 8 They respectively represent the weights of the braking deviation assessment corresponding to the set overlapping trajectory length ratio and completion time deviation, and z represents the number of braking tests.

[0074] In a specific embodiment of the present invention, a 7 The setting value is 50%, a 8 The setting value is 50%.

[0075] The embodiment of the present invention analyzes the braking deviation index corresponding to each test vehicle by combining the overlapping trajectory length between the test trajectory corresponding to each test vehicle in each braking test and the actual driving trajectory, thereby ensuring the reliability and consistency of the test results, thereby providing data support for the proposal of effective optimization and improvement measures, and at the same time helping to improve the performance of the braking system and the safety of the vehicle, and facilitating the standardization and normalization of braking performance.

[0076] D2. Calculate the braking steering flexibility φ of each test vehicle based on the turning diameter of each turning point in each braking test. i .

[0077] It should be noted that the calculation of the braking steering flexibility corresponding to each test vehicle is specifically carried out as follows: J1. Compare the turning diameter corresponding to each turning point of each test vehicle in each braking test with the turning diameter of the set reference, and count the number of turning points where the turning diameter of each test vehicle in each braking test is smaller than the turning diameter of the set reference, and use it as the number of flexible steering times of each test vehicle in each braking test, recorded as ζ iq .

[0078] J3. Calculate the braking and steering flexibility φ corresponding to each test vehicle i , Wherein, ζ′ represents the number of flexible turns set as a reference.

[0079] It should be noted that the smaller the turning diameter, the stronger the car's ability to pass through narrow curves or bypass insurmountable obstacles, and the better the steering flexibility.

[0080] D3. Calculate the braking performance index corresponding to each test vehicle Among them, ρ′ and φ′ represent the reference braking deviation index and braking steering flexibility, respectively, a 5 and a 6 They respectively represent the weights of the set braking deviation index and braking steering flexibility corresponding to the braking performance evaluation, and e represents a natural constant.

[0081] In a specific embodiment of the present invention, a 5The setting value is 50%, a 6 The setting value is 50%. A lower braking deviation index means that the vehicle is more stable during braking, which is crucial to improving driving safety. A higher braking steering flexibility means that the vehicle can respond more quickly to the driver's steering instructions during braking, which is very important for improving the vehicle's handling and driving experience. Therefore, when analyzing the braking performance index corresponding to each test vehicle, the braking deviation index and braking steering flexibility are equally key performance indicators.

[0082] The fuel consumption abnormality analysis module is used to collect the fuel tank oil volume corresponding to each test car before and after each fuel consumption test, and analyze the fuel consumption abnormality index corresponding to each test car.

[0083] It should be noted that the fuel tank oil levels of each test vehicle before and after each fuel consumption test are collected by a fuel meter of each test vehicle.

[0084] In a specific embodiment of the present invention, the analysis of the fuel consumption anomaly index corresponding to each test car is performed as follows: F1. The corresponding fuel consumption of each type of test car in each historical fuel consumption test is extracted from the database, and according to the type of each test car, the corresponding fuel consumption of each test car in each historical fuel consumption test is matched, and the average is calculated to obtain the historical average fuel consumption of each test car in the fuel consumption test.

[0085] F2. Subtract the fuel tank oil volume of each test vehicle before and after each fuel consumption test to obtain the fuel consumption volume of each test vehicle in each fuel consumption test, and compare it with the historical average fuel consumption volume of each test vehicle in the fuel consumption test. If the fuel consumption volume of a test vehicle in a fuel consumption test is greater than the historical average fuel consumption volume, then the fuel consumption test is recorded as an abnormal fuel consumption test. The number of abnormal fuel consumption tests of each test vehicle is counted and recorded as τ. i .

[0086] F3. Extract the maximum value from the corresponding fuel consumption of each test vehicle in each fuel consumption test, denoted as σ i .

[0087] F4. Calculate the fuel consumption abnormality index ζ corresponding to each test vehicle i , Among them, τ′ and σ′ represent the number of abnormal fuel consumption tests and the amount of fuel consumed, respectively, b 1 and b 2 They respectively represent the weights of the abnormal fuel consumption assessment corresponding to the set number of abnormal fuel consumption tests and the amount of fuel consumed.

[0088] In a specific embodiment of the present invention, b 1 The setting value of b is 50%, 2 The setting value is 50%.

[0089] The database is used to store the standard starting reaction time, standard braking reaction time and standard braking distance corresponding to each type of test car, and store the corresponding fuel consumption of each type of test car in each historical fuel consumption test.

[0090] The enterprise production quality assessment module is used to calculate the comprehensive performance index corresponding to each test car, extract the test cars produced by each automobile manufacturing enterprise in the target automobile smart park, and analyze the enterprise production quality assessment index corresponding to each automobile manufacturing enterprise in the target automobile smart park.

[0091] In a specific embodiment of the present invention, the calculation formula of the comprehensive performance index corresponding to each test vehicle is: Among them, ψ i represents the comprehensive performance index corresponding to the i-th test car, b 3 , b 4 and b 5 They respectively represent the weights of the set response sensitivity, braking performance index and fuel consumption abnormality index corresponding to the comprehensive performance evaluation.

[0092] In a specific embodiment of the present invention, b 3 The setting value of b is 30%, 4 The setting value of b is 40%, 5 The setting value is 30%. The braking performance index is directly related to the safety performance of the vehicle. Excellent braking performance can reduce the risk of accidents. Braking performance is crucial for all driving scenarios, especially highway and mountain driving. Therefore, when analyzing the comprehensive performance index corresponding to each test vehicle, braking performance is more important than response sensitivity and abnormal fuel consumption.

[0093] In a specific embodiment of the present invention, the enterprise production quality evaluation index corresponding to each automobile manufacturer in the target automobile smart park is analyzed, and the specific analysis process is as follows: G1. Based on each test automobile produced by each automobile manufacturer in the target automobile smart park, the comprehensive performance index corresponding to each test automobile produced by each automobile manufacturer is obtained, and the average is calculated to obtain the average comprehensive performance index of the test automobile produced by each automobile manufacturer, which is recorded as Wherein, f represents the number of the automobile manufacturer, f=1, 2, ..., 1.

[0094] G2. Compare the comprehensive performance index of each test vehicle produced by each automobile manufacturer with the comprehensive performance index of the set reference, count the number of test vehicles of each automobile manufacturer whose comprehensive performance index is greater than or equal to the comprehensive performance index of the set reference, and record it as the number of qualified vehicles, denoted as μ f .

[0095] G3. Calculate the enterprise production quality evaluation index ω corresponding to each automobile manufacturer in the target automobile smart park f , Among them, ψ′ and μ′ represent the comprehensive performance index and the number of qualified cars of the reference, respectively. 6 and b 7 They respectively represent the weight of the set comprehensive performance index and the number of qualified cars corresponding to the enterprise's production quality assessment.

[0096] In a specific embodiment of the present invention, b 6 The setting value of b is 50%, 7 The setting value is 50%.

[0097] The embodiment of the present invention calculates the comprehensive performance index corresponding to each test car by analyzing the response sensitivity, braking performance index and fuel consumption abnormality index corresponding to each test car, thereby analyzing the enterprise production quality evaluation index corresponding to each automobile manufacturing enterprise in the target automobile smart park. This analysis is not affected by a single indicator, and can better reflect the overall production level and technical strength of the enterprise. At the same time, it provides a basis for comparison and competition between enterprises, which is helpful to promote healthy competition and technological progress among automobile manufacturers.

[0098] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A smart park big data platform management system based on data analysis, characterized in that: include: The response sensitivity analysis module is used to perform unmanned driving performance tests on each test vehicle in the target automobile smart park, extract the type of each test vehicle, collect the time points corresponding to each time the test vehicle receives a start signal and each time the start is successful, collect the time points corresponding to each time the test vehicle receives a brake signal and each time it stops completely, and the braking distance corresponding to each braking, and analyze the response sensitivity corresponding to each test vehicle; The braking performance analysis module is used to extract the test trajectory and estimated completion time corresponding to each test vehicle in each braking test, collect the actual driving trajectory and actual driving time corresponding to each test vehicle in each braking test, collect the turning diameter corresponding to each turning point in each braking test of each test vehicle, and analyze the braking performance index corresponding to each test vehicle; The fuel consumption abnormality analysis module is used to collect the fuel tank oil volume of each test vehicle before and after each fuel consumption test, and analyze the fuel consumption abnormality index corresponding to each test vehicle; A database for storing the standard starting reaction time, standard braking reaction time and standard braking distance corresponding to each type of test vehicle, and storing the corresponding fuel consumption of each type of test vehicle in each historical fuel consumption test; The enterprise production quality assessment module is used to calculate the comprehensive performance index corresponding to each test car, extract the test cars produced by each automobile manufacturer in the target automobile smart park, and analyze the enterprise production quality assessment index corresponding to each automobile manufacturer in the target automobile smart park.

2. According to claim 1, a smart park big data platform management system based on data analysis is characterized in that: The response sensitivity of each test vehicle is analyzed, and the specific analysis process is as follows: A1. Calculate the starting sensitivity β of each test vehicle based on the time points corresponding to each time the test vehicle receives the starting signal and each time the test vehicle starts successfully. i , where i represents the number of the test car, i=1, 2, ..., n; A2. Calculate the braking sensitivity x of each test vehicle based on the time points corresponding to each time the test vehicle receives the brake signal and each time it stops completely, as well as the braking distance corresponding to each braking. i ; A3. Calculate the response sensitivity δ corresponding to each test vehicle i , Among them, β′ and X′ represent the starting sensitivity and braking sensitivity of the set reference, respectively, and a1 and a2 represent the weights of the response sensitivity evaluation corresponding to the set starting sensitivity and braking sensitivity, respectively.

3. According to claim 2, a smart park big data platform management system based on data analysis is characterized in that: The specific calculation process of calculating the starting sensitivity corresponding to each test vehicle is as follows: B1. Subtract the time points corresponding to each test vehicle receiving the start signal and each successful start to obtain the corresponding start reaction time of each test vehicle at each start, and record it as T ij , where j represents the number of each start, j = 1, 2, ..., m; B2. Extract the standard starting reaction time corresponding to each type of test car from the database, and match the standard starting reaction time corresponding to each test car according to the type of each test car, which is recorded as B3. Calculate the starting sensitivity β corresponding to each test vehicle. Wherein, m represents the number of starts.

4. According to claim 2, a smart park big data platform management system based on data analysis is characterized in that: The specific calculation process of calculating the brake sensitivity corresponding to each test vehicle is as follows: C1. Calculate the reasonableness of the braking reaction time of each test vehicle based on the time points corresponding to each time the test vehicle receives the brake signal and each time it stops completely. C2. The braking distance corresponding to each braking of each test car is recorded as L ig , where g represents the number of each brake, g = 1, 2, ..., r; C3. Extract the standard braking distance corresponding to each type of test car from the database, and match the standard braking distance corresponding to each test car according to the type of each test car, recorded as C4. Calculate the reasonableness of the braking distance corresponding to each test car Among them, r represents the number of braking times; C5. Calculate the brake sensitivity x corresponding to each test vehicle i , in, and They respectively represent the reasonableness of the braking reaction time and the reasonableness of the braking distance for the set reference, and a3 and a4 respectively represent the weights of the braking sensitivity evaluation corresponding to the reasonableness of the braking reaction time and the reasonableness of the braking distance for the set reference.

5. According to claim 2, a smart park big data platform management system based on data analysis is characterized in that: The specific analysis process of analyzing the braking performance index corresponding to each test vehicle is as follows: D1. Calculate the braking deviation index ρ of each test vehicle based on the actual driving trajectory and actual driving duration of each test vehicle in each braking test. i ; D2. Calculate the braking steering flexibility φ of each test vehicle based on the turning diameter of each turning point in each braking test. i ; D3. Calculate the braking performance index corresponding to each test vehicle Among them, ρ′ and φ′ represent the set reference braking deviation index and braking steering flexibility respectively, a5 and a6 represent the set braking deviation index and braking steering flexibility corresponding to the braking performance evaluation weights respectively, and e represents a natural constant.

6. According to claim 5, a smart park big data platform management system based on data analysis is characterized in that: The specific calculation process of calculating the braking deviation index corresponding to each test vehicle is as follows: E1. Overlap and compare the test track corresponding to each test vehicle in each braking test with the actual driving track to obtain the length of the overlapping track corresponding to each test vehicle in each braking test, recorded as Wherein, q represents the number of the braking test, q = 1, 2, ..., z: E2. Locate the length of the test track from the test track corresponding to each test vehicle in each braking test, and record it as E3. The estimated completion time and actual driving time of each test vehicle in each braking test are recorded as and E4. Calculate the braking deviation index ρ corresponding to each test vehicle i . Among them, K1 and △T represent the overlapping trajectory length ratio and completion time deviation of the set reference respectively, a7 and a8 represent the braking deviation evaluation ratio weights corresponding to the set overlapping trajectory length ratio and completion time deviation respectively, and z represents the number of braking tests.

7. According to claim 5, a smart park big data platform management system based on data analysis is characterized in that: The specific analysis process of analyzing the abnormal fuel consumption index corresponding to each test vehicle is as follows: F1. Extract the corresponding fuel consumption of each type of test vehicle in each historical fuel consumption test from the database, and match the corresponding fuel consumption of each test vehicle in each historical fuel consumption test according to the type of each test vehicle, and calculate the average value to obtain the historical average fuel consumption of each test vehicle in the fuel consumption test; F2. Subtract the fuel tank oil volume of each test vehicle before and after each fuel consumption test to obtain the fuel consumption volume of each test vehicle in each fuel consumption test, and compare it with the historical average fuel consumption volume of each test vehicle in the fuel consumption test. If the fuel consumption volume of a test vehicle in a fuel consumption test is greater than the historical average fuel consumption volume, then the fuel consumption test is recorded as an abnormal fuel consumption test. The number of abnormal fuel consumption tests of each test vehicle is counted and recorded as τ. i ; F3. Extract the maximum value from the corresponding fuel consumption of each test vehicle in each fuel consumption test, denoted as σ i ; F4. Calculate the fuel consumption abnormality index ξ corresponding to each test vehicle i , Among them, τ′ and σ′ represent the set reference abnormal fuel consumption test times and fuel consumption respectively, and b1 and b2 represent the set abnormal fuel consumption test times and fuel consumption corresponding to the abnormal fuel consumption assessment weights respectively.

8. According to claim 7, a smart park big data platform management system based on data analysis is characterized in that: The calculation formula of the comprehensive performance index corresponding to each test vehicle is: Among them, ψ i represents the comprehensive performance index corresponding to the i-th test vehicle, b3, b4 and b5 represent the weights of the set response sensitivity, braking performance index and fuel consumption abnormality index corresponding to the comprehensive performance evaluation, respectively.

9. The smart park big data platform management system based on data analysis according to claim 8 is characterized by: The specific analysis process of the enterprise production quality evaluation index corresponding to each automobile manufacturing enterprise in the target automobile smart park is as follows: G1. Based on the test cars produced by each automobile manufacturer in the target automobile smart park, the comprehensive performance index of each test car produced by each automobile manufacturer is obtained, and the average is calculated to obtain the average comprehensive performance index of the test cars produced by each automobile manufacturer. Wherein, f represents the number of the automobile manufacturer, f = 1, 2, ..., 1; G2. Compare the comprehensive performance index of each test vehicle produced by each automobile manufacturer with the comprehensive performance index of the set reference, count the number of test vehicles of each automobile manufacturer whose comprehensive performance index is greater than or equal to the comprehensive performance index of the set reference, and record it as the number of qualified vehicles, denoted as μ f ; G3. Calculate the enterprise production quality evaluation index ω corresponding to each automobile manufacturer in the target automobile smart park f , Among them, ψ′ and μ′ represent the set reference comprehensive performance index and the number of qualified cars respectively, b6 and b7 represent the weights of the set comprehensive performance index and the number of qualified cars corresponding to the enterprise production quality assessment respectively.

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