Vehicle information management system based on big data

Abstract

The present invention discloses a vehicle information management system based on big data, and specifically relates to the technical field of data cleaning and preprocessing of big data. The vehicle information management system comprises the following modules: an information collection module, which collects information of common passenger cars on the market and passenger cars in a vehicle information management system and information of users; a data calculation module, which calculates the safety assessment coefficient of a vehicle by establishing a formula and obtains safety information in vehicle characteristic information; a characteristic value calculation module, which is used to calculate the characteristic value of the vehicle characteristic information; a characteristic value matching module, which converts the characteristic value information of the user into a characteristic value, converts the characteristic value of the vehicle and the user into a characteristic vector, and calculates the difference value of two characteristic vectors through a formula, and selects a vehicle with a small difference value. The present invention is conducive to reducing the speed of information processing by the system background, and at the same time comprehensively combines multiple factors to meet the needs of users.

Description

Technical Field

[0001] The present invention relates to the technical field of data cleaning and preprocessing of big data, and more specifically, to a vehicle information management system based on big data. Background Art

[0002] A vehicle information management system based on big data is an analysis and storage system for the data generated by the vehicle while it is driving or stationary based on the basic information of the passenger car and computer networking. The vehicle is dispatched based on the generated data and the needs of the user, so that the vehicle operating company can match the needs of the user through the collection and analysis of passenger car information. This system is suitable for common types of passenger cars other than pure electric vehicles, such as sedans, SUVs, MPVs, etc.

[0003] Vehicle information management systems usually include information storage, detection, and matching, among which information storage is to store the basic information data of the vehicle, information detection is to transmit the vehicle detection results to the information storage module, and information matching is to call similar vehicle information in the information storage module according to user needs to realize vehicle scheduling.

[0004] The prior art has the following deficiencies:

[0005] In order to better manage vehicles, the vehicle information management system needs to record the basic data of the vehicle, such as license plate number, vehicle identification number, mileage, vehicle status, etc. Vehicle operating companies using the system can easily manage and control vehicles, facilitate vehicle rental and use, and improve operational efficiency. However, in fact, the vehicle information management system provides too much basic information and cannot quickly provide effective information. Users cannot comprehensively consider various vehicle data. The high degree of repetition of various data cannot reasonably screen out the vehicles needed by users, which increases the speed of background information processing. Secondly, existing vehicle information management systems usually lose the investigation of vehicle safety, and the system cannot comprehensively consider various factors to meet users' needs for vehicles. However, users will focus on vehicle safety issues and will meet their own needs based on their actual conditions.

[0006] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a vehicle information management system based on big data to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: the vehicle information management system includes an information collection module, a data calculation module, a characteristic value calculation module and a characteristic value matching module, and the modules are signal-connected;

[0009] Information collection module: used to collect information about passenger cars commonly used in the market and passenger car information in vehicle information management systems and user demand information, and obtain characteristic information of vehicles and users, including a vehicle information collection unit and a user demand information collection unit;

[0010] Data calculation module: used to calculate the safety assessment coefficient of the vehicle in the vehicle information management system by establishing a formula to obtain the safety information in the vehicle characteristic information, including a parameter calculation unit and a safety assessment coefficient unit;

[0011] Characteristic value calculation module: used to calculate the characteristic value of vehicle characteristic information, including a fuel efficiency calculation unit, a passenger capacity calculation unit, a cargo capacity calculation unit and a safety calculation unit;

[0012] Feature value matching module: used to convert the user's feature information into feature values, and convert the vehicle and user's feature values ​​into feature vectors, calculate the difference between the vehicle and user feature vectors through a formula, and select the vehicle with the smallest difference value, which includes a vehicle feature value vector unit, a user feature value vector unit, and a difference value calculation and sorting unit.

[0013] In a preferred embodiment, the data calculation module calculates the safety assessment coefficient through a formula, which specifically includes the following steps:

[0014] The data calculation module obtains the safety information in the vehicle characteristic information. The parameters used include the braking capacity coefficient, the newness coefficient and the risk index coefficient. The parameters used are used to establish the safety assessment coefficient through the formula. The expression is: In the formula, is the safety assessment coefficient of the vehicle, L is the braking capacity coefficient of the vehicle, is the vehicle's newness coefficient, is the risk index coefficient of the vehicle, , , They are respectively the proportional coefficients of the vehicle's braking capacity coefficient, the vehicle's newness coefficient, and the vehicle's risk index coefficient.

[0015] In a preferred embodiment, the vehicle information management system based on big data is characterized in that the calculation expression of the vehicle's newness coefficient is: ;in, The newness rate calculated based on the vehicle's mileage. is the newness rate calculated based on the vehicle’s service life. The newness rate calculated based on the vehicle’s service life is expressed as: 00%； in, is the age of the vehicle in use, The vehicle's useful life and the newness rate calculated based on the vehicle's mileage ;in, is the rated mileage of the vehicle, The mileage used by the vehicle;

[0016] The vehicle information management system based on big data is characterized in that the calculation expression of the risk index coefficient is: ;in, The accident history of the vehicle. The length of time since the vehicle's last maintenance record;

[0017] The vehicle information management system based on big data is characterized in that: the calculation expression of the braking capacity coefficient is: ; Where z is the displacement distance of the vehicle under emergency braking, It is the deviation angle that occurs when the vehicle brakes. When the deviation angle is less than 1 degree or no deviation occurs, Substitute 1 into the formula for the constant.

[0018] In a preferred embodiment, the eigenvalue calculation module calculates the eigenvalues ​​of the feature information respectively by formulas, and the eigenvalue general expression is: In the formula, in the formula, is the characteristic value of a certain vehicle’s characteristic information, K is the characteristic information of a certain vehicle, This is the worst value of the common passenger car characteristic information on the market. It is the optimal value of the characteristic information of common passenger cars on the market.

[0019] In a preferred embodiment, the feature value matching module uses the Euclidean distance to calculate the difference value, which is expressed as: In the formula, is the difference between vector A and vector B, A is the vehicle feature vector, B is the user feature vector, i is the i-th element in the vector, is the i-th element in vector A, is the i-th element in the B vector.

[0020] In a preferred embodiment, the user characteristic value vector unit converts the user's rating of the vehicle characteristic information in the information collection module into a characteristic value ranging from 0 to 1, and the rating specifications are lowest, low, lower, higher, high, and highest. The characteristic values ​​are 0, 0.2, 0.4, 0.6, 0.8, and 1, respectively, and the characteristic values ​​are converted into the form of a spatial vector.

[0021] In a preferred embodiment, the user rates the vehicle rating specification according to a value reference formula, and establishes a vehicle value reference formula, which is expressed as follows: In the formula, is the value of the vehicle, Minimum standard thresholds for vehicle values ​​set for vehicle information management systems, The price increase ratio for different demand ratings, R is a constant, is the additional value of the vehicle brand or function, is the price weight of different vehicle feature information, and j is the jth feature information of the vehicle feature information.

[0022] In the above system modules, the technical effects and advantages of the present invention are as follows:

[0023] The present invention simplifies the basic information of the vehicle into four types of characteristic information, wherein the safety assessment coefficient of the vehicle is calculated by establishing a formula to obtain the safety characteristic information. This method extracts a large amount of information into effective information, reduces the speed of background data processing information, and integrates multiple factors to complete the vehicle safety assessment;

[0024] The present invention calculates the vehicle characteristic information through a formula to obtain the characteristic value of the vehicle characteristic information. The user obtains the user characteristic information according to his own needs in combination with the vehicle value reference formula, and scales the characteristic information of four different scales to the same range to ensure that the influence weights of different characteristic information on the system processing data are relatively balanced. The size of the characteristic value can be used to judge the advantages and disadvantages of the passenger cars in the vehicle management system relative to the common passenger cars on the market in the characteristic information.

[0025] The present invention converts the characteristic values ​​of vehicle and user characteristic information into vector form, calculates the difference value of vehicle and user characteristic vectors through a formula, and selects vehicles with high difference values. This method is conducive to intuitively selecting suitable vehicles, optimizes the calculation steps, and reduces the speed of data processing in the background of the vehicle information management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0027] Figure 1 It is a module diagram of a vehicle information management system based on big data;

[0028] Figure 2 This is a flowchart of the steps of the vehicle information management system based on big data. DETAILED DESCRIPTION

[0029] 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.

[0030] Embodiment 1, a vehicle information management system based on big data, the vehicle information management system includes an information collection module, a data calculation module, a characteristic value calculation module and a characteristic value matching module, the information collection module is used to collect characteristic information of common passenger cars, other information of the vehicle, characteristic information of vehicles required by users and other basic information provided by users, the data calculation module establishes a safety assessment coefficient through formula calculation to obtain safety characteristic information, the characteristic value calculation module is used to calculate the characteristic value of the vehicle characteristic information, and calculates the characteristic values ​​of four characteristic information according to the general expression of the characteristic value, and the characteristic value matching module is used to match the vehicle required by the user according to the actual situation of the user, by converting the vehicle characteristic value and the user characteristic value into characteristic vectors, using the formula to calculate the difference value between the characteristic vectors, sorting the vehicles according to the inverse of the difference value, and selecting the vehicle with the smallest difference value.

[0031] S1: Collect information on common passenger cars in the vehicle management system and on the market and information on user needs;

[0032] The information collection module includes a vehicle information collection unit and a user demand information collection unit. The vehicle information collection unit collects information on common passenger cars on the market and in the vehicle management system, including four vehicle characteristic information of fuel efficiency, passenger capacity, cargo capacity, and safety, as well as other basic information of the vehicle. The user demand information collection unit collects user demand information on the vehicle, including four user characteristic information of fuel efficiency, passenger capacity, cargo capacity, and safety, as well as other basic information provided by the user.

[0033] The vehicle information collection unit collects information data of common passenger cars, and defines that passenger cars with monthly sales exceeding a certain number nationwide are considered common passenger cars. The vehicle's fuel efficiency needs to collect the fuel efficiency of common passenger cars on the market, which is obtained by querying the official website data of the passenger car, and collecting the fuel efficiency of the passenger cars in the vehicle information management system. The fuel consumption of each vehicle per 100 kilometers is actually evaluated under the same conditions. The passenger capacity of the vehicle refers to the number of passengers that the vehicle can reasonably and safely accommodate. The cargo capacity of the vehicle refers to the information on the cargo capacity provided in the vehicle technical specification manual, usually refers to the space size of the vehicle. Safety refers to the safety assessment coefficient provided in the data calculation module. Other basic information of the vehicle is other information in the vehicle information management system except the vehicle characteristic information.

[0034] The characteristic information collected in the user demand information collection unit is the user's rating of four characteristic information: fuel efficiency, passenger capacity, cargo capacity, and safety, which has six rating specifications: lowest, low, relatively low, relatively high, high, and highest.

[0035] Selecting fuel efficiency, passenger capacity, cargo capacity, and safety as characteristic information is to extract effective information about passenger cars in the vehicle information management system. The four characteristic information can meet the user's basic needs for the vehicle, among which fuel efficiency takes into account the user's driving cost, passenger capacity and cargo capacity take into account the user's purpose, and safety takes into account the user's safety requirements.

[0036] By setting characteristic information in the vehicle information management system, effective information is provided to the system, the speed of background information processing is slowed down, and by comparing the four characteristic information, the differences between each vehicle can be judged more clearly and intuitively, and the user's basic needs for the vehicle can be met.

[0037] S2: Calculate the safety assessment coefficient of the vehicle by establishing a formula to obtain vehicle safety feature information;

[0038] The data calculation module includes a parameter calculation unit and a safety assessment coefficient unit. The parameter calculation unit calculates the parameters required for the safety assessment coefficient based on the information provided by the information collection module. The parameters include a braking capacity coefficient, a newness coefficient, and a risk index coefficient. The safety assessment coefficient unit establishes the safety assessment coefficient through a formula calculation based on the parameters provided by the parameter calculation unit, and obtains safety feature information based on the safety assessment coefficient.

[0039] The data calculation module calculates the safety assessment coefficient through a formula, which specifically includes the following steps:

[0040] The parameter calculation unit collects the basic information of the vehicle through the information collection module to calculate the parameters required for the safety assessment coefficient. The parameters are obtained in two states respectively. The risk index coefficient of the vehicle and the newness coefficient of the vehicle are obtained in the vehicle unassessed state, and the braking capacity coefficient of the vehicle is obtained in the vehicle assessment state. The safety assessment coefficient unit uses the formula to establish the safety assessment coefficient. The expression is:

[0041]

[0042] In the formula, is the safety assessment coefficient of the vehicle, L is the braking capacity coefficient of the vehicle, is the vehicle's newness coefficient, is the risk index coefficient of the vehicle, , , They are respectively the proportional coefficients of the vehicle's braking capacity coefficient, the vehicle's newness coefficient, and the vehicle's risk index coefficient.

[0043] The logic for obtaining the newness coefficient is to multiply the newness rate calculated by the vehicle's mileage and the newness rate calculated by the vehicle's years of use. The newness rate calculated by the vehicle's years of use is expressed as: 00%； in, is the age of the vehicle in use, The useful life of the vehicle, the vehicle's newness rate calculated based on mileage ;in, is the rated mileage of the vehicle, is the mileage of the vehicle, and the calculation expression of the vehicle's newness coefficient is: ;in, The newness rate calculated based on the vehicle's mileage. The depreciation rate calculated based on the vehicle’s age.

[0044] The higher the vehicle's renewal rate calculated by age and mileage, the higher the vehicle's renewal rate, which reflects the intensity of vehicle use, the lower the frequency of use, and the lower the storage time, which means the vehicle has less wear and tear and is safer.

[0045] The logic for obtaining the risk index coefficient is the product of the number of accidents that occurred in the vehicle's history plus one and the length of time since the vehicle's most recent maintenance record. The length of time is calculated based on how many unit time intervals it contains. Usually, the unit time interval is 7 days. The calculation expression for the risk index coefficient is: ;in, The accident history of the vehicle. The length of time since the vehicle's last maintenance record.

[0046] The number of accidents in the vehicle's history and the length of time since the vehicle's most recent maintenance record are collected by the information collection module through accident record and maintenance record queries. The higher the frequency of vehicle maintenance and the fewer accidents in history, the better the vehicle's condition and the safer the car. The vehicle maintenance coefficient directly affects the vehicle's performance, reliability and safety.

[0047] The logic for obtaining the braking capacity coefficient is that when the vehicle is driving in a straight line at a constant speed and in an emergency, the reciprocal of the product of the displacement distance of the vehicle from the moving state to the static state and the side slip angle of the vehicle when braking is obtained without touching the steering wheel. The calculation expression of the braking capacity coefficient is: ; Where z is the displacement distance of the vehicle under emergency braking, It is the deviation angle that occurs when the vehicle brakes. When the deviation angle is less than 1 degree or no deviation occurs, Substitute 1 into the formula for the constant.

[0048] When evaluating the braking capacity coefficient of a vehicle, the evaluation environment must be consistent and the speed of the evaluation vehicle must be consistent. A speed of 100km / h is usually selected because there is a sufficiently long displacement distance for comparison between different vehicles and vehicles can often reach this speed on highways. The greater the braking displacement distance and side slip angle, the poorer the braking capacity of the vehicle. When the vehicle encounters danger, the higher the driver's requirements for vehicle control and the poorer the safety of the vehicle.

[0049] Suppose the vehicle management system collects data on a passenger car. The car is 6 years old, has a usable life of 15 years, has a mileage of 60,000 kilometers, has a rated mileage of 600,000 kilometers, has a time interval of 5.2 from the last maintenance, has had 3 accidents, has an emergency braking distance of 41 meters at a speed of 100 km / h, and has a deflection angle of 2 degrees during braking.

[0050] The braking capacity coefficient of the vehicle is: ;

[0051] The car's old and new coefficient is: ;

[0052] The risk index coefficient of the vehicle is .

[0053] The safety assessment coefficient of the vehicle is obtained through the data calculation module. The safety assessment coefficient is closely related to the safety of the vehicle. The higher the safety assessment coefficient of the vehicle, the higher the safety of the vehicle. By establishing the safety assessment coefficient, an abstract concept is transformed into a specific value, which clearly reflects the level of vehicle safety. This invention solves the problem of evaluating vehicle safety. Without collision tests and professional tests, it is difficult to express the safety of the vehicle through data. The invention integrates multiple factors to complete the evaluation of vehicle safety.

[0054] According to the information and data obtained by the information collection module and the data calculation module in Example 1, the eigenvalue calculation module normalizes the obtained information and data to establish a general formula for eigenvalues, and obtains eigenvalue data of the vehicle in the range of 0 to 1. The eigenvalue matching module is used to extract eigenvalues, convert the eigenvalues ​​into the form of spatial vectors, and use the Euclidean distance calculation method based on the user eigenvector and the vehicle eigenvector to obtain the difference between the vehicle and the user eigenvectors. The difference values ​​are sorted and the vehicle with the smallest difference value with the user is selected.

[0055] S3: Calculate the characteristic value of the vehicle characteristic information using the characteristic value general formula according to the information collection module;

[0056] The characteristic value calculation module includes a fuel efficiency calculation unit, a passenger capacity calculation unit, a cargo capacity calculation unit, and a safety calculation unit, wherein the fuel efficiency calculation unit, the passenger capacity calculation unit, and the cargo capacity calculation unit obtain the fuel efficiency characteristic value, the passenger capacity characteristic value, and the cargo capacity characteristic value through the data collected by the information collection module, and the safety calculation unit obtains the safety characteristic value through the safety assessment coefficient calculated by the data calculation module.

[0057] The characteristic information of common passenger cars is collected according to the information collection module, the maximum and minimum values ​​of each characteristic information are extracted, the vehicle characteristic information in the vehicle information management system is collected, and the collected data is substituted into the formula to obtain the characteristic value of each characteristic information.

[0058] Among them, the characteristic value calculation module calculates the fuel efficiency characteristic value, passenger capacity characteristic value, cargo capacity characteristic value, and safety characteristic value of each vehicle through formulas. The general expression of the characteristic value is:

[0059]

[0060] In the formula, is the characteristic value of a certain vehicle’s characteristic information, K is the characteristic information of a certain vehicle, This is the worst value of the common passenger car characteristic information on the market. It is the optimal value of the characteristic information of common passenger cars on the market.

[0061] Assume that the information collection module in the vehicle information management system collects characteristic information of common passenger cars on the market and characteristic information of passenger cars in the system, and the optimal and worst values ​​of the characteristic information are: fuel efficiency is 4.6 and 20, passenger capacity is 9 and 4, cargo capacity is 1130 and 280, and safety is 8 and 2. Substituting the optimal and worst values ​​of the characteristic information of vehicles in the vehicle information management system and the characteristic information of common passenger cars on the market into the general expression of characteristic values, the characteristic value of the characteristic information of each vehicle in the vehicle information management system can be calculated.

[0062] For example, to calculate the characteristic value of passenger car a in the vehicle management system, according to the information collection module, it can be obtained that the fuel efficiency of passenger car a is 9.4, the passenger capacity is 4, the cargo capacity is 425, and the safety is 4.5.

[0063] The fuel efficiency characteristic value of passenger car a is: 0.69;

[0064] The passenger capacity characteristic value of passenger car a is: ;

[0065] The characteristic value of the cargo capacity of passenger car a is: ;

[0066] The safety characteristic value of passenger car a is: .

[0067] Through the eigenvalue processing module, the feature information of four different scales are scaled to the same range respectively to ensure that the influence weights of different feature information on the system processing data are relatively balanced, and the eigenvalue of each feature information is obtained. In addition, the size of the eigenvalue can be used to judge the advantages and disadvantages of the passenger cars in the vehicle management system compared with the common passenger cars on the market in the feature information.

[0068] S4: obtaining a user characteristic value, converting the vehicle and user characteristic values ​​into characteristic vectors, and calculating the difference between the characteristic vectors through a formula, and selecting a vehicle with a small difference value;

[0069] The eigenvalue matching module includes a vehicle eigenvalue vector unit, a user eigenvalue vector unit and a difference value calculation and sorting unit. The vehicle eigenvalue vector unit converts the eigenvalue calculated by the eigenvalue calculation module into a form of a space vector, each eigenvalue corresponds to a dimension, and a vehicle eigenvector is obtained. The user eigenvalue vector unit converts the eigenvalue into a eigenvalue according to the user's rating of the four characteristic information of the vehicle in the information collection module, and converts the eigenvalue into the form of a space vector, each rating of the vehicle characteristic information corresponds to a dimension, and a user characteristic vector is obtained. The difference value calculation and sorting unit calculates the difference value between the eigenvalue vectors by using the Euclidean distance calculation method between vectors, measures the degree of difference between the vehicle and the vehicle required by the user according to the size of the difference value, and sorts according to the size of the difference value to select the vehicle with the smallest difference value.

[0070] The vehicle eigenvalue vector unit converts the eigenvalue into a space vector according to the eigenvalue of the vehicle in the eigenvalue calculation module. For example, the eigenvalues ​​of passenger car a are , , , , then the characteristic vector of passenger car a is [ , , , ].

[0071] The user characteristic value vector unit converts the rating of the vehicle characteristic information by the user in the information collection module into a characteristic value ranging from 0 to 1, and the rating specifications are lowest, low, relatively low, relatively high, high, and highest. The characteristic values ​​are 0, 0.2, 0.4, 0.6, 0.8, and 1 respectively. The characteristic values ​​are converted into the form of spatial vectors. For example, if the user's requirements for vehicle characteristic information are relatively high fuel efficiency, low passenger capacity, low cargo capacity, and high safety, then the customer's characteristic value vector is [0.6, 0.2, 0.2, 0.8].

[0072] The user's rating of vehicle characteristic information is combined with the user's actual situation. The user will adjust the rating of vehicle characteristic information based on his or her own needs and budget and choose a suitable vehicle according to his or her actual situation. The higher the customer's rating of vehicle characteristic information, the higher the price required. The standard threshold of vehicle value is set, usually 100 yuan per day. The value growth rate of different demand ratings is different. According to the lowest, low, relatively low, relatively high, high, and highest ratings, the value growth rate increases in sequence. Usually the unit growth rate is 5%. For each increase in rating, the unit growth rate doubles, and the value weights of different characteristic information are different. Usually the value weights of the four vehicle characteristic information are ranked as safety > cargo capacity > fuel consumption > passenger capacity.

[0073] According to the number and brands of passenger cars in the vehicle information management system, the minimum threshold of vehicle value, the value weights of different vehicle characteristic information, and the value growth ratio of different ratings of vehicle characteristic information are set to establish a reference formula for vehicle value. The expression is:

[0074]

[0075] In the formula, is the value of the vehicle, Minimum standard thresholds for vehicle values ​​set for vehicle information management systems, The price increase ratio for different demand ratings, R is a constant, is the additional value of the vehicle brand or function, is the price weight of different vehicle feature information, and j is the jth feature information of the vehicle feature information.

[0076] In the difference value calculation and sorting unit, the difference between the vehicle feature vector and the user feature vector is measured by the Euclidean distance between the vectors, and the difference value is calculated by the two vectors through the formula, which is expressed as:

[0077]

[0078] In the formula, is the difference between vector A and vector B, A is the vehicle feature vector, B is the user feature vector, i is the i-th element in the vector, is the i-th element in vector A, is the i-th element in the B vector.

[0079] In the vehicle information management system, passenger cars correspond to vehicle feature vectors one by one. The vehicle feature vector and user feature vector of each passenger car are substituted into the expression to obtain the difference between the vehicle feature vector and the user feature vector of each passenger car. The smaller the difference value, the more similar the two feature vectors are, and the more the vehicle meets the needs of the user. The difference values ​​are sorted in reverse order, and the vehicle at the front of the difference value sorting table is selected.

[0080] It is known that the eigenvalues ​​of passenger car a are 0.69, 0, 0.17, and 0.42 respectively. The eigenvalues ​​are converted into vectors in the form of a=[0.69, 0, 0.17, 0.42]. The eigenvector of the user is b=[0.6, 0.2, 0.2, 0.8]. The difference between passenger car a and the user's demand is:

[0081] ; Calculate the difference between all vehicles in the vehicle information management system and user needs, and select the vehicle with the smallest difference.

[0082] The characteristic value matching module converts the difference between the passenger cars in the vehicle information management system and the passenger cars required by the user into difference values, so that the vehicle suitable for the user can be selected more intuitively, reducing the speed of data processing in the background of the vehicle information management system.

[0083] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0084] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0085] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0086] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0091] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0092] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle information management system based on big data, characterized by: The vehicle information management system includes an information collection module, a data calculation module, a characteristic value calculation module and a characteristic value matching module, and the modules are signal-connected; Information collection module: used to collect information about passenger cars commonly used in the market and passenger car information in vehicle information management systems and user demand information, and obtain characteristic information of vehicles and users, including a vehicle information collection unit and a user demand information collection unit; Data calculation module: used to calculate the safety assessment coefficient of the vehicle in the vehicle information management system by establishing a formula to obtain the safety information in the vehicle characteristic information, including a parameter calculation unit and a safety assessment coefficient unit; Characteristic value calculation module: used to calculate the characteristic value of vehicle characteristic information, including a fuel efficiency calculation unit, a passenger capacity calculation unit, a cargo capacity calculation unit and a safety calculation unit; Feature value matching module: used to convert the user's feature information into feature values, and convert the vehicle and user's feature values ​​into feature vectors, calculate the difference between the vehicle and user's feature vectors through a formula, and select the vehicle with the smallest difference value, including a vehicle feature value vector unit, a user feature value vector unit, and a difference value calculation and sorting unit; The data calculation module calculates the safety assessment coefficient through a formula, which specifically includes the following steps: The data calculation module obtains the safety information in the vehicle characteristic information. The parameters used include the braking capacity coefficient, the newness coefficient and the risk index coefficient. The safety assessment coefficient is established through the formula. The expression is: In the formula, is the safety assessment coefficient of the vehicle, L is the braking capacity coefficient of the vehicle, is the vehicle's newness coefficient, is the risk index coefficient of the vehicle, , , They are the proportional coefficients of the vehicle's braking capacity coefficient, the vehicle's newness coefficient, and the vehicle's risk index coefficient; The calculation expression of the newness coefficient of the vehicle is: ;in, The newness rate calculated based on the vehicle's mileage. is the newness rate calculated based on the vehicle’s service life. The newness rate calculated based on the vehicle’s service life is expressed as: 00%； in, is the age of the vehicle in use, The vehicle's useful life and the newness rate calculated based on the vehicle's mileage ;in, is the rated mileage of the vehicle, The mileage used by the vehicle; The calculation expression of the risk index coefficient is: ;in, The accident history of the vehicle. The length of time since the vehicle's last maintenance record; The calculation expression of the braking capacity coefficient is: ; Where z is the displacement distance of the vehicle under emergency braking, It is the deviation angle of the vehicle when braking. When the deviation angle is less than 1 degree or no deviation occurs, Substitute the constant 1 into the formula; The characteristic value calculation module calculates the characteristic values ​​of the characteristic information respectively through the formula, and the characteristic value general expression is: ; In the formula, is the characteristic value of a certain vehicle’s characteristic information, K is the characteristic information of a certain vehicle, This is the worst value of the common passenger car characteristic information on the market. It is the optimal value of the characteristic information of common passenger cars on the market; The feature value matching module uses the Euclidean distance to calculate the difference value, and the expression is: In the formula, is the difference between vector A and vector B, A is the vehicle feature vector, B is the user feature vector, i is the i-th element in the vector, is the i-th element in vector A, is the i-th element in the B vector.

2. The vehicle information management system based on big data according to claim 1, characterized in that: The user characteristic value vector unit converts the user's rating of the vehicle characteristic information in the information collection module into a characteristic value ranging from 0 to 1, and the rating specifications are lowest, low, relatively low, relatively high, high, and highest. The characteristic values ​​are 0, 0.2, 0.4, 0.6, 0.8, and 1 respectively, and the characteristic values ​​are converted into the form of a spatial vector.

3. The vehicle information management system based on big data according to claim 2 is characterized by: The user rates the vehicle according to the value reference formula and establishes a vehicle value reference formula, which is expressed as follows: In the formula, is the value of the vehicle, Minimum standard thresholds for vehicle values ​​set for vehicle information management systems, The price increase ratio for different demand ratings, R is a constant, is the additional value of the vehicle brand or function, is the price weight of different vehicle feature information, and j is the jth feature information of the vehicle feature information.

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