A method for intelligent processing of automobile data

Abstract

The present invention discloses an intelligent processing method for automobile data, which belongs to the field of data processing technology and includes the following steps: S1, collecting driving data of automobile driving files, generating several key triples for the driving data; S2, generating a first part of keys according to several key triples of driving data, and generating a second part of keys according to automobile driving files stored in the cloud; S3, combining the first part of keys and the second part of keys to generate an encryption key for the automobile driving files, and encrypting them. The present invention makes the key generation method more flexible and diverse, which not only improves the security and privacy protection level of automobile data, but also makes full use of the advantages of cloud computing technology, and provides new ideas and methods for the intelligent processing of automobile data.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to an intelligent processing method for automobile data. Background Art

[0002] With the rapid development of the automotive industry and the advancement of intelligent technology, the collection, processing and analysis of automotive data have become increasingly important. As a key data source that reflects various aspects of information such as vehicle operating status, driving behavior, and road conditions, the security and privacy protection of vehicle driving data have become the focus of industry attention. Traditional automotive data processing methods often focus on data storage and simple analysis, and lack sufficient consideration of data security and privacy. However, in the era of big data and cloud computing, automotive data is not only huge in quantity, but also contains a lot of personal privacy and vehicle sensitive information. Therefore, how to effectively protect this data and prevent unauthorized access and leakage has become an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes an intelligent processing method for automobile data.

[0004] The technical solution of the present invention is: a method for intelligent processing of automobile data comprises the following steps:

[0005] S1, collecting driving data of the vehicle driving file, and generating several key triples for the driving data;

[0006] S2, generating a first part of the key according to several key triplets of the driving data, and generating a second part of the key according to the vehicle driving file stored in the cloud;

[0007] S3. Combine the first part of the key and the second part of the key to generate an encryption key for the vehicle driving file and encrypt it.

[0008] Furthermore, S2 includes the following sub-steps:

[0009] S11, extracting several key values ​​of the driving data in the vehicle driving file, and performing hash processing on the several key values ​​using a hash function to obtain several hash values;

[0010] S12, arranging several hash values ​​from large to small to generate a hash value sequence, extracting the first Hash values, where N represents the number of hash values. Indicates rounding up;

[0011] S13, according to the hash value sequence Hash values ​​are used to generate key triples for each key value.

[0012] The beneficial effect of the above further scheme is: in the present invention, the automobile driving data includes many characteristic parameters of the automobile driving process, such as the average value including the average speed, average fuel consumption, average acceleration, etc.; such as the maximum / minimum value including the maximum speed, the lowest fuel consumption, the maximum acceleration, etc.; such as the standard deviation including the standard deviation of the speed fluctuation and the acceleration change, which is used to measure the driving stability; such as the frequency distribution including the frequency of sudden acceleration, sudden braking, and sharp turning. The more important parameters in the driving data are extracted and dimensionless as key values.

[0013] By hashing the key value, a fixed-length hash value can be obtained. This hash value can be regarded as a kind of "fingerprint" of the original key value. Because the hash function is collision-resistant, hashing not only reduces the amount of data, but also increases the security of the data. Selecting a value close to the median in the hash value sequence as the benchmark value represents the typical characteristics of the entire hash value to a certain extent. The generated triple contains both the information of the original data and the hash value, which is helpful for generating the key.

[0014] Furthermore, in S13, the key triple G of the mth key value m The expression is:

[0015] Where H m Represents the hash value of the mth key value, H * Represents the first hash value, round(·) represents the rounding function, Indicates rounding up, and N indicates the number of hash values.

[0016] Furthermore, S2 includes the following sub-steps:

[0017] S21, calculating the node matrix of the vehicle driving file stored in the cloud;

[0018] S22, generating a driving cost value according to a plurality of key triplets of driving data;

[0019] S23, generating a first part of the key and a second part of the key according to the node matrix of the vehicle driving file stored in the cloud and the driving cost value.

[0020] The beneficial effect of the above further scheme is: in the present invention, the driving cost value is calculated based on the hash value in the key triplet to form a comprehensive indicator for measuring driving data and generating the first key part. By comprehensively considering the idle rate of cloud storage space, memory utilization and CPU utilization, a weighted calculation is performed on the node matrix of cloud storage, which reflects the overall status of driving files in cloud storage resources. It is a multi-dimensional set of performance indicators and generates the second key part. The weighting coefficient allows the importance of each performance indicator to be adjusted according to actual needs. This step combines the driving cost value and cloud storage, increases the randomness and complexity of the key, and improves the security of the data.

[0021] Furthermore, in S21, the expression of the node matrix Y of the vehicle driving file stored in the cloud is:

[0022] ; In the formula, α1 represents the weighted coefficient of the cloud storage space idle rate, α2 represents the weighted coefficient of the cloud memory utilization, α3 represents the weighted coefficient of the cloud CPU utilization, e represents the cloud storage space idle rate, u1 represents the cloud memory utilization, and u2 represents the cloud CPU utilization.

[0023] Furthermore, in S22, the calculation formula of the driving cost value S is:

[0024] Where H m Represents the hash value of the mth key value, H * Represents the first hash values, roud(·) represents the rounding function, M represents the number of key values, Indicates rounding up, and N indicates the number of hash values.

[0025] Further, S23 includes the following sub-steps:

[0026] S231, generating a random number, and determining a first part of the key according to the driving cost value;

[0027] S232. Calculate the second part of the key according to the node matrix of the vehicle driving file stored in the cloud.

[0028] Furthermore, in S231, the calculation formula of the first part of the key K1 is: ; In the formula, r represents a random number and S represents the driving cost value.

[0029] Furthermore, in S232, the calculation formula of the second part key K2 is:

[0030] ; Where Y represents the node matrix of the car driving file stored in the cloud, T represents the matrix transpose, and Tr(·) represents the matrix rank operation.

[0031] The beneficial effects of the present invention are as follows: the present invention proposes an intelligent processing method for automobile data, which collects the driving data of the automobile driving file and generates a number of key triplets for it, generates two parts of keys according to these key triplets and the automobile driving file stored in the cloud, combines the two parts of keys, generates an encryption key for the automobile driving file, and performs encryption processing. The present invention makes the key generation method more flexible and diverse, which not only improves the security and privacy protection level of automobile data, but also makes full use of the advantages of cloud computing technology, and provides new ideas and methods for the intelligent processing of automobile data. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The figure is a flow chart of the method for intelligent processing of automobile data. DETAILED DESCRIPTION

[0033] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0034] like Figure 1 As shown, the present invention provides a method for intelligently processing automobile data, comprising the following steps:

[0035] S1, collecting driving data of the vehicle driving file, and generating several key triples for the driving data;

[0036] S2, generating a first part of the key according to several key triplets of the driving data, and generating a second part of the key according to the vehicle driving file stored in the cloud;

[0037] S3. Combine the first part of the key and the second part of the key to generate an encryption key for the vehicle driving file and encrypt it.

[0038] In this embodiment of the present invention, S2 includes the following sub-steps:

[0039] S11, extracting several key values ​​of the driving data in the vehicle driving file, and performing hash processing on the several key values ​​using a hash function to obtain several hash values;

[0040] S12, arranging several hash values ​​from large to small to generate a hash value sequence, extracting the first Hash values, where N represents the number of hash values. Indicates rounding up;

[0041] S13, according to the hash value sequence Hash values ​​are used to generate key triples for each key value.

[0042] In the present invention, the automobile driving data includes many characteristic parameters of the automobile driving process, such as the average value including the average speed, average fuel consumption, average acceleration, etc.; the maximum / minimum value including the maximum speed, the lowest fuel consumption, the maximum acceleration, etc.; the standard deviation including the standard deviation of speed fluctuation and acceleration change, which is used to measure driving stability; the frequency distribution including the frequency of sudden acceleration, sudden braking, and sharp turning. The more important parameters in the driving data are extracted and dimensionless as key values.

[0043] By hashing the key value, a fixed-length hash value can be obtained. This hash value can be regarded as a kind of "fingerprint" of the original key value. Because the hash function is collision-resistant, hashing not only reduces the amount of data, but also increases the security of the data. Selecting a value close to the median in the hash value sequence as the benchmark value represents the typical characteristics of the entire hash value to a certain extent. The generated triple contains both the information of the original data and the hash value, which is helpful for generating the key.

[0044] In the embodiment of the present invention, in S13, the key triplet G of the mth key value m The expression is:

[0045] Where H m Represents the hash value of the mth key value, H * Represents the first hash value, round(·) represents the rounding function, Indicates rounding up, and N indicates the number of hash values.

[0046] In this embodiment of the present invention, S2 includes the following sub-steps:

[0047] S21, calculating the node matrix of the vehicle driving file stored in the cloud;

[0048] S22, generating a driving cost value according to a plurality of key triplets of driving data;

[0049] S23, generating a first part of the key and a second part of the key according to the node matrix of the vehicle driving file stored in the cloud and the driving cost value.

[0050] In the present invention, the driving cost value is calculated based on the hash value in the key triplet to form a comprehensive indicator for measuring driving data and generating the first key part. By comprehensively considering the idle rate of cloud storage space, memory utilization and CPU utilization, a weighted calculation is performed on the node matrix of cloud storage, which reflects the overall status of driving files in cloud storage resources. It is a multi-dimensional set of performance indicators and generates the second key part. The weighting coefficient allows the importance of each performance indicator to be adjusted according to actual needs. This step combines the driving cost value and cloud storage, increases the randomness and complexity of the key, and improves the security of the data.

[0051] In the embodiment of the present invention, in S21, the expression of the node matrix Y of the vehicle driving file stored in the cloud is:

[0052] ; In the formula, α1 represents the weighted coefficient of the cloud storage space idle rate, α2 represents the weighted coefficient of the cloud memory utilization, α3 represents the weighted coefficient of the cloud CPU utilization, e represents the cloud storage space idle rate, u1 represents the cloud memory utilization, and u2 represents the cloud CPU utilization.

[0053] In the embodiment of the present invention, in S22, the calculation formula of the driving cost value S is:

[0054] Where H m Represents the hash value of the mth key value, H * Represents the first hash values, roud(·) represents the rounding function, M represents the number of key values, Indicates rounding up, and N indicates the number of hash values.

[0055] In this embodiment of the present invention, S23 includes the following sub-steps:

[0056] S231, generating a random number, and determining a first part of the key according to the driving cost value;

[0057] S232. Calculate the second part of the key according to the node matrix of the vehicle driving file stored in the cloud.

[0058] In the embodiment of the present invention, in S231, the calculation formula of the first part of the key K1 is: ; In the formula, r represents a random number and S represents the driving cost value.

[0059] In the embodiment of the present invention, in S232, the calculation formula of the second part key K2 is: ; Where Y represents the node matrix of the car driving file stored in the cloud, T represents the matrix transpose, and Tr(·) represents the matrix rank operation.

[0060] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for intelligent processing of automobile data, characterized in that: The following steps are involved: S1, collecting driving data of the vehicle driving file, and generating several key triples for the driving data; S2, generating a first part of the key according to several key triplets of the driving data, and generating a second part of the key according to the vehicle driving file stored in the cloud; S3, combining the first part of the key and the second part of the key to generate an encryption key for the vehicle driving file, and encrypting the key; The S1 comprises the following sub-steps: S11, extracting several key values ​​of the driving data in the vehicle driving file, and performing hash processing on the several key values ​​using a hash function to obtain several hash values; S12, arranging several hash values ​​from large to small to generate a hash value sequence, and extracting the first Hash values, where N represents the number of hash values. Indicates rounding up; S13, according to the hash value sequence Hash values, generate key triples for each key value; In S13, the key triple G of the mth key value m The expression is: Where H m Represents the hash value of the mth key value, H * Represents the first hash value, round(·) represents the rounding function, Indicates rounding up, and N indicates the number of hash values; The S2 comprises the following sub-steps: S21, calculating the node matrix of the vehicle driving file stored in the cloud; S22, generating a driving cost value according to a plurality of key triplets of driving data; S23, generating a first key according to the driving cost value; generating a second key according to the node matrix of the vehicle driving file stored in the cloud; In S21, the expression of the node matrix Y of the vehicle driving file stored in the cloud is: ; In the formula, α1 represents the weighted coefficient of the cloud storage space idle rate, α2 represents the weighted coefficient of the cloud memory utilization, α3 represents the weighted coefficient of the cloud CPU utilization, e represents the cloud storage space idle rate, u1 represents the cloud memory utilization, and u2 represents the cloud CPU utilization; In S22, the calculation formula of the driving cost value S is: Where H m Represents the hash value of the mth key value, H * Represents the first hash values, roud(·) represents the rounding function, M represents the number of key values, Indicates rounding up, and N indicates the number of hash values; The S23 comprises the following sub-steps: S231, generating a random number, and determining a first part of the key according to the driving cost value; S232. Calculate the second part of the key according to the node matrix of the vehicle driving file stored in the cloud.

2. The method for intelligent processing of automobile data according to claim 1, characterized in that: In S231, the calculation formula of the first part of the key K1 is: ; In the formula, r represents a random number and S represents the driving cost value.

3. The method for intelligent processing of automobile data according to claim 1, characterized in that: In S232, the calculation formula of the second part key K2 is: ; Where Y represents the node matrix of the car driving file stored in the cloud, T represents the matrix transpose, and Tr(·) represents the matrix rank operation.

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