Intelligent manufacturing data security protection method based on electronic information encryption technology

Abstract

The invention discloses an intelligent manufacturing data security protection method based on an electronic information encryption technology. The method comprises the following steps: S1, preprocessing original sensor data generated in an intelligent manufacturing process; s2, performing wavelet decomposition on the preprocessed original sensor to obtain a multi-scale coefficient; s3, scrambling the multi-scale coefficient obtained by wavelet decomposition through chaotic mapping; s4, performing fusion encryption on the scrambled multi-scale coefficient and an encryption key; and S5, outputting the fused and encrypted data. Therefore, by combining the multi-scale characteristic of signal processing and the unpredictability of chaotic encryption, the industrial sensor data in the intelligent manufacturing process is converted into the high-security encrypted data, the method has extremely high differential attack resistance and processing rate, the high real-time requirement of an intelligent manufacturing scene for data encryption can be met, and the method is suitable for popularization and application. The method is suitable for safety protection of industrial sensor data in an intelligent manufacturing system.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to a method for protecting intelligent manufacturing data security based on electronic information encryption technology. Background Art

[0002] In the field of intelligent manufacturing, the security protection of industrial sensor data is the key to ensuring the stable operation of the production system. Existing electronic information encryption technology has played a certain protective role in data transmission, storage and other aspects.

[0003] With the increase in data scale and interaction complexity in intelligent manufacturing scenarios, the requirements for encryption technology in terms of real-time performance and adaptability to industrial environments are constantly increasing, and there is an urgent need for security protection solutions that are more in line with actual needs. Summary of the Invention

[0004] The present invention aims to solve the technical problems in the above-mentioned technologies at least to some extent.

[0005] To this end, the present invention discloses a smart manufacturing data security protection method based on electronic information encryption technology, comprising the following steps:

[0006] S1: Preprocessing of raw sensor data generated in the intelligent manufacturing process;

[0007] S2: performing wavelet decomposition on the pre-processed original sensor to obtain multi-scale coefficients;

[0008] S3: scrambling the multi-scale coefficients obtained by wavelet decomposition through chaotic mapping;

[0009] S4: fusing and encrypting the scrambled multi-scale coefficients and the encryption key;

[0010] S5: Output the fused encrypted data.

[0011] According to the intelligent manufacturing data security protection method based on electronic information encryption technology disclosed in the present invention, by combining the multi-scale characteristics of signal processing and the unpredictability of chaotic encryption, the industrial sensor data in the intelligent manufacturing process is converted into high-security encrypted data. It has extremely strong resistance to differential attacks and processing speed, can meet the high real-time requirements of data encryption in intelligent manufacturing scenarios, and is suitable for the security protection of industrial sensor data in intelligent manufacturing systems.

[0012] In addition, the intelligent manufacturing data security protection method based on electronic information encryption technology disclosed in the present invention may also have the following additional technical features:

[0013] Furthermore, the step S1 includes the following sub-steps:

[0014] S1.1: converting the original sensor data into 32-bit unsigned integer format;

[0015] S1.2: dividing the converted original sensor data into data blocks of 1024 32-bit integers, and zero-padding the tail data block to 1024 integers.

[0016] Further, in step S1.1, if the original sensor data is of floating-point type x, and the range of x is -V max ~V max , then the converted value is int[(x+V max )×2 31 / V max ], wherein V max is the full-scale value of the sensor.

[0017] Further, in step S2, a db4 wavelet basis function is used to perform 3-layer wavelet decomposition on each 1024-point data block of the preprocessed original sensor data, the first layer of decomposition obtains 64 low-frequency coefficients cA1 and 64 high-frequency coefficients cD1; the second layer of decomposition operates on cA1 to obtain 32 low-frequency coefficients cA2 and 32 high-frequency coefficients cD2; the third layer of decomposition operates on cA2 to obtain 16 low-frequency coefficients cA3 and 16 high-frequency coefficients cD3, and the multi-scale coefficients obtained after decomposition are represented as {cA3, cD3, cD2, cD1}.

[0018] Further, in step S2, each layer of decomposition is implemented by low-pass filtering and high-pass filtering, the low-pass filtering uses the scaling function φ(t) of the db4 wavelet, and the high-pass filtering uses the wavelet function ψ(t) of the db4 wavelet.

[0019] Further, in step S3, the low-frequency coefficients cA3 in the multi-scale coefficients are scrambled using Logistic chaotic mapping, and the high-frequency coefficients cD3, cD2, and cD1 in the multi-scale coefficients are scrambled using Arnold transformation.

[0020] Further, the formula of Logistic chaotic mapping is x k+1 =μ×x k ×(1-x k ), the initial value x 0= 0.37, and the control parameter μ=4.0; 16 chaotic sequence values x1~x 16 are generated, normalized to integers between 0 and 15 as permutation indexes, and the elements of the low-frequency coefficients cA3 are rearranged according to the permutation indexes.

[0021] Further, the step S4 includes the following sub-steps:

[0022] S4.1: generating a 256-bit AES key K based on the device physical fingerprint, and grouping K into K0~K7 in 32-bit;

[0023] S4.2: performing XOR operation between each low-frequency coefficient cA3 in the multi-scale coefficient after the permutation and a corresponding sub-key K ’ i t mod 2 32 addition;

[0024] S4.3: performing XOR operation between each high-frequency coefficient cD3, cD2, cD1 in the multi-scale coefficient after the permutation and a corresponding AES key byte.

[0025] Further, the device physical fingerprint is a SHA-256 hash value of a CPU serial number and a MAC address, and the 256-bit AES key K is generated based on the device physical fingerprint.

[0026] Further, in step S5, the multi-scale coefficient after the fusion encryption is recombined into a 1024-point data block, converted into a binary stream in a little-endian sequence, and after splicing all data blocks, a 16-byte check code is added, and the check code is a CRC32 value of the data after the fusion encryption.

[0027] Additional contents and advantages of the present application will be given in the following description, or can be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0028] The technical solutions and beneficial effects of the present application will become apparent and easy to understand from the following contents combined with the drawings.

[0029] Figure 1 A workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present application;

[0030] Figure 2 Another workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present application;

[0031] Figure 3 Another workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present application;

[0032] Figure 4 Another workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present application;

[0033] Figure 5 Another workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present application;

[0034] Figure 6 This is another workflow diagram of the intelligent manufacturing data security protection method based on electronic information encryption technology of the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0036] The following will describe the intelligent manufacturing data security protection method based on electronic information encryption technology disclosed in the present invention with reference to the accompanying drawings;

[0037] like Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 and Figure 6 As shown, the intelligent manufacturing data security protection method based on electronic information encryption technology includes the following steps:

[0038] S1: Preprocessing of raw sensor data generated in the intelligent manufacturing process;

[0039] S2: Perform wavelet decomposition on the pre-processed raw sensor to obtain multi-scale coefficients;

[0040] S3: Scramble the multi-scale coefficients obtained by wavelet decomposition through chaotic mapping;

[0041] S4: Fusion encryption of the scrambled multi-scale coefficients and the encryption key;

[0042] S5: Output the fused encrypted data.

[0043] It should be noted that step S1 includes the following sub-steps:

[0044] S1.1: Convert the raw sensor data into 32-bit unsigned integer format;

[0045] S1.2: Divide the converted raw sensor data into data blocks of 1024 32-bit integers each, and fill the trailing data blocks with zeros to make them 1024 integers.

[0046] In step S1.1, if the original sensor data is a floating point type x, and the range of x is -V max ~V max , then the converted value is int[(x+V max )×2 31 / V max ], where V max is the full-scale value of the sensor.

[0047] In step S2, the db4 wavelet basis function is used to perform a three-layer wavelet decomposition on each 1024-point data block of the preprocessed raw sensor data. The first-layer decomposition obtains 64-point low-frequency coefficients cA1 and 64-point high-frequency coefficients cD1; the second-layer decomposition operates on cA1 to obtain 32-point low-frequency coefficients cA2 and 32-point high-frequency coefficients cD2; the third-layer decomposition operates on cA2 to obtain 16-point low-frequency coefficients cA3 and 16-point high-frequency coefficients cD3. The multi-scale coefficients obtained after decomposition are expressed as {cA3, cD3, cD2, cD1}.

[0048] In step S2, each layer of decomposition is achieved by low-pass filtering and high-pass filtering. The low-pass filtering uses the scaling function φ(t) of the db4 wavelet, and the high-pass filtering uses the wavelet function ψ(t) of the db4 wavelet.

[0049] In step S3, the low-frequency coefficient cA3 in the multi-scale coefficients is scrambled using Logistic chaotic mapping, and the high-frequency coefficients cD3, cD2, and cD1 in the multi-scale coefficients are scrambled using Arnold transformation.

[0050] The formula of Logistic chaotic mapping is x k+1 =μ×x k ×(1-x k ), initial value x 0= 0.37, control parameter μ=4.0; generate 16 chaotic sequence values ​​x1~x 16 , normalized to an integer between 0 and 15 as a permutation index, and the elements of the low-frequency coefficient cA3 are rearranged according to the permutation index.

[0051] Step S4 includes the following sub-steps:

[0052] S4.1: Generate a 256-bit AES key K based on the device's physical fingerprint and group it into 32-bit groups K0 to K7.

[0053] S4.2: Low-frequency coefficient cA3 in the scrambled multi-scale coefficients ’ , every 4 elements and 1 subkey K t Modulo 2 32 addition;

[0054] S4.3: For the high-frequency coefficients cD3, cD2, and cD1 in the scrambled multi-scale coefficients, perform an XOR operation on each element with the AES key byte at the corresponding position.

[0055] The device's physical fingerprint is the SHA-256 hash value of the CPU serial number and MAC address. The 256-bit AES key K is generated based on the device's physical fingerprint.

[0056] In step S5, the fused encrypted multi-scale coefficients are reorganized into 1024-point data blocks, converted into a binary stream in little-endian order, and spliced to add a 16-byte check code. The check code is the CRC32 value of the fused encrypted data. The final output encrypted data has the format: number of data blocks + check code + encrypted coefficient stream.

[0057] Specifically:

[0058] In the data preprocessing stage of step S1, the original data collected by industrial sensors in the intelligent manufacturing process need to be standardized first, so that the subsequent encryption operation can be efficient and accurate.

[0059] For the conversion of the original sensor data in S1.1 to 32-bit unsigned integer format, the output type of the sensor needs to be determined first in actual operation.

[0060] If the sensor output data is floating-point type and its measurement range is between -V max and V max (where V max is the full-scale value of the sensor, which can be obtained from the technical parameter manual of the sensor), the floating-point data needs to be converted to 32-bit unsigned integer.

[0061] During the conversion, first calculate the sum of the floating-point value and V 31 , then multiply the sum by 2 max and divide by V max , and finally take the integer part of the result to obtain the converted 32-bit unsigned integer. For example, if the full-scale V 16 of a temperature sensor is 100 and the collected floating-point temperature value is 25, first calculate the sum of 25 and 100, which is 125, then multiply 125 by 231 and divide by 100, and the integer part of the result is the converted 32-bit unsigned integer.

[0062] Through this conversion, sensor data of different ranges can be unified into the format of 32-bit unsigned integer.

[0063] After completing the format conversion, enter the data block division step of S1.2. At this time, the total number of 32-bit unsigned integers obtained after conversion needs to be counted, and then divided according to the standard of 1024 integers per data block.

[0064] If the total number is an integer multiple of 1024, it can be directly divided into the corresponding number of complete data blocks.

[0065] If the total number has a remainder after being divided by 1024, i.e., there is not enough 1024 data in the tail, zero padding needs to be performed on the tail data block until the data block contains 1024 32-bit unsigned integers;

[0066] For example, if the converted data has a total of 2500 integers, 2500 divided by 1024 is 2 with a remainder of 452, then it needs to be divided into 3 data blocks, the first two are complete 1024 integers, and the third data block is composed of 452 original data and 572 zeros;

[0067] Such a division method can ensure that the subsequent wavelet decomposition is performed on fixed-size data blocks, ensuring the normalization and consistency of the processing process;

[0068] The wavelet decomposition operation in step S2 is based on the preprocessed data block, and the purpose is to decompose the original data into coefficients of different scales, so as to be encrypted in subsequent targeted manner;

[0069] In specific implementation, db4 wavelet basis function is used to perform 3-layer wavelet decomposition on each 1024-point data block, and each layer of decomposition is realized by low-pass filtering and high-pass filtering;

[0070] The low-pass filter uses the scale function of the db4 wavelet, which is mainly used to extract the low-frequency components in the data, which usually contain the main features of the data;

[0071] The high-pass filter uses the wavelet function of the db4 wavelet, which is used to extract the high-frequency components in the data, which are mostly details or noise of the data;

[0072] In the first layer decomposition, the 1024-point data block is input into the low-pass filter and the high-pass filter. After filtering, 64 low-frequency coefficients cA1 and 64 high-frequency coefficients cD1 are obtained. Here, 64 points are obtained by downsampling the filtered results, and the downsampling process retains every other point in the filtered results, thereby reducing the data amount by half to adapt to the needs of subsequent processing;

[0073] The second layer decomposition is only performed on the low-frequency coefficients cA1 obtained in the first layer. Similarly, through low-pass filtering and high-pass filtering, and after downsampling processing, 32 low-frequency coefficients cA2 and 32 high-frequency coefficients cD2 are extracted from the 64-point cA1;

[0074] The third layer decomposition is performed on the low-frequency coefficients cA2 obtained in the second layer, and through the same filtering and downsampling operation, 16 low-frequency coefficients cA3 and 16 high-frequency coefficients cD3 are obtained from the 32-point cA2;

[0075] After completing these three layers of decomposition, all the coefficients obtained are sorted in the order of cA3, cD3, cD2, and cD1 to form a multi-scale coefficient set for the data block, preparing for the next scrambling operation;

[0076] Step S3 scrambles the multi-scale coefficients through chaotic mapping. The core of this method is to utilize the unpredictability of chaotic mapping to disrupt the original order of the coefficients and enhance data security.

[0077] For the low-frequency coefficient cA3, Logistic chaotic mapping is used for scrambling;

[0078] During the operation, the initial value of the Logistic chaotic map is first determined to be 0.37, the control parameter is 4.0, and then a series of chaotic sequence values ​​are generated according to its mapping rule;

[0079] Since cA3 contains 16 elements, 16 chaotic sequence values ​​x1 to x1 need to be generated. 16 ;

[0080] After generating these values, they are normalized and converted into integers between 0 and 15. These integers are the permutation indices used for scrambling.

[0081] Then, rearrange the 16 elements in cA3 according to this permutation index. For example, if the permutation index is [3,1,5,...,0], the element originally at position 0 in cA3 is moved to position 3, the element originally at position 1 is moved to position 1, the element originally at position 2 is moved to position 5, and so on. The scrambled cA3 is recorded as cA3'.

[0082] For the high-frequency coefficients cD3, cD2, and cD1, Arnold transform is used for scrambling. Arnold transform is a scrambling method based on matrix transformation and is suitable for scrambling two-dimensional data.

[0083] During the operation, each high-frequency coefficient set is first arranged into a two-dimensional matrix according to certain rules. For example, for a 64-point cD1, it can be arranged into a matrix of 8 rows and 8 columns;

[0084] The 32-point cD2 can be arranged into a matrix of 4 rows and 8 columns or 8 rows and 4 columns (the specific arrangement can be determined according to the actual situation, as long as all elements can be completely contained). The 16-point cD3 can be arranged into a matrix of 4 rows and 4 columns.

[0085] Then, the number of Arnold transform iterations is determined (usually set based on security requirements, typically ranging from a few to dozens of times), and the positions of the elements in the matrix are transformed according to the rules of the Arnold transform. During each transformation, the position (i, j) of each element in the matrix is ​​transferred to a new position according to specific rules. After multiple iterations, the original order of the elements in the matrix is ​​completely disrupted, thereby achieving scrambling of the high-frequency coefficients.

[0086] The fusion encryption in step S4 combines the scrambled coefficients with the encryption key to further enhance the encryption strength of the data.

[0087] In S4.1, key generation is based on the device's physical fingerprint, which is generated by the device's CPU serial number and MAC address. The specific operation is to first obtain the device's CPU serial number (which can be obtained through the device's hardware information query tool) and MAC address (which can be queried through network settings or related commands), then concatenate these two pieces of information into a string, and then perform a SHA-256 hash operation on the string to obtain a 256-bit hash value. This hash value is the basis for generating the 256-bit AES key K;

[0088] Afterwards, the 256-bit AES key K is divided into groups of 32 bits each, resulting in 8 subkeys, denoted as K0 to K7;

[0089] In S4.2, the scrambled low-frequency coefficients cA3' are encrypted. Since cA3' contains 16 elements and there are 8 subkeys, the elements in cA3' are grouped into groups of 4, for a total of 4 groups.

[0090] Then, perform a modulo 2^32 addition operation on the 4 elements of the first group and the subkey K0, perform a modulo 2^32 addition operation on the 4 elements of the second group and the subkey K1, and so on, until all elements have completed the operation;

[0091] The modulo 2^32 addition here means that after adding two numbers, if the result exceeds 2^32, the remainder of the result divided by 2^32 is taken as the final result to ensure that the result of the operation is still a 32-bit integer;

[0092] For the high-frequency coefficients cD3, cD2, and cD1 after scrambling in S4.3, an exclusive-OR operation is used for encryption. First, the 256-bit AES key K is converted to 32 bytes (because 1 byte equals 8 bits and 256 bits equals 32 bytes);

[0093] Then, for each element in each high-frequency coefficient set, convert it to its corresponding byte form and perform an XOR operation with the byte at the corresponding position in the AES key;

[0094] The rule of XOR operation is: when the two corresponding byte bits are the same, the result is 0, and when the two corresponding byte bits are different, the result is 1. Through this byte-by-byte XOR operation, the high-frequency coefficients are encrypted;

[0095] The output encrypted data of step S5 is to convert the fused encrypted coefficients into a specific format and add verification information to facilitate storage and transmission;

[0096] First, the encrypted multi-scale coefficients are reorganized according to the structure of the original data block to restore them into 1024-point data blocks. During the reorganization, the encrypted coefficients need to be combined in the order of cA3', cD3, cD2, and cD1 to ensure that the size of each data block is still 1024 points.

[0097] Next, the reassembled 1024-point data block is converted into a binary stream in little-endian order. Little-endian order means that the low-order byte of the data is stored at a low memory address, and the high-order byte is stored at a high memory address. During the conversion, each 32-bit integer is split into four bytes and then arranged in order, with the low-order byte first and the high-order byte last, to form a binary stream.

[0098] After completing the binary stream conversion for all data blocks, these binary streams are concatenated. The CRC32 checksum is then calculated for the concatenated data. The CRC32 checksum is a 4-byte (32-bit) checksum generated by a cyclic redundancy check algorithm, used to detect errors during data transmission or storage. Here, it is converted to a 16-byte checksum (which can be achieved through repetition or a specific encoding method) and added to the end of the concatenated binary stream.

[0099] Finally, the final encrypted data is output in the format of "number of data blocks + checksum + encryption coefficient stream." The number of data blocks indicates how many 1024-point data blocks the encrypted data contains, the checksum verifies data integrity, and the encryption coefficient stream is the concatenated binary stream data.

[0100] In summary, the intelligent manufacturing data security protection method based on electronic information encryption technology disclosed in the present invention converts industrial sensor data in the intelligent manufacturing process into high-security encrypted data by combining the multi-scale characteristics of signal processing and the unpredictability of chaotic encryption. It has extremely strong resistance to differential attacks and processing speed, can meet the high real-time requirements of data encryption in intelligent manufacturing scenarios, and is suitable for the security protection of industrial sensor data in intelligent manufacturing systems.

[0101] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for protecting intelligent manufacturing data security based on electronic information encryption technology, characterized in that: The following steps are involved: S1: Preprocessing of raw sensor data generated in the intelligent manufacturing process; S2: performing wavelet decomposition on the pre-processed original sensor to obtain multi-scale coefficients; S3: scrambling the multi-scale coefficients obtained by wavelet decomposition through chaotic mapping; S4: fusing and encrypting the scrambled multi-scale coefficients and the encryption key; S5: Output the fused encrypted data.

2. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 1 is characterized in that: The step S1 includes the following sub-steps: S1.1: Convert the raw sensor data into a 32-bit unsigned integer format; S1.2: Divide the converted raw sensor data into data blocks of 1024 32-bit integers each, and fill the tail data blocks with zeros if the number of data blocks is less than 1024 to make them 1024 integers.

3. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 2 is characterized in that: In step S1.1, if the original sensor data is a floating point type x, and the range of x is -V max ~V max , then the converted value is int[(x+V max )×2 31 / V max ], where V max is the full-scale value of the sensor.

4. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 1 is characterized in that: In step S2, the db4 wavelet basis function is used to perform a three-layer wavelet decomposition on each 1024-point data block of the preprocessed raw sensor data. The first-layer decomposition obtains 64-point low-frequency coefficients cA1 and 64-point high-frequency coefficients cD1; the second-layer decomposition operates on cA1 to obtain 32-point low-frequency coefficients cA2 and 32-point high-frequency coefficients cD2; the third-layer decomposition operates on cA2 to obtain 16-point low-frequency coefficients cA3 and 16-point high-frequency coefficients cD3. The multi-scale coefficients obtained after decomposition are expressed as {cA3, cD3, cD2, cD1}.

5. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 4 is characterized in that: In step S2, each layer of decomposition is achieved by low-pass filtering and high-pass filtering. The low-pass filtering uses the scaling function φ(t) of the db4 wavelet, and the high-pass filtering uses the wavelet function ψ(t) of the db4 wavelet.

6. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 1 is characterized in that: In step S3, the low-frequency coefficient cA3 in the multi-scale coefficients is scrambled using Logistic chaotic mapping, and the high-frequency coefficients cD3, cD2, and cD1 in the multi-scale coefficients are scrambled using Arnold transformation.

7. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 6 is characterized in that: The formula of Logistic chaotic mapping is x k+1 =μ×x k ×(1-x k ), initial value x 0= 0.37, control parameter μ=4.0; generate 16 chaotic sequence values ​​x1~x 16 , normalized to an integer between 0 and 15 as a permutation index, and the elements of the low-frequency coefficient cA3 are rearranged according to the permutation index.

8. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 1 is characterized in that: The step S4 includes the following sub-steps: S4.1: Generate a 256-bit AES key K based on the device's physical fingerprint and group it into 32-bit groups K0 to K7. S4.2: Low-frequency coefficient cA3 in the scrambled multi-scale coefficients ’ , every 4 elements and 1 subkey K t Modulo 2 32 addition; S4.3: For each element of the high-frequency coefficients cD3, cD2, and cD1 in the scrambled multi-scale coefficients, perform an XOR operation with the AES key byte at the corresponding position.

9. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 8 is characterized in that: The device physical fingerprint is the SHA-256 hash value of the CPU serial number and the MAC address, and the 256-bit AES key K is generated based on the device physical fingerprint.

10. The intelligent manufacturing data security protection method based on electronic information encryption technology according to claim 1, characterized in that: In step S5, the multi-scale coefficients after fusion encryption are reorganized into 1024-point data blocks, converted into binary streams in little-endian order, and a 16-byte check code is added after all data blocks are spliced. The check code is the CRC32 value of the fusion encrypted data.

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