A big data-based human resource data management method and system

Abstract

The present application relates to the technical field of encrypted storage, and more particularly to a human resource data management method and system based on big data. The method comprises the following steps: encoding human resource data sequences to obtain an initial Huffman tree and an encoded sequence; selecting a first target position, obtaining the adhesion of the first target position and other positions; selecting the position with the smallest adhesion among all other positions as a position to be replaced; keeping the encoding at the first target position and the position to be replaced in the initial Huffman tree unchanged, and replacing the human resource data at the two positions to obtain a first Huffman tree; decoding the encoded sequence according to the first Huffman tree to obtain a first encrypted sequence, obtaining the degree of regular variation of the first encrypted sequence, and in response to the degree of regular variation being greater than a preset degree threshold, storing the first encrypted data sequence as a final encrypted sequence. Thus, the security of human resource data is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of encrypted storage, and in particular to a human resource data management method and system based on big data. BACKGROUND

[0002] With the acceleration of digital transformation, a large amount of sensitive information is stored in the human resource management system of an enterprise, such as personal information of employees, salary records, bank account information, etc. These data not only involve the privacy of employees, but also relate to the reputation and economic interests of the enterprise. Therefore, in order to protect the security of these data, human resource data needs to be encrypted.

[0003] As a commonly used encryption type, permutation encryption is often used for encryption of human resource data. This encryption method destroys the spatial correlation information between data by exchanging the positions of data to achieve data encryption, but this encryption method does not change the value of the data, resulting in that the statistical characteristics of the encrypted data are not destroyed. Human resource data not only has contextual correlation, but also has certain statistical characteristics, so using ordinary permutation encryption algorithm cannot hide the regular information of human resource data, leaving a breakthrough for crackers to crack the encrypted data of human resource data. Therefore, how to improve the encryption effect of permutation encryption has become the research focus of the present application.

[0004] The patent application file with publication number CN117236908A discloses a human resource data management method and system. The method in the patent application file is mainly related to the prediction of human resource data and does not involve the related content of permutation encryption, so using the method in the patent application file cannot solve the technical problems of the present application. SUMMARY

[0005] In order to improve the security effect of human resource data, the present application provides a human resource data management method and system based on big data.

[0006] In the first aspect, the present application provides a human resource data management method based on big data, which adopts the following technical solution:

[0007] A human resource data management method based on big data, comprising the steps of:

[0008] obtaining a human resource data sequence;

[0009] encoding the human resource data sequence to obtain an initial Huffman tree and an encoded sequence; selecting a leaf position as a first target position in the initial Huffman tree, and obtaining the adhesion of the first target position and other positions, which satisfies the relationship: filtering out the human resource data segment in which the first target position and any other position appear simultaneously from all data segments of the human resource data sequence with the length of i, and recording as i-element reference data segment, obtaining the mean value of the closest distance between the human resource data of the first target position and the human resource data of any other position in the i-element reference data segment, and recording as i-element metric distance, indicating the i-element metric distance between the first target position and any other position, indicating the number of i-element reference data segments, N indicating the preset cutoff length of reference data segment, and L indicating the adhesion between the first target position and any other position; selecting the position with the minimum adhesion from all other positions as the position to be replaced; keeping the codes at the first target position and the position to be replaced in the initial Huffman tree unchanged, and replacing the human resource data at the two positions to obtain the first Huffman tree;

[0010] decoding the coding sequence according to the first Huffman tree to obtain the first encrypted sequence, obtaining the regular variation degree of the first encrypted sequence, and in response to the regular variation degree being greater than a preset degree threshold, storing the first encrypted data sequence as the final encrypted sequence.

[0011] The present application changes the statistical information of human resource data by replacing the human resource data at different positions in the Huffman tree to change the corresponding relationship between human resource data and coding, thereby effectively improving the encryption effect; further, when replacing the human resource data at different positions in the Huffman tree, the corresponding position to be replaced is selected by adhesion, thereby effectively destroying the association of data and improving the encryption effect; further, when calculating the adhesion, the metric distance and the number of reference data segments are introduced to accurately analyze the adhesion of the human resource data at two positions in the Huffman tree, thereby providing a basis for subsequent effective encryption; further, after each replacement encryption, the regular change of the data is analyzed, thereby accurately controlling the encryption process and preventing over-encryption and under-encryption.

[0012] Preferably, the selecting a leaf position as the first target position in the initial Huffman tree comprises:

[0013] obtaining a chaotic sequence;

[0014] taking the data with the same value in the human resource data sequence as a class, obtaining the number of value categories of the data in the human resource data sequence, and recording as the first number, and obtaining the first positioning data by adding one to the remainder of the first data in the chaotic sequence and the first number;

[0015] taking each leaf of the Huffman tree as an independent position, obtaining the D1th position as the first target position in the initial Huffman tree in the order from top to bottom and from left to right, and D1 indicating the first positioning data.

[0016] The application selects the replacement position by the chaotic sequence with great randomness, effectively destroys the regularity of the data replacement position, and improves the encryption effect.

[0017] Preferably, the method for obtaining the i-ary metric distance comprises:

[0018] The human resource data at the first target position of the initial Huffman tree is recorded as first target position data, all data with the value equal to the first target position data in the human resource data sequence is recorded as analysis data of the first target position, human resource data at any other position in the initial Huffman tree is recorded as reference position data, and all data with the value equal to the reference position data in the human resource data sequence is recorded as reference data of the first target position.

[0019] The distance between each analysis data and the nearest reference data in the i-ary reference data segment is obtained, and the average of the distances between all analysis data and the nearest reference data in the i-ary reference data segment is taken as the i-ary metric distance between the first target position and any other position.

[0020] The application reflects the distance relationship between the human resource data at two positions in the Huffman tree more comprehensively and accurately by the metric distance, and provides a basis for subsequent cohesiveness analysis.

[0021] Preferably, the method for obtaining the regular variation degree of the first encryption sequence comprises:

[0022] ;

[0023] wherein the data with the same value is taken as a category, the position of each data in each category in the human resource data sequence is recorded as a pre-encryption position, the position of each data in each category in the first encryption data sequence is recorded as a post-encryption position, the post-encryption position nearest to the pre-encryption position of each data in each category is taken as a reference post-encryption position of each data in each category from all post-encryption positions of the data in the same category, represents the frequency of the kth category data in the human resource data sequence, represents the frequency of the kth category data in the first encryption sequence, represents the distance between the pre-encryption position of the jth data in the kth category and the reference post-encryption position, represents the number of data in the kth category, represents the number of data categories in the human resource data sequence, C represents the regular variation degree of the first encryption sequence, and tanh() represents the hyperbolic tangent function.

[0024] The present application can accurately measure the data regular variation by analyzing the data frequency and position difference before and after encryption, and provide a basis for accurate encryption control.

[0025] Preferably, in response to the regular variation degree being not greater than the preset degree threshold, the subsequent includes:

[0026] In the first Huffman tree, a leaf position is selected as a second target position, the adhesion of the second target position to other positions is calculated according to the first encryption sequence, the position with the minimum adhesion is selected as a to-be-replaced position from all other positions, the encoding at the second target position and the to-be-replaced position in the first Huffman tree is kept unchanged, the human resource data at the two positions is replaced to obtain a second Huffman tree, the second encryption sequence is obtained by decoding the encoding sequence using the second Huffman tree, the regular variation degree of the second encryption sequence is calculated, and in response to the regular variation degree being greater than the preset degree threshold, the second encryption sequence is stored as a final encryption sequence.

[0027] Preferably, in the first Huffman tree, a leaf position is selected as a second target position, including:

[0028] Obtaining a chaotic sequence;

[0029] The data with the same value in the human resource data sequence is taken as a category, the number of value categories of the data in the human resource data sequence is obtained and denoted as a first number, the second positioning data is obtained by adding one to the remainder of the second data in the chaotic sequence and the first number.

[0030] Each leaf of the first Huffman tree is taken as an independent position, the D2th position in the first Huffman tree is obtained as a second target position in the order from top to bottom and from left to right, and D2 represents the second positioning data.

[0031] Preferably, the first encryption data sequence is stored as a final encryption sequence, including:

[0032] The first encryption data sequence is stored as a final encryption sequence, the number of data participating in the encryption processing in the chaotic sequence is obtained and denoted as a key length, and the final encryption sequence and the key length are stored.

[0033] The present application only needs to store the final encryption sequence and the key length to realize decryption, has less encryption-related information exposed, and further improves the security of the data after encryption.

[0034] In a second aspect, the present application provides a human resource data management system based on big data, which adopts the following technical scheme:

[0035] A human resource data management system based on big data, comprising: a processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the above-mentioned human resource data management method based on big data.

[0036] By adopting the above technical solution, the above-mentioned human resource data management system based on big data generates a computer program and is stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, and facilitate use.

[0037] The present application has the following technical effects:

[0038] The present application changes the statistical information of human resource data by replacing human resource data at different positions in the Huffman tree and changing the correspondence between human resource data and coding, thereby effectively improving the encryption effect.

[0039] Further, when replacing human resource data at different positions in the Huffman tree, the corresponding position to be replaced is selected by adhesion, thereby effectively destroying the association of the data and improving the encryption effect.

[0040] Further, when calculating the adhesion, the number of reference data segments and the metric distance are introduced to accurately analyze the adhesion of human resource data at two positions in the Huffman tree, thereby providing a basis for subsequent effective encryption.

[0041] Further, after each replacement encryption, the regular change of the data is analyzed, thereby accurately controlling the encryption process and preventing over-encryption and under-encryption. BRIEF DESCRIPTION OF DRAWINGS

[0042] The above and other objects, features and advantages of the exemplary embodiments of the present application will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein like reference numerals refer to like elements throughout. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the present application.

[0043] Figure 1 is a method flowchart of a human resource data management method based on big data according to an embodiment of the present application;

[0044] Figure 2 is an example diagram of an initial Huffman tree provided for an embodiment of the present application;

[0045] Figure 3 is an example diagram of a first Huffman tree provided for an embodiment of the present application. DETAILED DESCRIPTION

[0046] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0047] It should be understood that when the claims, the specification, and the drawings of the present application use the terms "first", "second", etc., they are only used to distinguish different objects, and are not used to describe a specific sequence. The terms "include" and "contain" used in the specification and claims of the present application indicate the presence of the described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0048] The embodiments of the present application disclose a human resource data management method based on big data, referring to Figure 1 , comprising steps S1-S4:

[0049] S1: obtaining a human resource data sequence.

[0050] Specifically, the human resource data is collected, the characters and words in the human resource data are converted into decimal data by using the ASCLL table, and the sequence formed by the converted human resource data is referred to as a human resource data sequence.

[0051] The chaotic sequence is generated by using the Logistic mapping method.

[0052] It should be noted that the hyperparameter in the Logistic mapping method for generating the chaotic sequence is agreed by the encrypter and the decrypter, and does not need to be transmitted.

[0053] S2: encoding the human resource data sequence to obtain an initial Huffman tree and an encoding sequence; selecting a leaf position in the initial Huffman tree as a first target position, obtaining the adhesion of the first target position and other positions; selecting the position with the smallest adhesion from all other positions as a to-be-replaced position; keeping the encoding at the first target position and the to-be-replaced position in the initial Huffman tree unchanged, and replacing the human resource data at the two positions to obtain a first Huffman tree.

[0054] It should be noted that in order to make the encryption by scrambling, not only the position of the data can be changed, but also the statistical information of the data can be changed. The data with different statistical characteristics need to be replaced. For example, the data sequence [3, 6, 8, 8, 2, 6, 8, 0]. The frequency of data 8 is 3, and the frequency of data 0 is 0. Data 8 can be replaced with data 0, and data 0 can be replaced with data 8. After the replacement, the data sequence obtained is [3, 6, 0, 0, 2, 6, 0, 8]. Through the replacement, not only the position of the data can be changed, but also the statistical characteristics of the data can be changed. Therefore, the method can better hide the data regularity information.

[0055] S20: encoding the human resource data sequence to obtain an initial Huffman tree and an encoding sequence.

[0056] It should be noted that since the Huffman encoding algorithm can set different encoding values for data with different statistical characteristics, the replacement of data with different statistical characteristics can be realized through the correspondence between the scrambled data and the encoding values.

[0057] Preferably, as an example, the encoding of the human resource data sequence to obtain an initial Huffman tree and an encoding sequence comprises:

[0058] The human resource data sequence is encoded by using the Huffman encoding algorithm to obtain an initial Huffman tree and an encoding sequence.

[0059] S21: selecting a leaf position in the initial Huffman tree as a first target position, obtaining the adhesion of the first target position and other positions; selecting the position with the smallest adhesion from all other positions as a to-be-replaced position; keeping the encoding of the first target position and the to-be-replaced position in the initial Huffman tree unchanged, and replacing the human resource data of the two positions to obtain a first Huffman tree.

[0060] S210: selecting a leaf position in the initial Huffman tree as a first target position.

[0061] Preferably, as an example, the selection of a leaf position in the initial Huffman tree as a first target position comprises:

[0062] obtaining a chaotic sequence;

[0063] taking the data with the same value in the human resource data sequence as a class, obtaining the number of value categories of the data in the human resource data sequence, denoted as a first number, and adding one to the remainder of the first data in the chaotic sequence and the first number to obtain a first positioning data;

[0064] Each leaf of the Huffman tree is taken as an independent position, and the D1th position in the initial Huffman tree is taken as the first target position in the order from top to bottom and from left to right, where D1 represents the first positioning data.

[0065] S211: Obtain the adhesion of the first target position and other positions.

[0066] It should be noted that there are some data strings that appear multiple times in the human resource data sequence. When the data in the data string are replaced with each other, the data regularity trace can still be retained. For example, the sequence [1, 2, 3, 4, 7, 6, 1, 2, 3, 4, 2, 4, 1, 2, 3, 4] can be observed to see that 1, 2, 3, and 4 are data strings that appear multiple times in the data sequence. By using data 2 to replace data 1 and using data 1 to replace data 2, the data sequence [2, 1, 3, 4, 7, 6, 2, 1, 3, 4, 1, 4, 2, 1, 3, 4] is obtained. Although the repeated data string is changed from 1, 2, 3, and 4 to 2, 1, 3, and 4, the repetition regularity still exists. The internal data of the repeated string is replaced with each other, and the effect of hiding the regularity is poor. Therefore, it is necessary to avoid the replacement of the internal data of the repeated string with each other. First, it is necessary to determine the case that each two data are internal data of a repeated string. In this embodiment, the adhesion is used to reflect this index.

[0067] Preferably, as an example, the adhesion of the first target position and other positions is obtained, including:

[0068]

[0069] wherein, from all data segments with a length of i in the human resource data sequence, a data segment in which the human resource data of the first target position and the human resource data of any other position appear at the same time is screened out, denoted as an i-gram reference data segment, and a mean value of the closest distance between the human resource data of the first target position and the human resource data of any other position in the i-gram reference data segment is obtained, denoted as an i-gram metric distance, denotes the i-gram metric distance between the first target position and any other position, denotes the number of i-gram reference data segments, N denotes a preset cutoff length of the reference data segment, and L denotes the adhesion of the first target position and any other position.

[0070] It should be noted that, The smaller the adhesion is, the smaller the interval distance between the human resource data of the first target position and the human resource data of any other position in the sequence is, and thus the adhesion of the human resource data of the two positions is greater. The larger the value, the more frequently the human resources data of the first target location appears simultaneously with any other location in a shorter data string, indicating a greater cohesion between the human resources data of these two locations.

[0071] It should be added that the methods for obtaining a data segment of length i from a human resources data sequence include:

[0072] use The window is used to slide through the human resources data sequence with a sliding step size of 1. After each slide, the data segment in the window is obtained, resulting in all data segments of length i. Here, i is any integer between 2 and N, and N represents the preset cutoff length of the reference data segment. In this embodiment, N is 10 as an example. Other embodiments can take other values, and this embodiment does not impose specific restrictions.

[0073] It is necessary to further supplement the methods for obtaining the i-ary metric distance, including:

[0074] The human resources data at the first target location in the initial Huffman tree is denoted as the first target location data. All data in the human resources data sequence with values ​​equal to the first target location data are denoted as the analysis data for the first target location. The human resources data at any other location in the initial Huffman tree are denoted as the reference location data. All data in the human resources data sequence with values ​​equal to the reference location data are denoted as the reference data for the first target location.

[0075] In the i-ary reference data segment, obtain the distance between each analysis data and the nearest reference data. Use the average distance between all analysis data and the nearest reference data in the i-ary reference data segment as the i-ary metric distance between the first target location and any other location.

[0076] S212: Select the position with the least cohesion among all other positions as the position to be replaced; keep the encoding of the first target position and the position to be replaced in the initial Huffman tree unchanged, and replace the human resources data of the two positions to obtain the first Huffman tree.

[0077] Preferably, as an example, the position with the lowest cohesion among all other positions is selected as the position to be replaced; keeping the encodings at the first target position and the position to be replaced in the initial Huffman tree unchanged, the human resource data at the two positions are replaced to obtain the first Huffman tree, including:

[0078] In all other positions, the position with the least adhesion to the first target position is taken as the replacement position of the first target position. The human resource data of the first target position is replaced with the human resource data of the replacement position, and the encoding values of the first target position and the replacement position are not replaced, to obtain a replaced Huffman tree, denoted as a first Huffman tree. According to the Huffman coding algorithm, the encoding sequence is decoded by using the correspondence between the human resource data and the encoding in the first Huffman tree to obtain a decoded sequence, denoted as a first encrypted sequence.

[0079] For ease of understanding, the encryption process is described below with a specific data sequence as an example, for example, the sequence [3, 6, 8, 8, 8, 2, 8, 6, 8, 8, 0, 3, 0, 8, 0]. The sequence [3, 6, 8, 8, 8, 2, 8, 6, 8, 8, 0, 3, 0, 8, 0] is processed by using the Huffman coding algorithm to obtain an initial Huffman tree and an encoding sequence [1111, 110, 0, 0, 0, 1110, 0, 110, 0, 0, 10, 1111, 10, 0, 10], Figure 2 The initial Huffman tree is shown in the following figure, Figure 2 The data in the pentagram represents each human resource data, the data in the parentheses represents the frequency of each human resource data, and the data in the hexagon represents the corresponding encoding of each human resource data. The human resource data 8 in the first target position is replaced with the human resource data 6 in the replacement position, and the encoding in the first target position and the encoding in the replacement position are kept unchanged to obtain a first Huffman tree, Figure 3 The first Huffman tree is shown in the following figure. According to the Huffman coding algorithm, the encoding sequence is decoded by using the correspondence between the human resource data and the encoding in the first Huffman tree to obtain a first encrypted sequence [3, 8, 6, 6, 6, 2, 6, 8, 6, 6, 0, 3, 0, 6, 0].

[0080] S3: Decoding the encoding sequence according to the first Huffman tree to obtain a first encrypted sequence, obtaining the regular variation degree of the first encrypted sequence, and in response to the regular variation degree being greater than a preset degree threshold, storing the first encrypted data sequence as a final encrypted sequence.

[0081] It should be noted that, in order to ensure the encryption effect of the human resource data while saving the encryption time, the encryption effect of the obtained encrypted data needs to be evaluated after each encryption is completed. Thus, the encryption is controlled according to the encryption evaluation result.

[0082] S30: Obtaining the regular variation degree of the first encrypted sequence.

[0083] Preferably, as an example, the regular variation degree of the first encrypted sequence is obtained, including:

[0084] ;

[0085] wherein data with the same value is taken as a category, the position of each data of each category in the human resource data sequence is recorded as a pre-encryption position, the position of each data of each category in the first encrypted data sequence is recorded as a post-encryption position, the post-encryption position closest to the pre-encryption position of each data of each category is taken as a reference post-encryption position of each data of each category among all post-encryption positions of data of the same category, represents the frequency of occurrence of the kth category data in the human resource data sequence, represents the frequency of occurrence of the kth category data in the first encrypted sequence, represents the distance between the pre-encryption position of the jth data of the kth category and the reference post-encryption position, represents the number of data of the kth category, represents the number of data categories of the human resource data sequence, C represents the regular variation degree of the first encrypted sequence, and tanh() represents the hyperbolic tangent function.

[0086] S31: in response to the regular variation degree being greater than the preset degree threshold, storing the first encrypted data sequence as a final encrypted sequence.

[0087] Preferably, as an example, in response to the regular variation degree being greater than the preset degree threshold, storing the first encrypted data sequence as a final encrypted sequence, comprising:

[0088] in response to the regular variation degree being greater than the preset degree threshold, storing the first encrypted data sequence as a final encrypted sequence, and obtaining the number of data participating in the encryption processing in the chaotic sequence, denoted as the key length; storing the final encrypted sequence and the key length.

[0089] The preset degree threshold is taken as 0.8 in this embodiment, and other embodiments can take other values, and this embodiment is not specifically limited.

[0090] S32: in response to the regular variation degree being greater than the preset degree threshold, continuing to perform substitution encryption.

[0091] Preferably, as an example, in response to the regular variation degree being greater than the preset degree threshold, continuing to perform substitution encryption, comprising:

[0092] The second target position is selected from a leaf position in the first Huffman tree, the adhesion of the second target position to other positions is calculated according to the first encryption sequence, the position with the minimum adhesion is selected as the to-be-replaced position from all the other positions, the encoding at the second target position and the to-be-replaced position in the first Huffman tree is kept unchanged, the human resource data at the two positions is replaced to obtain the second Huffman tree, the second encryption sequence is obtained by decoding the encoding sequence using the second Huffman tree, the regular variation degree of the second encryption sequence is calculated, and in response to the regular variation degree being greater than a preset degree threshold, the second encryption sequence is stored as the final encryption sequence.

[0093] It should be noted that the second target position is selected from a leaf position in the first Huffman tree, including:

[0094] The chaotic sequence is obtained.

[0095] The data with the same value in the human resource data sequence is taken as a category, the number of value categories of the data in the human resource data sequence is obtained, and is recorded as a first number, the second positioning data is obtained by adding one to the remainder of the second data in the chaotic sequence and the first number.

[0096] Each leaf of the first Huffman tree is taken as an independent position, the D2th position in the first Huffman tree is obtained as the second target position in the order from top to bottom and from left to right, and D2 represents the second positioning data.

[0097] S4: The final encryption sequence is decrypted to obtain the human resource data sequence.

[0098] Preferably, as an example, the method for decrypting the final encryption sequence to obtain the human resource data sequence, comprising:

[0099] The decrypter obtains the final encryption sequence and the key length. The decrypter generates a chaotic sequence using the Logistic mapping method, and takes a sequence composed of the key length continuous data starting from the first data in the chaotic sequence as a key sequence.

[0100] The last data of the key sequence is added by one with the remainder of the first quantity to obtain first decryption positioning data, and the final encrypted sequence is processed by using a Huffman coding algorithm to obtain a first ciphertext Huffman tree and an encoding sequence of the final encrypted sequence. A V1th position in the first ciphertext Huffman tree is taken as a first decryption position, and V1 represents the first decryption positioning data. The adhesion of the first decryption position to each of other positions is calculated according to the final encrypted sequence, the position with the minimum adhesion to the first decryption position is obtained as a to-be-replaced position of the first decryption position from all other positions in the first ciphertext Huffman tree, the human resource data in the first decryption position of the first ciphertext Huffman tree is replaced with the human resource data in the to-be-replaced position, and the encoding of the first decryption position and the to-be-replaced position is kept unchanged to obtain a second ciphertext Huffman tree.

[0101] The second last data of the key sequence is added by one with the remainder of the first quantity to obtain second decryption positioning data, and a V2th position obtained from the first ciphertext Huffman tree is taken as a second decryption position, V2 represents the second decryption positioning data, the adhesion of the second decryption position to each of other positions is calculated according to the final encrypted sequence, the position with the minimum adhesion to the second decryption position is obtained as a to-be-replaced position of the second decryption position from all other positions in the second ciphertext Huffman tree, the human resource data in the second decryption position of the second ciphertext Huffman tree is replaced with the human resource data in the to-be-replaced position, and the encoding of the second decryption position and the to-be-replaced position is kept unchanged to obtain a third ciphertext Huffman tree.

[0102] In this way, the data replacement in the Huffman tree is completed by using the first data in the key sequence, and the ciphertext Huffman tree obtained after the last replacement is completed is taken as a final Huffman tree. Based on the Huffman coding algorithm, the encoding sequence of the final encrypted sequence is decoded by using the correspondence between the human resource data and the encoding in the final Huffman tree to obtain a human resource data sequence.

[0103] It should be noted that the adhesion of each two positions is not changed no matter how the human resource data in each position in the Huffman tree is replaced, and thus the adhesion of each two positions does not need to be transmitted when encryption is performed, and thus data leakage in the data transmission process can be avoided.

[0104] The embodiment of the application further discloses a human resource data management system based on big data, comprising a processor and a memory, and the memory stores computer program instructions which realize the human resource data management method based on big data according to the application when executed by the processor.

[0105] The system further comprises a communication bus and a communication interface and other components known to those skilled in the art, and the arrangement and functions of the components are known in the art, and thus are not described herein.

[0106] In this application, the memory can be, for example, a tangible medium including or storing a program or programs that can be used by or in connection with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic storage medium or magnetic-optical storage medium, including but not limited to resistive random access memory, dynamic random access memory, static random access memory, enhanced dynamic access memory, high bandwidth memory, hybrid memory cube, or the like, or any other medium that can be used to store desired information and that can be accessed by an application, module, or both. Any such computer storage media can be part of the device or accessible or connectable thereto.

[0107] While the present application has been illustrated and described in detail in the drawings and foregoing description, such illustration and description is to be considered illustrative or exemplary and not restrictive; the present application is not limited to the disclosed embodiments. Various modifications, changes, and alternatives can become apparent to one of ordinary skill in the art without departing from the spirit and scope of the present application, which is set forth in the following claims.

[0108] The above are only preferred embodiments of the present application, not to limit the protection scope of the present application, therefore: any equivalent changes made on the structure, shape, principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A human resource data management method based on big data, characterized in that, Including the following steps: Obtain human resources data sequences; The human resources data sequence is encoded to obtain an initial Huffman tree and an encoded sequence; a leaf position in the initial Huffman tree is selected as the first target position, and the adhesion between the first target position and other positions is obtained. The adhesion satisfies the following relationship: From all data segments of length i extracted from the human resources data sequence, select the data segment that contains human resources data from both the first target location and any other location. This segment is denoted as the i-ary reference data segment. Within the i-ary reference segment, calculate the average of the nearest distances between the human resources data from the first target location and the human resources data from any other location. This average is denoted as the i-ary metric distance. This represents the i-gram metric distance between the first target location and any other location. The number of i-ary reference data segments is represented by N, the preset cutoff length of the reference data segments is represented by L, and the adhesion between the first target position and any other position is represented by L. The position with the least adhesion among all other positions is selected as the position to be replaced. The encodings at the first target position and the position to be replaced in the initial Huffman tree are kept unchanged, and the human resources data at the two positions are replaced to obtain the first Huffman tree. The first encrypted sequence is obtained by decoding the encoded sequence using the first Huffman tree. The degree of regular variation in the first encrypted sequence is then determined, including: ； Specifically, data with the same value are grouped together. The position of each data point in the human resources data sequence for each category is recorded as the pre-encryption position. The position of each data point in the first encrypted data sequence for each category is recorded as the post-encryption position. Among all the post-encryption positions of data in the same category, the post-encryption position closest to the pre-encryption position of each data point in each category is taken as the reference post-encryption position for each data point in each category. This indicates the frequency of the k-th category of data in the human resources data sequence. This indicates the frequency of the k-th category of data in the first encrypted sequence. This represents the distance between the unencrypted position of the j-th data item in the k-th category and the reference encrypted position. This represents the number of data points in the k-th category. The number of data categories in the human resources data sequence is represented by C, which represents the degree of regularity in the first encrypted sequence, and tanh() represents the hyperbolic tangent function. If the degree of regular variation exceeds a preset threshold, the first encrypted data sequence is stored as the final encrypted sequence.

2. The human resource data management method based on big data according to claim 1, characterized in that, The step of selecting a leaf position in the initial Huffman tree as the first target position includes: Obtain the chaotic sequence; Data with the same value in the human resources data sequence are grouped into one category. The number of value categories of data in the human resources data sequence is obtained and recorded as the first quantity. The remainder of the first data in the chaotic sequence and the first quantity is added to one to obtain the first location data. Treat each leaf of the Huffman tree as an independent position, and obtain the D1th position in the initial Huffman tree as the first target position in the order from top to bottom and from left to right, where D1 represents the first positioning data.

3. The human resource data management method based on big data according to claim 1, characterized in that, The method for obtaining the i-ary metric distance includes: The human resources data at the first target location in the initial Huffman tree is denoted as the first target location data. All data in the human resources data sequence with values ​​equal to the first target location data are denoted as the analysis data for the first target location. The human resources data at any other location in the initial Huffman tree are denoted as the reference location data. All data in the human resources data sequence with values ​​equal to the reference location data are denoted as the reference data for the first target location. In the i-ary reference data segment, obtain the distance between each analysis data and the nearest reference data. Use the average distance between all analysis data and the nearest reference data in the i-ary reference data segment as the i-ary metric distance between the first target location and any other location.

4. The human resource data management method based on big data according to claim 2, characterized in that, In response to the condition that the degree of regular variation does not exceed a preset threshold, the following steps are included: In the first Huffman tree, a leaf position is selected as the second target position. The cohesion between the second target position and other positions is calculated based on the first encryption sequence. The position with the least cohesion is selected as the position to be replaced. The encoding at the second target position and the position to be replaced in the first Huffman tree remains unchanged. The human resources data at the two positions are replaced to obtain the second Huffman tree. The second Huffman tree is used to decode the encoded sequence to obtain the second encryption sequence. The degree of regularity change in the second encryption sequence is calculated. If the degree of regularity change is greater than a preset threshold, the second encryption sequence is stored as the final encryption sequence.

5. A human resource data management method based on big data according to claim 4, characterized in that, Select a leaf position in the first Huffman tree as the second target position, including: Obtain the chaotic sequence; Data with the same value in the human resources data sequence are grouped into one category. The number of value categories of data in the human resources data sequence is obtained and recorded as the first quantity. The remainder of the second data in the chaotic sequence and the first quantity is added to one to obtain the second location data. Treat each leaf of the first Huffman tree as an independent position, and obtain the D2th position in the first Huffman tree as the second target position in the order from top to bottom and from left to right, where D2 represents the second positioning data.

6. A human resource data management method based on big data according to claim 5, characterized in that, The first encrypted data sequence is stored as the final encrypted sequence, including: The first encrypted data sequence is used as the final encrypted sequence, and the number of data in the chaotic sequence that participates in the encryption process is obtained and recorded as the key length; the final encrypted sequence and the key length are stored.

7. A human resource data management system based on big data, characterized in that, include: A processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement a human resource data management method based on big data according to any one of claims 1-6.

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