CN117135000B

CN117135000B - POS machine dynamic data remote management method and system

Info

Publication number: CN117135000B
Application number: CN202311401893.6A
Authority: CN
Inventors: 翁锋华; 邹祥永
Original assignee: Shenzhen Dingzhi Communication Co ltd
Current assignee: Shenzhen Dingzhi Communication Co ltd
Priority date: 2023-10-27
Filing date: 2023-10-27
Publication date: 2024-02-02
Anticipated expiration: 2043-10-27
Also published as: CN117135000A

Abstract

The application relates to the technical field of digital data processing, in particular to a POS machine dynamic data remote management method and system, comprising the following steps: firstly, confirming a plurality of abnormal data points according to a preset number of IMF components corresponding to original data, then calculating the distribution of reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, further calculating the initial instability of the reserved derivative points corresponding to the target abnormal data points, confirming the final instability of the original data points corresponding to the target abnormal data points, further confirming the unstable data points, finally carrying out data confusion processing on the original data through the unstable data points, and confirming the confusion data corresponding to the original data, so as to carry out encryption transmission on the confusion data corresponding to the original data.

Description

POS machine dynamic data remote management method and system

Technical Field

The application relates to the technical field of digital data processing, in particular to a POS machine dynamic data remote management method and system.

Background

A POS is an electronic device used in the retail and service industries for processing payment transactions and sales records. POS devices are typically composed of hardware and software that can accept various payment means, such as credit cards, debit cards, cash, mobile payments, and the like. Whereas payment transactions at POS typically involve sensitive information such as bank card numbers, passwords, etc. If the data is intercepted during transmission or the server is hacked, the user information may be leaked, so before the sensitive information is sent, the POS machine encrypts the data first and then sends the encrypted data to the receiving end through the network. After receiving the encrypted data, the receiving end converts the encrypted data back to the original data by using a corresponding decryption algorithm. Thus, even if data is intercepted in the transmission process, the real content cannot be known.

The traditional encryption method generally adopts a symmetric encryption method, such as DES (data encryption standard) and the like, but the traditional encryption method has a simpler encryption mode and longer use time in the industry, so that plaintext is easily inferred through ciphertext, and further the security of the original data of the POS machine is lower during transmission.

Disclosure of Invention

In view of the above, it is necessary to provide a method and a system for remotely managing dynamic data of a POS machine, which improve the complexity of data encryption of the POS machine, further improve the security of the data and reduce the maintenance cost of the data compared with the traditional data encryption and transmission modes of the POS machine.

The first aspect of the application provides a method for remotely managing dynamic data of a POS machine, which is applied to the field of data encryption of the POS machine, and comprises the following steps: confirming a plurality of abnormal data points corresponding to each dividing period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data; calculating the distribution of reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points which are the same as the sequence values of the target abnormal data points in the corresponding IMF components in other IMF components; calculating initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points based on a divided period segment sequence corresponding to the reserved derivative points, wherein the divided period segment sequence corresponding to the reserved derivative points comprises a plurality of data points of other divided period segments with the same sequence value of the reserved derivative points in the corresponding divided period segments; according to the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data point and the reserved derivative point distribution, calculating the final instability of the original data point corresponding to the target abnormal data point to determine whether the original data point corresponding to the target abnormal data point is an unstable data point; and carrying out data confusion processing on the original data through the unstable data points, and confirming the confusion data corresponding to the original data so as to carry out encryption transmission on the confusion data corresponding to the original data.

In one embodiment, the determining, according to a preset number of IMF components corresponding to the original data, a plurality of abnormal data points corresponding to each of the divided period segments in the preset number of IMF components specifically includes: decomposing the original data according to a preset signal decomposition method, and confirming a preset number of IMF components; confirming a corresponding dividing period according to the maximum amplitude of each IMF component in the frequency domain space so as to divide each IMF component into a plurality of dividing period sections; respectively inputting a plurality of divided period segments in each IMF component into a preset abnormal point monitoring algorithm, and confirming an abnormal value corresponding to each data point in the plurality of divided period segments in each IMF component; and comparing the abnormal values corresponding to the data points in the plurality of divided period segments in each IMF component with a preset abnormal threshold value respectively, and confirming the data points larger than the preset abnormal threshold value as abnormal data points.

In one embodiment, the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, where the reserved derivative points refer to abnormal data points in other IMF components, which are the same as the order values of the target abnormal data points in the corresponding IMF components, specifically includes: confirming abnormal data points in other IMF components with the same sequence value as that of the target abnormal data points in the corresponding IMF components as reserved derivative points corresponding to the target abnormal data points; sorting the dividing periods corresponding to the IMF components where the reserved derivative points corresponding to the target abnormal data points are located from small to large, and constructing a periodic sequence of reserved derivative points corresponding to the target abnormal data points; and calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data and the periodic sequence of the reserved derivative points corresponding to the target abnormal data points.

In one embodiment, the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data, and the periodic sequence of reserved derivative points corresponding to the target abnormal data points specifically includes:

wherein,preserving derivative point distribution for target outlier data points, +.>Number of reserved derivative points corresponding to target outlier data points, +.>For the number of IMF components corresponding to the original data, < >>Maximum dividing period of the periodic sequence of reserved derivative points corresponding to the target abnormal data point +.>Minimum dividing period of periodic sequence of reserved derivative points corresponding to target abnormal data points, +.>For the last data value of the periodic sequence of retained derivative points corresponding to the target outlier data point, +.>The first data value of the periodic sequence of derivative points is reserved for the target outlier data point.

In one embodiment, the calculating the initial instability of the number of reserved derivative points corresponding to the target abnormal data point based on the divided period segment sequence corresponding to the number of reserved derivative points, where the divided period segment sequence corresponding to the reserved derivative points includes a number of data points of other divided period segments with the same order value as the reserved derivative points in the corresponding divided period segments specifically includes: constructing a dividing period segment sequence corresponding to the target retaining derivative point based on the data points of other dividing period segments with the same sequence value of the target retaining derivative point of the target abnormal data point in the corresponding dividing period segment; and calculating the initial instability of the target reservation derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the divided period segment sequence corresponding to the target reservation derivative point, the number of divided period segments of the IMF component where the target abnormal data points are located and the variance corresponding to the divided period segment sequence corresponding to the target reservation derivative point.

In one embodiment, the calculating the initial instability of the target reserved derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the divided period segment sequence corresponding to the target reserved derivative point, the number of divided period segments of the IMF component where the target abnormal data point is located, and the variance corresponding to the divided period segment sequence corresponding to the target reserved derivative point specifically includes:

wherein,for the +.>The individual targets retain the initial instability of the derivative point, < >>Is the firstThe number of abnormal data points in the divided periodic segment sequence corresponding to the target reserved derivative points, +.>Indicate->The number of divided period segments of the IMF component in which the target outlier data point is located, +.>Is->The target keeps the variance corresponding to the dividing period segment sequence corresponding to the derivative point.

In one embodiment, the calculating the final instability of the original data point corresponding to the target abnormal data point according to the initial instability and the distribution of the reserved derivative points of the plurality of reserved derivative points corresponding to the target abnormal data point to determine whether the original data point corresponding to the target abnormal data point is an unstable data point specifically includes: based on the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points, constructing an initial instability sequence corresponding to the target abnormal data points; inputting a variance of an initial instability sequence corresponding to the target abnormal data point, a data mean value of all data points and a reserved derivative point distribution into a preset stability calculation formula, and calculating final instability of an original data point corresponding to the target abnormal data point; and comparing the final instability of the original data point corresponding to the target abnormal data point with a preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point.

In one embodiment, the calculating the final instability of the original data point corresponding to the target abnormal data point by inputting a preset stability calculation formula to the variance corresponding to the initial instability sequence corresponding to the target abnormal data point, the data mean of all data points and the distribution of the reserved derivative points specifically includes:

wherein,for the final instability of the original data point corresponding to the target outlier data point, +.>The data mean value of all data points in the initial instability sequence corresponding to the target abnormal data point,/>for the variance corresponding to the initial instability sequence corresponding to the target outlier data point,/for>And reserving derivative point distribution for the corresponding target abnormal data points.

In one embodiment, the comparing the final instability of the original data point corresponding to the target abnormal data point with the preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point specifically includes: when the final instability of the original data point corresponding to the target abnormal data point is larger than a preset stability threshold value, comparing, and confirming that the target abnormal data point is an unstable data point; and when the final instability of the original data point corresponding to the target abnormal data point is smaller than or equal to a preset stability threshold value, comparing, and confirming that the target abnormal data point is a stable data point.

The second aspect of the present application provides a POS machine dynamic data remote management system, applied to the POS machine data encryption field, the system includes: the confirming module is used for confirming a plurality of abnormal data points corresponding to each dividing period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data; the computing module is used for computing the distribution of the reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points which are the same as the sequence values of the target abnormal data points in the corresponding IMF components in other IMF components; the analysis module is used for calculating the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points based on the divided period segment sequences corresponding to the reserved derivative points, wherein the divided period segment sequences corresponding to the reserved derivative points comprise a plurality of data points of other divided period segments with the same sequence value as that of the reserved derivative points in the corresponding divided period segments; the judging module is used for calculating the final instability of the original data point corresponding to the target abnormal data point according to the initial instability and the distribution of the reserved derivative points of a plurality of reserved derivative points corresponding to the target abnormal data point so as to confirm whether the original data point corresponding to the target abnormal data point is an unstable data point or not; and the encryption module is used for carrying out data confusion processing on the original data through the unstable data points, confirming the confusion data corresponding to the original data and carrying out encryption transmission on the confusion data corresponding to the original data.

According to the method, firstly, a plurality of abnormal data points corresponding to each divided period section in the preset number of IMF components are confirmed according to a preset number of IMF components corresponding to original data, then, based on reserved derivative points corresponding to target abnormal data points, reserved derivative point distribution corresponding to the target abnormal data points is calculated, wherein the reserved derivative points refer to abnormal data points with the same sequence value as the target abnormal data points in the corresponding IMF components in other IMF components, then, based on divided period section sequences corresponding to the plurality of reserved derivative points, initial instability of the plurality of reserved derivative points corresponding to the target abnormal data points is calculated, wherein the divided period section sequences corresponding to the reserved derivative points comprise data points of other divided period sections with the same sequence value as the reserved derivative points in the corresponding divided period sections, and according to the initial instability and reserved derivative point distribution of the plurality of reserved derivative points corresponding to the target abnormal data points, final instability of original data points corresponding to the target abnormal data points is calculated, whether the original data points corresponding to the target abnormal data points are unstable data points is finally, the original data points corresponding to the unstable data points are confirmed, and the original data corresponding to the original data are encrypted through the unstable data is processed. The original data is decomposed by a preset number of IMF components, so that the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points and the reserved derivative point distribution are obtained, unstable data points are further obtained, the original data is subjected to data confusion processing by the unstable data points to realize data encryption transmission, and compared with the traditional POS machine data encryption and transmission mode, the complexity of POS machine data encryption is improved, the data safety is further improved, and the data maintenance cost is reduced.

Drawings

Fig. 1 is a flow chart of a method for remotely managing dynamic data of a POS according to an embodiment of the application.

Fig. 2 is a schematic diagram of a first sub-flow of a POS mobile data remote management method according to an embodiment of the application.

Fig. 3 is a schematic diagram of a second sub-flow of a POS mobile data remote management method according to an embodiment of the application.

Fig. 4 is a schematic diagram of a third sub-flow of a POS mobile data remote management method according to an embodiment of the application.

Fig. 5 is a schematic diagram of a fourth sub-flow of a POS mobile data remote management method according to an embodiment of the application.

Fig. 6 is a schematic diagram of a fifth sub-flow of a POS mobile data remote management method according to an embodiment of the application.

Fig. 7 is a block diagram of a remote management system for dynamic data of a POS according to an embodiment of the application.

Detailed Description

In describing embodiments of the present application, words such as "exemplary," "or," "such as," and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," "such as," and the like are intended to present related concepts in a concrete fashion.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, "/" means or, unless otherwise indicated herein. For example, A/B may represent A or B. The term "and/or" in this application is merely an association relationship describing an association object, and means that three relationships may exist. For example, a and/or B may represent: a exists alone, A and B exist simultaneously, and B exists alone. "at least one" means one or more. "plurality" means two or more than two. For example, at least one of a, b or c may represent: seven cases of a, b, c, a and b, a and c, b and c, a, b and c.

It should be further noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings are used for the purpose of describing particular sequences or successes, respectively. The methods disclosed in the embodiments of the present application or the methods illustrated in the flowcharts include one or more steps for implementing the methods, and the execution order of the steps may be interchanged with one another, and some steps may be deleted without departing from the scope of the claims.

It should be noted that a POS is an electronic device used in retail and service industries for processing payment transactions and sales records. POS devices are typically composed of hardware and software that can accept various payment means, such as credit cards, debit cards, cash, mobile payments, and the like. The POS machine has the main functions of:

1. and (3) payment processing: the POS device may read information on the credit or debit card, communicate with the payment gateway, and complete the payment transaction. It can accept different payment modes such as card swiping, card insertion, near Field Communication (NFC) and the like.

2. Sales records: the POS machine can record detailed information of each sale, including commodity names, prices, quantity and the like. These sales records may be used for inventory management, sales analysis, financial reporting, and the like.

3. Printing a receipt: POS machines are typically equipped with printers that print sales receipts to customers. The receipt displays information of the purchased goods, payment method, transaction time, etc.

4. Inventory management: the POS machine can be integrated with an inventory management system, update inventory quantity in real time and automatically reduce inventory quantity in the sales process. This helps merchants to know inventory of goods in time, avoiding backorder or over-stock.

5. Sales reporting and analysis: the POS may generate various sales reports and analyses, such as daily sales totals, best-selling goods, sales trends, etc. Such data may help merchants understand business conditions, make sales strategies and decisions.

That is, the use of the POS machine simplifies the payment and sales process, and improves transaction efficiency and accuracy. It is widely applied to various commercial places such as retail shops, catering industry, hotels, supermarkets and the like. With the increasing of application scenes and modes of POS devices, the corresponding data encryption and transmission work needs to be equipped with a mode with higher security.

The traditional POS machine data encryption method can adopt the following modes: 1. symmetric encryption: the same key is used to encrypt and decrypt the data. Common symmetric encryption algorithms are AES, DES, etc. In POS machines, sensitive data such as credit card numbers, passwords, etc. can be cryptographically protected using symmetric encryption algorithms. 2. Asymmetric encryption: a pair of keys is used, a public key for encrypting data and a private key for decrypting data. Common asymmetric encryption algorithms are RSA, ECC, etc. In a POS machine, an asymmetric encryption algorithm can be used for identity verification and secure communication. 3. Hash function: the mapping of data to hash values of fixed length is irreversible. Common hash functions are MD5, SHA-1, SHA-256, etc. In a POS machine, a hash function may be used to digest sensitive data to verify the integrity of the data. 4. Data Encryption Standard (DES): is a symmetric key encryption algorithm that encrypts data using a 56 bit key. In the POS machine, the data can be encrypted and protected by using a DES algorithm. 5. Advanced encryption standard (Advanced Encryption Standard, AES): is a symmetric key encryption algorithm that encrypts data using a 128-bit, 192-bit or 256-bit key. In the POS machine, the AES algorithm can be used for encrypting and protecting the data.

It should be noted that encryption is only one means of protecting data, and a common encryption method is the above manner, and along with the wide use of technology and the gradual popularization of technical information, the above common encryption method is easy to be broken, so that the security performance of the POS machine data is lower.

The embodiment of the application firstly provides a POS machine dynamic data remote management method which is applied to the POS machine data encryption field, and referring to the attached figure 1, the method comprises the following steps:

s101, confirming a plurality of abnormal data points corresponding to each divided period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data.

The original data are transmission data generated in the transaction process of the POS machine, and the transmission data are preferably transaction amount data. The IMF components refer to a series of eigen-mode functions obtained by decomposing original data through a preset decomposition method, and each IMF component has relevance, so that the data length of each IMF component is the same. For example, the original data is primarily decomposed to obtain a first IMF component, the first IMF component is decomposed again to obtain a second IMF component, the second IMF component is decomposed again to obtain a third IMF component, and the like, so as to obtain a preset number of IMF components, and the number and the length of each IMF component are the same. Each dividing period section in the IMF component refers to dividing the IMF component into a plurality of dividing periods with the same length by a preset rule, and a data section corresponding to the dividing period is the dividing period section. And respectively carrying out anomaly detection on each divided period section in the preset number of IMF components through a preset anomaly detection algorithm to obtain a plurality of abnormal data points corresponding to each divided period section.

The IMF components are eigenmode functions with different frequencies and amplitudes, and each eigenmode function is a monotonic and residual-free vibration mode. Each eigenmode function satisfies the following two conditions: 1. in the time range of the whole signal, the up-and-down fluctuation times of the eigenmode function are equal, namely the number of zero crossing points is equal to the number of extreme points. 2. At any given point in time, the mean of the eigenmode functions is zero. The specific arrangement is only required to achieve the object of the present embodiment with reference to the prior art, and the IMF component is not further limited in this case.

S102, calculating the distribution of reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points which are the same as the sequence values of the target abnormal data points in the corresponding IMF components in other IMF components.

It should be noted that, since the data length of each IMF component is the same, the order values of the data points in each IMF component are in one-to-one correspondence. The data points of the IMF component, which are the same as the sequence value of the data points in the original data, are derived points of the data points in the original data, and the derived points of the data points in the original data, which belong to different data points, are abnormal data points. The reserved derivative point corresponding to the target outlier point refers to an outlier point in the other IMF component that is the same as the order value of the target outlier point in the corresponding IMF component. The order value refers to the position or ranking of each data after a group of data is arranged according to the order of size. The order value may be used to describe the relative position of one data in the entire dataset. That is, an order value is commonly used to describe the ordering relationship of data and to compare the sizes of data, and by calculating the order value, it is possible to determine where a certain data is located throughout the data set, and its relative size in the data set. For example, when the order of the time values of the target abnormal data points in the corresponding IMF components is 18, the order value corresponding to the target abnormal data points is 18, one data point adjacent to the target abnormal data points is 19, and the order value corresponding to one data point adjacent to the target abnormal data points is 19. The distribution of the reserved derivative points corresponding to the target abnormal data points refers to further calculation to obtain calculation parameters for calculating whether the target abnormal data points are unstable data points according to the distribution of the reserved derivative points corresponding to the target abnormal data points in different IMF components.

S103, calculating initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points based on the divided period segment sequences corresponding to the reserved derivative points, wherein the divided period segment sequences corresponding to the reserved derivative points comprise a plurality of data points of other divided period segments with the same sequence value as that of the reserved derivative points in the corresponding divided period segments.

The sequence of the divided period segments corresponding to the reserved derivative points refers to a data sequence formed by data points of other divided period segments with the same sequence value as the reserved derivative points in the divided period segments where the reserved derivative points are located, and the other divided period segments refer to divided period segments which are not the divided period segments corresponding to the reserved derivative points and belong to one IMF component. For example, the order value of the reserved derivative point in the corresponding divided period is 2, and the divided period which is other than the divided period corresponding to the reserved derivative point and belongs to one IMF component is the other divided period, and the data points with the order value of 2 in the other divided period and the reserved derivative point together form the divided period sequence corresponding to the reserved derivative point. The initial instability of the reserved derivative point refers to the stability degree of the reserved derivative point obtained by further calculation according to the parameters corresponding to the divided period segment sequences corresponding to the reserved derivative point.

It should be noted that, when the initial instability of the plurality of reserved derivative points corresponding to the target abnormal data point is greater, the probability that the target abnormal data point is an unstable data point is proved to be greater. And when the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data point is smaller, proving that the target abnormal data point is less likely to be an unstable data point.

S104, calculating final instability of the original data points corresponding to the target abnormal data points according to initial instability and distribution of the reserved derivative points of a plurality of reserved derivative points corresponding to the target abnormal data points so as to confirm whether the original data points corresponding to the target abnormal data points are unstable data points.

And further calculating final instability of the original data points corresponding to the target abnormal data points based on the initial instability and the distribution of the reserved derivative points after acquiring the initial instability and the distribution of the reserved derivative points of a plurality of reserved derivative points corresponding to the target abnormal data points, so as to further confirm whether the target abnormal data points are unstable data points according to the final instability of the original data points corresponding to each target abnormal data point. The final instability of the original data point corresponding to the target abnormal data point refers to the instability degree of the target abnormal data point. Further, after the final instability of the original data point corresponding to the target abnormal data point is obtained, comparing the final instability of the original data point corresponding to the target abnormal data point with a preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point according to a comparison result.

It should be noted that, when the final instability of the original data point corresponding to the target abnormal data point is larger, the larger probability of the original data point corresponding to the target abnormal data point is an unstable data point, and when the final instability of the original data point corresponding to the target abnormal data point is smaller, the larger probability of the original data point corresponding to the target abnormal data point is a stable data point.

S105, performing data confusion processing on the original data through the unstable data points, and confirming the confusion data corresponding to the original data so as to encrypt and transmit the confusion data corresponding to the original data.

After all the unstable data points corresponding to the original data are obtained, performing data confusion processing on the original data through all the unstable data points corresponding to the original data, wherein the data confusion processing can be to fit an unstable data curve according to all the unstable data points corresponding to the original data, and add the data curve corresponding to the original data and the unstable data curve, namely add the data values at the same position to obtain the confusion data. And encrypting the mixed data by a traditional encryption method, preferably a DES encryption method, so as to obtain encrypted data corresponding to the mixed data, thereby realizing encrypted transmission.

It should be noted that the DES encryption method is a symmetric encryption algorithm that uses the same key for encryption and decryption. The encryption process of the DES algorithm can be divided into the following steps:

1. and (3) key generation: 16 sub-keys, each 48 bits long, are generated from the key input by the user.

2. Initial replacement: the input plaintext is rearranged according to a fixed substitution table to obtain a new 64-bit data.

3. Encryption cycle: the initial data is divided into left and right 32-bit portions L0 and R0. Then, 16 rounds of iterative encryption are carried out, and the encryption process of each round is as follows: 1) R (i-1) is copied to L (i). 2) And (3) expanding R (i-1) into 48-bit data through an expansion substitution table, and performing exclusive OR operation with the subkey Ki to obtain new 48-bit data. 3) Dividing the data obtained in the last step into 8 blocks with 6 bits, and replacing each block by a corresponding S box to obtain new 4-bit data. 4) And connecting the 8 4-bit data obtained in the last step, and rearranging the 8-bit data through a fixed substitution table to obtain new 32-bit data. 5) And performing exclusive OR operation on the data obtained in the last step and L (i-1) to obtain new 32-bit data serving as R (i).

4. Reverse initial substitution: and combining the L16 and the R16 obtained by the last round of encryption, and rearranging according to the inverse initial substitution table to obtain a final encryption result.

The specific encryption method may be performed by substituting data with reference to the above steps, and is not limited thereto.

In an embodiment of the present application, referring to fig. 2, S101, the determining, according to a preset number of IMF components corresponding to original data, a plurality of abnormal data points corresponding to each divided period segment in the preset number of IMF components specifically includes:

s201, decomposing the original data according to a preset signal decomposition method, and confirming a preset number of IMF components.

The preset signal decomposition method may be an EMD signal decomposition method, by which the original data is decomposed into a preset number of IMF components.

It should be noted that the EMD signal decomposition method is a signal decomposition method for decomposing an arbitrarily complex signal into a set of eigen-mode functions, i.e. IMF components. The EMD signal decomposition method is an adaptive decomposition method, and does not require a preset decomposition mode or frequency range. The EMD signal decomposition method comprises the following decomposition steps: 1. the extreme points (local maxima and minima) of the original signal are taken as the upper and lower envelopes of the signal. 2. The mean line of the upper envelope and the lower envelope is calculated. 3. The mean line is subtracted from the original signal to obtain the first IMF component. 4. Repeating steps 1 through 3 for the first IMF component until all components satisfying the IMF condition are obtained.

S202, confirming a corresponding dividing period according to the maximum amplitude of each IMF component in the frequency domain space, so as to divide each IMF component into a plurality of dividing period segments.

After all IMF components corresponding to the original data are acquired, each IMF component is converted into a frequency domain space through Fourier transformation, and a corresponding dividing period is confirmed through the maximum amplitude in the frequency domain space, so that each IMF component is divided into a plurality of dividing period segments according to the dividing period. The determining the corresponding dividing period through the maximum amplitude value in the frequency domain space refers to taking the reciprocal of the frequency corresponding to the maximum amplitude value in the frequency domain space as the period of the corresponding IMF component.

S203, respectively inputting a plurality of dividing period segments in each IMF component into a preset abnormal point monitoring algorithm, and confirming an abnormal value corresponding to each data point in the plurality of dividing period segments in each IMF component.

After a plurality of divided period segments in each IMF component are acquired, each divided period segment is taken as an input unit and is input into a preset outlier monitoring algorithm to confirm an outlier corresponding to each data point in each divided period segment. Specifically, the outlier monitoring algorithm may be a LOF algorithm, and the outlier corresponding to each data point may be a LOF value corresponding to each data point.

Note that the LOF algorithm is an unsupervised learning algorithm for anomaly detection. It is based on the idea of density to determine if each data point is an outlier by calculating its local outlier. The basic idea of the LOF algorithm is to calculate, for each data point, the density ratio between it and its surrounding neighbor points. If the density ratio of a data point is small, meaning that the density of neighboring points around it is high, then the data point may be an outlier. If a data point has a higher density ratio, meaning that the density of neighboring points around it is lower, then the data point may be a normal point. Specifically, the LOF algorithm first estimates the local density of each data point by calculating the distance between each data point and its k nearest neighbor points. Then, for each data point, its local outlier factor (LOF value) is calculated as a measure of its degree of anomaly. The larger the LOF value, the more likely the data point is an outlier.

S204, comparing the abnormal values corresponding to the data points in the plurality of divided period sections in each IMF component with a preset abnormal threshold value respectively, and confirming the data points larger than the preset abnormal threshold value as abnormal data points.

After the abnormal value corresponding to each data point in the plurality of divided period segments in each IMF component is obtained, comparing the abnormal value corresponding to each data point with a preset abnormal threshold, confirming the data point larger than the preset abnormal threshold as an abnormal data point, and confirming the data point smaller than or equal to the preset abnormal threshold as a normal data point. Preferably, the preset anomaly threshold value may be 0.7.

In one embodiment of the present application, referring to fig. 3, S102, the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, where the reserved derivative points refer to abnormal data points in other IMF components, where the abnormal data points are the same as the order values of the target abnormal data points in the corresponding IMF components, specifically includes:

s301, confirming abnormal data points in other IMF components with the same sequence value as that of the target abnormal data points in the corresponding IMF components as reserved derivative points corresponding to the target abnormal data points.

The data length of the original data is the same as the data length of the IMF components with the preset number, and the data points in the original data and the IMF components with the preset number have a one-to-one correspondence. And confirming the abnormal data point in other IMF components with the same sequence value as that of the target abnormal data point in the corresponding IMF components as a reserved derivative point corresponding to the target abnormal data point.

It should be noted that, the target abnormal data point is an abnormal data point in the corresponding IMF component, and the abnormal data point in other IMF components with the same sequence value in the corresponding IMF component may be an abnormal data point or a normal data point, and the target abnormal data point is confirmed to be a reserved derivative point, and the normal data point is a derivative point.

S302, sorting the dividing periods corresponding to the IMF components where the reserved derivative points corresponding to the target abnormal data points are located from small to large, and constructing a periodic sequence of the reserved derivative points corresponding to the target abnormal data points.

Wherein, each IMF component corresponds to one division period, and the division period corresponding to each IMF component may be the same or different. And sequentially arranging the dividing periods corresponding to the IMF components where the reserved derivative points corresponding to the target abnormal data points are located in a descending order mode to form a periodic sequence of reserved derivative points corresponding to the target abnormal data points.

S303, calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data and the periodic sequence of the reserved derivative points corresponding to the target abnormal data points.

After the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data and the periodic sequence of reserved derivative points corresponding to the target abnormal data points are obtained, the periodic sequence is used as a calculation parameter, and a preset calculation rule is input to further calculate the reserved derivative point distribution corresponding to the target abnormal data points.

Specifically, the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data, and the periodic sequence of reserved derivative points corresponding to the target abnormal data points specifically includes:

wherein,preserving derivative point distribution for target outlier data points, +.>Number of reserved derivative points corresponding to target outlier data points, +.>For the number of IMF components corresponding to the original data, < >>Maximum dividing period of the periodic sequence of reserved derivative points corresponding to the target abnormal data point +.>Minimum dividing period of periodic sequence of reserved derivative points corresponding to target abnormal data points, +.>For the last data value of the periodic sequence of retained derivative points corresponding to the target outlier data point, +. >The first data value of the periodic sequence of derivative points is reserved for the target outlier data point.

Note that, when the target abnormal data point corresponds to the reserved derivative point distributionThe larger the target outlier data point, the more likely it is an unstable data point. Distribution of remaining derivative points when said target outlier data points correspond +.>The smaller the target outlier data point, the more likely it is a stable data point.

In an embodiment of the present application, referring to fig. 4, S103, the calculating the initial instability of the number of reserved derivative points corresponding to the target abnormal data point based on the divided period segment sequence corresponding to the number of reserved derivative points, where the divided period segment sequence corresponding to the reserved derivative points includes a number of data points of other divided period segments with the same order value as the reserved derivative points in the corresponding divided period segments specifically includes:

s401, constructing a dividing period segment sequence corresponding to the target retaining derivative point based on the data points of other dividing period segments, which are the same as the order value of the target retaining derivative point of the target abnormal data point in the corresponding dividing period segment.

The method comprises the steps of firstly obtaining the sequence value of a target reserved derivative point in a corresponding divided period section, then confirming the data points of other divided period sections which are the same as the sequence value of the target reserved derivative point in the corresponding divided period section, and further constructing a divided period section sequence corresponding to the target reserved derivative point. The other dividing period sections refer to other dividing period sections except the dividing period section corresponding to the target reserved derivative point in the IMF component where the target reserved derivative point is located.

S402, calculating initial instability of the target reserved derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the divided period segment sequence corresponding to the target reserved derivative point, the number of divided period segments of the IMF component where the target abnormal data points are located and the variance corresponding to the divided period segment sequence corresponding to the target reserved derivative point.

After the divided period segment sequence corresponding to the target reserved derivative point is obtained, counting the number of abnormal data points in the divided period segment sequence corresponding to the target reserved derivative point, the number of divided period segments of an IMF component where the abnormal data points are located, and the variance corresponding to the divided period segment sequence corresponding to the target reserved derivative point, and further calculating the initial instability of the target reserved derivative point corresponding to the abnormal data point by taking the three as calculation parameters.

Specifically, the calculating the initial instability of the target reserved derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the divided period segment sequence corresponding to the target reserved derivative point, the number of divided period segments of the IMF component where the target abnormal data point is located, and the variance corresponding to the divided period segment sequence corresponding to the target reserved derivative point specifically includes:

In an embodiment of the present application, referring to fig. 5, S104, according to initial instability and distribution of the reserved derivative points of the plurality of reserved derivative points corresponding to the target abnormal data point, the final instability of the original data point corresponding to the target abnormal data point is calculated to determine whether the original data point corresponding to the target abnormal data point is an unstable data point, which specifically includes:

s501, based on the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points, constructing an initial instability sequence corresponding to the target abnormal data points.

The initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points is calculated respectively, and after the initial instability of all reserved derivative points corresponding to the target abnormal data points is obtained, an initial instability sequence corresponding to the target abnormal data points is built based on the initial instability of all reserved derivative points corresponding to the target abnormal data points.

S502, inputting a variance of an initial instability sequence corresponding to the target abnormal data point, a data mean value of all data points and a reserved derivative point distribution into a preset stability calculation formula, and calculating final instability of the original data point corresponding to the target abnormal data point.

After the initial instability sequence corresponding to the target abnormal data point is obtained, the variance of the initial instability sequence and the data average value of all data points in the initial instability sequence are counted, then the product calculation is carried out on the variance of the initial instability sequence corresponding to the target abnormal data point, the data average value of all data points and the reserved derivative point distribution, and the final instability of the original data point corresponding to the target abnormal data point is further calculated.

Specifically, the step of inputting the variance corresponding to the initial instability sequence corresponding to the target abnormal data point, the data mean value of all data points and the reserved derivative point distribution into a preset stability calculation formula to calculate the final instability of the original data point corresponding to the target abnormal data point specifically includes:

wherein,for the final instability of the original data point corresponding to the target outlier data point, +. >For the data mean value of all data points in the initial instability sequence corresponding to the target abnormal data point, ++>For the variance corresponding to the initial instability sequence corresponding to the target outlier data point,/for>And reserving derivative point distribution for the corresponding target abnormal data points.

S503, comparing the final instability of the original data point corresponding to the target abnormal data point with a preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point.

Specifically, after the final instability of the original data point corresponding to the target abnormal data point is obtained, the final instability of the original data point corresponding to the target abnormal data point is compared with a preset stability threshold value, and whether the target abnormal data point is an unstable data point is confirmed according to a comparison result.

Specifically, referring to fig. 6, the comparing the final instability of the original data point corresponding to the target abnormal data point with the preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point specifically includes:

s601, comparing when the final instability of the original data point corresponding to the target abnormal data point is larger than a preset stability threshold value, and confirming that the original data point corresponding to the target abnormal data point is an unstable data point;

S602, when the final instability of the original data point corresponding to the target abnormal data point is smaller than or equal to a preset stability threshold, comparing, and confirming that the original data point corresponding to the target abnormal data point is a stable data point.

Specifically, after all the unstable data points corresponding to the original data are obtained, data confusion processing is performed on the original data through all the unstable data points corresponding to the original data, and the confusion data corresponding to the original data are confirmed so as to carry out encryption transmission on the confusion data corresponding to the original data.

The embodiment of the application also provides a POS machine dynamic data remote management system, referring to fig. 7, applied to the POS machine data encryption field, the system comprises:

the confirming module 1 is used for confirming a plurality of abnormal data points corresponding to each divided period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data;

the calculating module 2 is configured to calculate, based on a reserved derivative point corresponding to a target abnormal data point, a reserved derivative point distribution corresponding to the target abnormal data point, where the reserved derivative point refers to an abnormal data point in other IMF components, where the abnormal data point is the same as the order value of the target abnormal data point in the corresponding IMF component;

The analysis module 3 is configured to calculate initial instability of a plurality of reserved derivative points corresponding to the target abnormal data point based on a divided periodic segment sequence corresponding to the plurality of reserved derivative points, where the divided periodic segment sequence corresponding to the reserved derivative points includes a plurality of data points of other divided periodic segments with the same order value as the reserved derivative points in the corresponding divided periodic segments;

the judging module 4 is configured to calculate final instability of an original data point corresponding to the target abnormal data point according to initial instability and distribution of a plurality of reserved derivative points corresponding to the target abnormal data point, so as to determine whether the original data point corresponding to the target abnormal data point is an unstable data point;

and the encryption module 5 is used for carrying out data confusion processing on the original data through the unstable data points and confirming the confusion data corresponding to the original data so as to carry out encryption transmission on the confusion data corresponding to the original data.

The implementation process of the POS mobile data remote management system corresponds to the POS mobile data remote management method of the foregoing embodiment one by one, which is not described in detail in this embodiment.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The above-described embodiments of the application are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

Claims

1. A dynamic data remote management method of POS machine, apply to the POS machine data encryption field, characterized by that, the said method includes:

confirming a plurality of abnormal data points corresponding to each dividing period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data;

calculating the distribution of reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points which are the same as the sequence values of the target abnormal data points in the corresponding IMF components in other IMF components;

Calculating initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points based on a divided period segment sequence corresponding to the reserved derivative points, wherein the divided period segment sequence corresponding to the reserved derivative points comprises a plurality of data points of other divided period segments with the same sequence value of the reserved derivative points in the corresponding divided period segments;

according to the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data point and the reserved derivative point distribution, calculating the final instability of the original data point corresponding to the target abnormal data point to determine whether the original data point corresponding to the target abnormal data point is an unstable data point;

carrying out data confusion processing on the original data through the unstable data points, and confirming the confusion data corresponding to the original data so as to carry out encryption transmission on the confusion data corresponding to the original data;

the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data and the periodic sequence of reserved derivative points corresponding to the target abnormal data points specifically comprises the following steps:

Wherein,preserving derivative point distribution for target outlier data points, +.>Number of reserved derivative points corresponding to target outlier data points, +.>For the number of IMF components corresponding to the original data, < >>Maximum dividing period of the periodic sequence of reserved derivative points corresponding to the target abnormal data point +.>Minimum dividing period of periodic sequence of reserved derivative points corresponding to target abnormal data points, +.>For the last data value of the periodic sequence of retained derivative points corresponding to the target outlier data point, +.>Reserving a first data value of a periodic sequence of derivative points for a target outlier data point;

the calculating, based on the divided periodic segment sequences corresponding to the plurality of reserved derivative points, initial instability of the plurality of reserved derivative points corresponding to the target abnormal data point, where the divided periodic segment sequences corresponding to the reserved derivative points include a plurality of data points of other divided periodic segments with the same order value as the reserved derivative points in the corresponding divided periodic segments, specifically includes:

constructing a dividing period segment sequence corresponding to the target retaining derivative point based on the data points of other dividing period segments with the same sequence value of the target retaining derivative point of the target abnormal data point in the corresponding dividing period segment;

Calculating the initial instability of the target reservation derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the divided period segment sequence corresponding to the target reservation derivative point, the number of divided period segments of the IMF component where the target abnormal data points are located and the variance corresponding to the divided period segment sequence corresponding to the target reservation derivative point;

the calculating the initial instability of the target retaining derivative point corresponding to the target abnormal data point according to the number of abnormal data points in the dividing period segment sequence corresponding to the target retaining derivative point, the number of dividing period segments of the IMF component where the target abnormal data points are located, and the variance corresponding to the dividing period segment sequence corresponding to the target retaining derivative point specifically comprises:

wherein,for the +.>The individual targets retain the initial instability of the derivative point, < >>Is->The number of abnormal data points in the divided periodic segment sequence corresponding to the target reserved derivative points, +.>Indicate->The number of divided period segments of the IMF component in which the target outlier data point is located, +.>Is->The target keeps the variance corresponding to the dividing period segment sequence corresponding to the derivative point;

according to the initial instability and the distribution of the reserved derivative points of the plurality of reserved derivative points corresponding to the target abnormal data point, calculating the final instability of the original data point corresponding to the target abnormal data point to determine whether the original data point corresponding to the target abnormal data point is an unstable data point, specifically including:

Based on the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points, constructing an initial instability sequence corresponding to the target abnormal data points;

inputting a variance of an initial instability sequence corresponding to the target abnormal data point, a data mean value of all data points and a reserved derivative point distribution into a preset stability calculation formula, and calculating final instability of an original data point corresponding to the target abnormal data point;

comparing the final instability of the original data point corresponding to the target abnormal data point with a preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point or not;

inputting a variance corresponding to an initial instability sequence corresponding to the target abnormal data point, a data average value of all data points and a reserved derivative point distribution into a preset stability calculation formula, and calculating final instability of an original data point corresponding to the target abnormal data point, wherein the method specifically comprises the following steps:

wherein,for the final instability of the original data point corresponding to the target outlier data point, +.>For the data mean value of all data points in the initial instability sequence corresponding to the target abnormal data point, ++ >For the variance corresponding to the initial instability sequence corresponding to the target outlier data point,/for>Retaining derivative point distribution for the corresponding target abnormal data points;

comparing the final instability of the original data point corresponding to the target abnormal data point with a preset stability threshold value, and determining whether the original data point corresponding to the target abnormal data point is an unstable data point or not, wherein the method specifically comprises the following steps:

when the final instability of the original data point corresponding to the target abnormal data point is larger than a preset stability threshold value, comparing, and confirming that the original data point corresponding to the target abnormal data point is an unstable data point;

and when the final instability of the original data point corresponding to the target abnormal data point is smaller than or equal to a preset stability threshold value, comparing, and confirming that the original data point corresponding to the target abnormal data point is a stable data point.

2. The method for remotely managing dynamic data of POS machine according to claim 1, wherein said identifying a number of abnormal data points corresponding to each divided period segment in a preset number of IMF components according to a preset number of IMF components corresponding to original data specifically includes:

decomposing the original data according to a preset signal decomposition method, and confirming a preset number of IMF components;

Confirming a corresponding dividing period according to the maximum amplitude of each IMF component in the frequency domain space so as to divide each IMF component into a plurality of dividing period sections;

respectively inputting a plurality of divided period segments in each IMF component into a preset abnormal point monitoring algorithm, and confirming an abnormal value corresponding to each data point in the plurality of divided period segments in each IMF component;

and comparing the abnormal values corresponding to the data points in the plurality of divided period segments in each IMF component with a preset abnormal threshold value respectively, and confirming the data points larger than the preset abnormal threshold value as abnormal data points.

3. The method for remotely managing POS machine dynamic data according to claim 2, wherein the calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points in other IMF components, which have the same order value as the target abnormal data points in the corresponding IMF components, specifically includes:

confirming abnormal data points in other IMF components with the same sequence value as that of the target abnormal data points in the corresponding IMF components as reserved derivative points corresponding to the target abnormal data points;

Sorting the dividing periods corresponding to the IMF components where the reserved derivative points corresponding to the target abnormal data points are located from small to large, and constructing a periodic sequence of reserved derivative points corresponding to the target abnormal data points;

and calculating the distribution of the reserved derivative points corresponding to the target abnormal data points based on the number of reserved derivative points corresponding to the target abnormal data points, the number of IMF components corresponding to the original data and the periodic sequence of the reserved derivative points corresponding to the target abnormal data points.

4. The utility model provides a POS machine dynamic data remote management system, is applied to POS machine data encryption field, its characterized in that, the system includes:

the confirming module is used for confirming a plurality of abnormal data points corresponding to each dividing period section in the preset number of IMF components according to the preset number of IMF components corresponding to the original data;

the computing module is used for computing the distribution of the reserved derivative points corresponding to the target abnormal data points based on the reserved derivative points corresponding to the target abnormal data points, wherein the reserved derivative points refer to abnormal data points which are the same as the sequence values of the target abnormal data points in the corresponding IMF components in other IMF components;

the analysis module is used for calculating the initial instability of a plurality of reserved derivative points corresponding to the target abnormal data points based on the divided period segment sequences corresponding to the reserved derivative points, wherein the divided period segment sequences corresponding to the reserved derivative points comprise a plurality of data points of other divided period segments with the same sequence value as that of the reserved derivative points in the corresponding divided period segments;

The judging module is used for calculating the final instability of the original data point corresponding to the target abnormal data point according to the initial instability and the distribution of the reserved derivative points of a plurality of reserved derivative points corresponding to the target abnormal data point so as to confirm whether the original data point corresponding to the target abnormal data point is an unstable data point or not;

the encryption module is used for carrying out data confusion processing on the original data through the unstable data points, confirming the confusion data corresponding to the original data and carrying out encryption transmission on the confusion data corresponding to the original data;

wherein,preserving derivative point distribution for target outlier data points, +.>Number of reserved derivative points corresponding to target outlier data points, +.>For the number of IMF components corresponding to the original data, < >>Maximum dividing period of the periodic sequence of reserved derivative points corresponding to the target abnormal data point +. >Minimum dividing period of periodic sequence of reserved derivative points corresponding to target abnormal data points, +.>For the last data value of the periodic sequence of retained derivative points corresponding to the target outlier data point, +.>Reserving a first data value of a periodic sequence of derivative points for a target outlier data point;

wherein,abnormality for the targetFinal instability of the original data point corresponding to the data point, +.>For the data mean value of all data points in the initial instability sequence corresponding to the target abnormal data point, ++>For the variance corresponding to the initial instability sequence corresponding to the target outlier data point,/for>Retaining derivative point distribution for the corresponding target abnormal data points;