CN116305224A - User order information storage system based on platform data - Google Patents

User order information storage system based on platform data Download PDF

Info

Publication number
CN116305224A
CN116305224A CN202310586447.0A CN202310586447A CN116305224A CN 116305224 A CN116305224 A CN 116305224A CN 202310586447 A CN202310586447 A CN 202310586447A CN 116305224 A CN116305224 A CN 116305224A
Authority
CN
China
Prior art keywords
data
scrambling
sequence
order
necessary
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310586447.0A
Other languages
Chinese (zh)
Other versions
CN116305224B (en
Inventor
张育麟
王其艳
王太富
李文征
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qingdao Yikaimei Digital Technology Co ltd
Original Assignee
Qingdao Yikaimei Digital Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qingdao Yikaimei Digital Technology Co ltd filed Critical Qingdao Yikaimei Digital Technology Co ltd
Priority to CN202310586447.0A priority Critical patent/CN116305224B/en
Publication of CN116305224A publication Critical patent/CN116305224A/en
Application granted granted Critical
Publication of CN116305224B publication Critical patent/CN116305224B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Security & Cryptography (AREA)
  • Accounting & Taxation (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioethics (AREA)
  • Software Systems (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Storage Device Security (AREA)

Abstract

The invention relates to the technical field of data processing, in particular to a user order information storage system based on platform data, which comprises: the data preprocessing module is used for acquiring an order data sequence, and scrambling the order data sequence to obtain a data scrambling sequence; the first screening module is used for obtaining the scrambling necessary degree of the order data according to the quantity of different order data in the order data sequence, and screening the order data to obtain first necessary data and second necessary data; the second screening module is used for obtaining the scrambling attention degree of the second necessary data according to the quantity of the second necessary data, and screening the second necessary data to obtain first attention data and second attention data; the data analysis module is used for acquiring the evaluation index of the data scrambling sequence, and screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence. The optimal data scrambling sequence obtained by the invention has the optimal scrambling encryption effect.

Description

User order information storage system based on platform data
Technical Field
The invention relates to the technical field of data processing, in particular to a user order information storage system based on platform data.
Background
The arrival of the Internet age affects the clothing and food residence of people, and is popularized in every corner of people's life; the convenience and advantages of online shopping also drive a large number of consumers to break away from the traditional shopping mode, and the consumers are put into the hot tide of online shopping consumption in a dispute; the number of the orders consumed by the users also shows explosive growth due to the rapid growth of the number of the consumers and the online store registries, so that personal privacy data of the users contained in the orders are at a large risk of being attacked and tampered, and cannot be effectively guaranteed.
However, the conventional method only uses random scrambling operation, the scrambling effect of the data cannot be guaranteed, and the data encryption effect is poor.
Disclosure of Invention
In order to solve the technical problem of poor encryption effect of random scrambling data, the invention aims to provide a user order information storage system based on platform data, and the adopted technical scheme is as follows:
the data preprocessing module is used for acquiring order data of a platform user to form an order data sequence, and scrambling the order data sequence to obtain at least two data scrambling sequences;
the first screening module is used for obtaining the scrambling necessary degree of the order data according to the quantity of different order data in the order data sequence, and screening the order data according to the scrambling necessary degree to obtain first necessary data and second necessary data;
the second screening module is used for obtaining scrambling attention degree of the second necessary data according to the quantity of the second necessary data, and screening the second necessary data according to the scrambling attention degree to obtain first attention data and second attention data;
the data analysis module is used for obtaining an evaluation index of the data scrambling sequence according to the difference condition between the first necessary data, the first concerned data and the second concerned data in each data scrambling sequence and the order data sequence, and screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence.
Preferably, the obtaining the scrambling necessity degree of the order data according to the number of different order data in the order data sequence specifically includes:
counting the occurrence frequency of each different order data in the order data sequence, and calculating the average value of the occurrence frequency of all the order data; for any order data, the difference between the frequency of occurrence of the order data and the average value of the frequency is taken as the scrambling necessity degree of the order data.
Preferably, the scrambling attention degree for obtaining the second necessary data according to the number of the second necessary data is specifically:
any one of the second necessary data is recorded as selected necessary data, the number of the second necessary data equal to the selected necessary data is obtained and recorded as the feature number, and the ratio between the feature number and the total number of all the second necessary data is used as the scrambling attention degree of the selected necessary data.
Preferably, the obtaining the evaluation index of the data scrambling sequence according to the difference between the first necessary data, the first attention data and the second attention data in each data scrambling sequence and the order data sequence specifically includes:
for any one data scrambling sequence, acquiring the number of the data belonging to the same data at the corresponding position of each first necessary data in the data scrambling sequence in the order data sequence, and recording the number as a first number; acquiring the number of the data belonging to the same data at the corresponding position of each first concerned data in the order data sequence in the data scrambling sequence, and recording the number as a second number; acquiring the number of the data of the corresponding position of each second concerned data in the order data sequence in the data scrambling sequence, wherein the data belong to the same data, and recording the number as a third number;
calculating the ratio of the first quantity to the total quantity of all order data in the order data sequence to obtain a first ratio, calculating the ratio of the second quantity to the total quantity of all order data in the order data sequence to obtain a second ratio, and calculating the ratio of the third quantity to the total quantity of all order data in the order data sequence to obtain a third ratio; and carrying out weighted summation on the first ratio, the second ratio and the third ratio to obtain a characteristic sum value, and taking the difference value between the preset value and the characteristic sum value as an evaluation index of the data scrambling sequence.
Preferably, the screening the order data according to the scrambling necessity degree to obtain the first necessary data and the second necessary data specifically includes:
and recording order data corresponding to the scrambling necessity degree larger than or equal to a preset necessary threshold value as second necessary data, and recording order data corresponding to the scrambling necessity degree smaller than the preset necessary threshold value as first necessary data.
Preferably, the screening the second necessary data according to the scrambling attention degree to obtain the first attention data and the second attention data specifically includes:
and recording the second necessary data corresponding to the scrambling attention degree larger than or equal to the preset attention threshold value as second attention data, and recording the second necessary data corresponding to the scrambling attention degree smaller than the preset attention threshold value as first attention data.
Preferably, the step of screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence specifically includes:
and determining the data scrambling sequence corresponding to the maximum value of the evaluation index as an optimal data scrambling sequence.
The embodiment of the invention has at least the following beneficial effects:
according to the invention, firstly, the original order data sequence of the platform user is scrambled to obtain the data scrambling sequence, so that the scrambling encryption effect of the sequence after scrambling processing can be evaluated later, and finally, the sequence with the optimal scrambling encryption effect is obtained. And obtaining the scrambling necessity degree of the order data according to the quantity of different order data in the order data sequence, analyzing the quantity distribution condition of the order data, obtaining the necessity degree of scrambling operation of the order data, screening the data based on the necessity degree of scrambling operation of the order data, and dividing the order data into two data with different necessity degrees. Further, the scrambling attention degree of the second necessary data is obtained according to the quantity of the second necessary data, the second necessary data is analyzed on the basis of the first screening to obtain the attention degree of the second necessary data, the second necessary data is screened on the basis of the attention degree of the second necessary data, and the second necessary data is divided into two data with different attention degrees. Finally, the scrambling encryption effect of the data scrambling sequence is evaluated by combining three scrambling necessity of order data with different degrees, so that the scrambling encryption effect of the finally obtained optimal data scrambling sequence is optimal.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a user order information storage system based on platform data of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to a specific implementation, structure, features and effects of a user order information storage system based on platform data according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 invention belongs.
The following specifically describes a specific scheme of a user order information storage system based on platform data provided by the invention with reference to the accompanying drawings.
Examples:
referring now to FIG. 1, shown is a block diagram illustrating a system for user order information storage based on platform data in accordance with one embodiment of the present invention, the system comprising: the system comprises a data preprocessing module, a first screening module, a second screening module and a data analysis module.
The data preprocessing module is used for acquiring order data of a platform user to form an order data sequence, and scrambling the order data sequence to obtain at least two data scrambling sequences.
Firstly, acquiring user order data information of a current platform, specifically, for a platform providing an API interface website, acquiring user order data information required in the website by calling an API interface of the platform; if the platform is a specific website which does not provide an API interface, the user order data information in the current platform is acquired by using a crawler written by python for processing and analysis, wherein the information in the website and the crawler acquired by calling the API interface of the platform are known techniques and are not described too much.
Because the user order data information needs to be transmitted in a computer system, the user order data information needs to be converted into binary coded data. Specifically, data such as english letters and identification symbols in the user order data information are converted into ASC codes by using an ASC table, and the obtained ASC codes are converted into binary codes. Binary conversion operation is carried out on each character or word in the order data information of the users, binary coding data of the order data information of each user are recorded as the order data of the platform users, and the collected order data of all the users form an order data sequence.
The order data of all users are formed into an order data sequence, and the order data of each user is ordered according to a set rule or randomly to form the order data sequence. And correspondingly storing the original data corresponding to the order data, namely, constructing an original data set by the original data corresponding to the order data of the user.
Because the maximum value of the binary code length after the decimal value conversion of the commonly used characters in the ASC code is 8 bits, in this embodiment, in order to normalize the data length of the binary code data of each character in the data, the bit filling operation is performed on the binary code data with the length of the order data less than 8 bits, so that the lengths of the binary code data corresponding to all the order data are 8 bits. For example, if the ASC code of one character is 12, it is converted into a binary code of 1100, and at this time, the binary code length corresponding to the order data is 4, so that it is necessary to perform zero padding operation before the first digit thereof, that is, the binary code corresponding to the finally obtained order data is 00001100.
Further, scrambling is performed on the order data sequence to obtain at least two data scrambling sequences. In this embodiment, the order data sequence is scrambled and encrypted by using the conventional chaotic mapping, so that a plurality of scrambled data sequences can be obtained, and the scrambled data sequences are recorded as data scrambling sequences.
The Logistic mapping is a classical chaotic mapping algorithm, and the expression is as follows:
Figure SMS_1
。
wherein mu is a controllable parameter,
Figure SMS_2
the nth number in the chaotic sequence is represented. When the coefficient is 3.57<When mu is less than or equal to 4, the system enters a chaotic state and generates [0,1 ]]And (5) iterating the Logistic chaotic mapping model t-1+p times by the chaotic sequence. t is the number of numerical values contained in the order data sequence, so that the first p items of the chaotic sequence are eliminated in order to prevent the chaotic sequence from being similar to the order data sequence to a high degree. At this time, the acquired chaotic sequence is [0,1]The chaos sequence between the two is projected to the interval suitable for the order data sequence, namely the obtained chaos sequenceEach number is multiplied by t and rounded down to obtain a range of intervals [0, t ]]Is a chaotic sequence of (a). Meanwhile, the Logistic mapping is a well-known technology, and only a simple description is given here.
Since the same data exists in the chaotic sequence obtained after the scrambling operation, i.e. the data scrambling sequence, if the data scrambling sequence corresponds to the order data sequence, a one-to-many situation may occur, i.e. a situation in which a data correspondence error occurs, in order to prevent such a situation, the data scrambling sequence is processed by adopting the joseph code in the embodiment. The principle of the joseph code is only briefly introduced here, namely, the data scrambling sequence is encircled into one circle, the value of the first sequence data in the data scrambling sequence at the corresponding position in the order data sequence is placed at the first position of the scrambled sequence, the currently processed value is deleted in the order data sequence, the remaining value in the data scrambling sequence is continuously encircled into one circle, the value of the second sequence data at the corresponding position in the order data sequence is placed at the second position of the scrambled sequence, and so on until all values in the order data sequence are replaced completely.
For example, if the current order data sequence is {27,32,45,17,90}, the corresponding chaotic sequence is {2,1,2,3,1}, the first transformation is that the first value in the chaotic sequence is 2, then the second value 32 in the order data sequence is placed in the first position in the data scrambling sequence, where the order data sequence is {27,45,17,90}, and the data scrambling sequence is {32, -, -, - }, where-indicates that there is no value temporarily at the corresponding position in the sequence. And the second conversion is that the second numerical value in the chaotic sequence is 1, the first numerical value 27 in the order data sequence is placed at the second position of the data scrambling sequence, the order data sequence is {45,17,90}, the data scrambling sequence is {32,27, -, - }, and the like, and the final data scrambling data is {32,27,17,45,90} after the fifth conversion.
It should be noted that, for convenience of understanding, decimal values are used for illustration in this embodiment, and it is to be understood that in this embodiment, the order data are all binary coded data, so when the data scrambling sequence is acquired, the data location transformation is also all binary coded data.
The first screening module is used for obtaining the scrambling necessary degree of the order data according to the quantity of different order data in the order data sequence, and screening the order data according to the scrambling necessary degree to obtain first necessary data and second necessary data.
In the data preprocessing module, binary coded data of data, namely order data, is obtained by preprocessing the user order data information, and uniform data length is set. Further, the order data sequence is scrambled and encrypted by using chaotic mapping, but because the scrambling effect of the data sequence obtained by the method has certain randomness, the scrambling and encrypting effect is poor, and the situation that the similarity between the data sequence obtained after scrambling and the original data sequence is high may occur. Therefore, in the embodiment of the present invention, the adaptive scrambling effect needs to be evaluated according to the frequency characteristic information of the order data in the order data sequence, and for the data with higher frequency, the importance degree is higher and the privacy is easier to be not guaranteed because the data frequently appears in the sequence, so that the data needs to have higher scrambling degree. Finally, the scrambling effect is evaluated in a targeted mode, and the data sequence with the best effect is screened out from the scrambled data sequence obtained through chaotic mapping, so that the aim of better encryption effect is fulfilled.
Based on the above, obtaining the scrambling necessary degree of the order data according to the quantity of different order data in the order data sequence, specifically, counting the occurrence frequency of each different order data in the order data sequence, and calculating the average value of the occurrence frequency of all kinds of order data; for any order data, the difference between the frequency of occurrence of the order data and the average value of the frequency is taken as the scrambling necessity degree of the order data.
In this embodiment, a calculation formula of the scrambling necessity degree of order data can be expressed as:
Figure SMS_3
。
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_4
indicating the level of scrambling necessity of the ith order data in order data sequence c, +.>
Figure SMS_5
Representing the frequency of occurrence of the ith order data in order data sequence c, +.>
Figure SMS_6
Representing the frequency of occurrence of the kth order data in the order data sequence c, +.>
Figure SMS_7
The category number of the numerical value of the order data included in the order data sequence c is represented.
Figure SMS_8
The average value of the occurrence frequency of all order data is represented, the overall balance condition among the occurrence frequencies of different order data in the order data sequence is reflected, and the average value of the occurrence frequencies of different order data in the order data sequence is represented.
Figure SMS_9
The difference between the ith order data and the reference value of the frequency of occurrence of the order data in the order data sequence is reflected, the greater the difference is, the greater the frequency of occurrence of the ith order data is, the greater the overall situation is, the more the order data frequently occurs in the sequence is further, the higher the importance degree is, the higher the scrambling degree is required, and the value of the corresponding scrambling necessary degree is greater.
The scrambling necessity degree of the order data characterizes the necessity degree of the order data for position scrambling, and the larger the scrambling necessity degree of the order data is, the higher the importance degree of the order data is, and the larger the necessity degree of position scrambling is. The smaller the value of the scrambling necessity degree of the order data, the smaller the number of times of occurrence of the order data, the lower the importance degree, and the smaller the necessity of performing position scrambling.
Based on the first and second necessary data, the order data is filtered according to the scrambling necessary degree. Specifically, order data corresponding to a scrambling necessity degree greater than or equal to a preset necessity threshold is recorded as second necessary data, and order data corresponding to a scrambling necessity degree less than the preset necessity threshold is recorded as first necessary data.
In this embodiment, the value of the necessary threshold is 0, and the practitioner can set according to the specific implementation scenario. When the scrambling necessity degree of the order data is smaller than 0, the order data is shown to be less in occurrence times, so that the scrambling necessity of the order data is considered to be less, and the corresponding order data is recorded as first necessary data. When the scrambling necessity degree of the order data is greater than or equal to 0, the fact that the order data frequently appears at the moment is indicated, so that the scrambling necessity of the order data is considered to be large, and the corresponding order data is recorded as second necessary data.
The second screening module is used for obtaining the scrambling attention degree of the second necessary data according to the quantity of the second necessary data, and screening the second necessary data according to the scrambling attention degree to obtain the first attention data and the second attention data.
The first necessary data represents order data with lower necessity for position scrambling, the second necessary data represents order data with higher necessity for position scrambling, and the second necessary data is further analyzed to screen out order data with different importance degrees.
Based on this, the degree of scrambling attention of the second necessary data is obtained from the number of second necessary data, specifically, any one of the second necessary data is recorded as selected necessary data, the number of second necessary data equal to the selected necessary data is obtained as the feature number, and the ratio between the feature number and the total number of all the second necessary data is recorded as the degree of scrambling attention of the selected necessary data.
In this embodiment, assuming that the nth second necessary data is the selected necessary data, a calculation formula of the scrambling attention degree of the selected necessary data may be expressed as:
Figure SMS_10
。
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_11
indicating the scrambling attention of the nth second essential data, +.>
Figure SMS_12
Representing the number of second necessary data equal to the value of the nth second necessary data, that is, feature data corresponding to the nth second necessary data; />
Figure SMS_13
Representing the total amount of all second necessary data.
Figure SMS_14
The larger the value of the selected necessary data is, the more the selected necessary data accounts for the order data with larger importance, the larger the second necessary data is focused, and the larger the corresponding value of the scrambling focused degree is.
The scrambling attention of the second necessary data characterizes the attention of the second necessary data, and the larger the scrambling attention, which means that the second necessary data is more focused, and the scrambling of the second necessary data is more needed. The smaller the scrambling attention, the smaller the second necessary data attention, and the smaller the necessity of scrambling the second necessary data.
Based on the above, the first attention data and the second attention data are obtained by screening the second necessary data according to the scrambling attention degree, specifically, the second necessary data corresponding to the scrambling attention degree larger than or equal to a preset attention threshold is recorded as the second attention data, and the second necessary data corresponding to the scrambling attention degree smaller than the preset attention threshold is recorded as the first attention data.
In this embodiment, the median of the scrambled attention degrees of all the second necessary data is used as the attention threshold, and in other embodiments, the average of the scrambled attention degrees of all the second necessary data may be used as the attention threshold, and the implementer may set according to the specific implementation scenario.
The data analysis module is used for obtaining an evaluation index of the data scrambling sequence according to the difference condition between the first necessary data, the first concerned data and the second concerned data in each data scrambling sequence and the order data sequence, and screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence.
Because of the randomness of the chaotic mapping rule, the positions of data in the generated data scrambling sequence and the order data sequence before scrambling are not changed, and the possibility of poor scrambling encryption effect exists. However, in this embodiment, all order data are classified into different scrambling priorities due to characteristic information such as frequency of occurrence of different order data. For order data with different priorities, the effect requirements before and after scrambling are different, namely, the requirements for whether the positions are changed before and after scrambling are different.
Therefore, the order data with lower priority in the sequence can be selected as far as possible under the condition that the data positions before and after scrambling are unchanged, and the order data with higher priority can be guaranteed to be less in the condition that the data positions before and after scrambling are changed as far as possible. Based on this, the scrambling degree of the plurality of data scrambling sequences obtained by the chaotic map is evaluated.
Based on the above, the evaluation index of the data scrambling sequence is obtained according to the difference condition between the first necessary data, the first attention data and the second attention data in each data scrambling sequence and the order data sequence.
Specifically, for any one data scrambling sequence, acquiring the number of the same data of the corresponding position of each first necessary data in the data scrambling sequence in the order data sequence, and recording the number as a first number; acquiring the number of the data belonging to the same data at the corresponding position of each first concerned data in the order data sequence in the data scrambling sequence, and recording the number as a second number; and acquiring the number of the data of the corresponding position of each second concerned data in the order data sequence in the data scrambling sequence, wherein the number of the data belongs to the same data, and recording the number as a third number.
Calculating the ratio of the first quantity to the total quantity of all order data in the order data sequence to obtain a first ratio, calculating the ratio of the second quantity to the total quantity of all order data in the order data sequence to obtain a second ratio, and calculating the ratio of the third quantity to the total quantity of all order data in the order data sequence to obtain a third ratio; and carrying out weighted summation on the first ratio, the second ratio and the third ratio to obtain a characteristic sum value, and taking the difference value between the preset value and the characteristic sum value as an evaluation index of the data scrambling sequence.
In this embodiment, taking the data scrambling sequence Q as an example for illustration, the calculation formula of the evaluation index of the data scrambling sequence Q can be expressed as:
Figure SMS_15
。
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_16
evaluation index representing data scrambling sequence Q, < >>
Figure SMS_20
The data representing the corresponding position of each first necessary data in the order data sequence c in the data scrambling sequence Q belongs to the same data quantity, namely the first quantity; />
Figure SMS_21
The data representing the corresponding position of each first concerned data in the order data sequence c in the data scrambling sequence Q belongs to the same data quantity, namely a second quantity; />
Figure SMS_18
The data representing the corresponding position of each second concerned data in the order data sequence c in the data scrambling sequence Q belongs to the same data quantity, namely a third quantity; />
Figure SMS_19
Representing the total number of all order data contained in the order data sequence, +.>
Figure SMS_22
、/>
Figure SMS_23
And->
Figure SMS_17
The weights are shown, and in this example, the values are 0.1,0.6 and 0.3, respectively.
Figure SMS_24
Indicating that the first essential data has the same number of duty cycles before and after scrambling +.>
Figure SMS_25
Indicating that the first data of interest has the same number of duty cycles before and after scrambling +.>
Figure SMS_26
Indicating that the second data of interest has the same number of duty cycles at positions before and after scrambling.
The first necessary data represents order data with lower position scrambling necessity, the second necessary data represents order data with higher position scrambling necessity, the first concerned data represents order data with higher position scrambling necessity in the second necessary data, and the second concerned data represents order data with lower position scrambling necessity in the second necessary data, so that the position scrambling necessity of the first necessary data, the second concerned data and the first concerned data is from small to large, and the corresponding weight value is set according to the position scrambling necessity of the data.
The larger the evaluation index value of the data scrambling sequence is, the more the corresponding order data with larger scrambling necessity is, the data positions are changed before and after scrambling, and the situation that the data positions are not changed before and after scrambling the order data with larger scrambling necessity is avoided as much as possible. Based on this, the data scrambling sequence corresponding to the maximum value of the evaluation index is determined as the optimal data scrambling sequence.
And finally, taking the optimal data scrambling sequence as encryption data of the order data sequence, and taking a chaotic sequence corresponding to the optimal data scrambling sequence as a secret key to realize encryption storage of order data information of a user. It should be noted that, the method for acquiring the chaotic sequence corresponding to the data scrambling sequence is a known technique, and will not be described herein too much.
In summary, the invention takes the frequency of each data in the order data of the user as the basis of the necessity of scrambling operation, and evaluates the effect of scrambling operation on the order data, thereby reducing the redundancy of the data to the greatest extent and releasing the memory of the encryption storage while ensuring the higher encryption effect of important high-frequency order data, so as to obtain the encrypted data with better encryption effect.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the scope of the embodiments of the present application, and are intended to be included within the scope of the present application.

Claims (7)

1. A user order information storage system based on platform data, the system comprising:
the data preprocessing module is used for acquiring order data of a platform user to form an order data sequence, and scrambling the order data sequence to obtain at least two data scrambling sequences;
the first screening module is used for obtaining the scrambling necessary degree of the order data according to the quantity of different order data in the order data sequence, and screening the order data according to the scrambling necessary degree to obtain first necessary data and second necessary data;
the second screening module is used for obtaining scrambling attention degree of the second necessary data according to the quantity of the second necessary data, and screening the second necessary data according to the scrambling attention degree to obtain first attention data and second attention data;
the data analysis module is used for obtaining an evaluation index of the data scrambling sequence according to the difference condition between the first necessary data, the first concerned data and the second concerned data in each data scrambling sequence and the order data sequence, and screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence.
2. The system for storing order information of users based on platform data according to claim 1, wherein the scrambling necessity degree of the order data obtained according to the number of different order data in the order data sequence is specifically:
counting the occurrence frequency of each different order data in the order data sequence, and calculating the average value of the occurrence frequency of all the order data; for any order data, the difference between the frequency of occurrence of the order data and the average value of the frequency is taken as the scrambling necessity degree of the order data.
3. The system for storing order information of users based on platform data according to claim 1, wherein the obtaining the scrambling attention degree of the second necessary data according to the amount of the second necessary data is specifically:
any one of the second necessary data is recorded as selected necessary data, the number of the second necessary data equal to the selected necessary data is obtained and recorded as the feature number, and the ratio between the feature number and the total number of all the second necessary data is used as the scrambling attention degree of the selected necessary data.
4. The system of claim 1, wherein the obtaining the evaluation index of the data scrambling sequence according to the difference between each data scrambling sequence and the first necessary data, the first attention data and the second attention data in the order data sequence comprises:
for any one data scrambling sequence, acquiring the number of the data belonging to the same data at the corresponding position of each first necessary data in the data scrambling sequence in the order data sequence, and recording the number as a first number; acquiring the number of the data belonging to the same data at the corresponding position of each first concerned data in the order data sequence in the data scrambling sequence, and recording the number as a second number; acquiring the number of the data of the corresponding position of each second concerned data in the order data sequence in the data scrambling sequence, wherein the data belong to the same data, and recording the number as a third number;
calculating the ratio of the first quantity to the total quantity of all order data in the order data sequence to obtain a first ratio, calculating the ratio of the second quantity to the total quantity of all order data in the order data sequence to obtain a second ratio, and calculating the ratio of the third quantity to the total quantity of all order data in the order data sequence to obtain a third ratio; and carrying out weighted summation on the first ratio, the second ratio and the third ratio to obtain a characteristic sum value, and taking the difference value between the preset value and the characteristic sum value as an evaluation index of the data scrambling sequence.
5. The system for storing user order information based on platform data according to claim 1, wherein the filtering the order data according to the scrambling necessity degree to obtain the first necessary data and the second necessary data specifically comprises:
and recording order data corresponding to the scrambling necessity degree larger than or equal to a preset necessary threshold value as second necessary data, and recording order data corresponding to the scrambling necessity degree smaller than the preset necessary threshold value as first necessary data.
6. The system for storing user order information based on platform data according to claim 1, wherein the filtering the second necessary data according to the scrambled attention degree to obtain the first attention data and the second attention data specifically comprises:
and recording the second necessary data corresponding to the scrambling attention degree larger than or equal to the preset attention threshold value as second attention data, and recording the second necessary data corresponding to the scrambling attention degree smaller than the preset attention threshold value as first attention data.
7. The system for storing user order information based on platform data according to claim 1, wherein the step of screening the data scrambling sequence according to the evaluation index to determine the optimal data scrambling sequence is specifically:
and determining the data scrambling sequence corresponding to the maximum value of the evaluation index as an optimal data scrambling sequence.
CN202310586447.0A 2023-05-24 2023-05-24 User order information storage system based on platform data Active CN116305224B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310586447.0A CN116305224B (en) 2023-05-24 2023-05-24 User order information storage system based on platform data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310586447.0A CN116305224B (en) 2023-05-24 2023-05-24 User order information storage system based on platform data

Publications (2)

Publication Number Publication Date
CN116305224A true CN116305224A (en) 2023-06-23
CN116305224B CN116305224B (en) 2023-07-21

Family

ID=86785449

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310586447.0A Active CN116305224B (en) 2023-05-24 2023-05-24 User order information storage system based on platform data

Country Status (1)

Country Link
CN (1) CN116305224B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07281937A (en) * 1994-04-04 1995-10-27 Fuji Photo Film Co Ltd Data management method and device therefor
CN103632375A (en) * 2013-12-10 2014-03-12 三峡大学 Image scrambling evaluation method and image scrambling evaluation device
WO2022077793A1 (en) * 2020-10-16 2022-04-21 齐鲁工业大学 Image encryption communication algorithm based on two-dimensional lag-complex logistic mapping, and image decryption communication algorithm based on two-dimensional lag-complex logistic mapping
CN115883057A (en) * 2023-03-08 2023-03-31 山东省鲁岳资源勘查开发有限公司 Geographic information mapping system based on remote sensing image
CN115941153A (en) * 2022-08-29 2023-04-07 南通美迪森医药科技有限公司 Safe storage method of drug synthesis data
CN116032464A (en) * 2022-11-01 2023-04-28 江西科技师范大学 Property data encryption system based on quantum communication
CN116074078A (en) * 2023-01-10 2023-05-05 广东浩传管理服务有限公司 Network communication safety monitoring system and method based on data encryption
CN116091175A (en) * 2023-04-10 2023-05-09 南京航空航天大学 Transaction information data management system and method based on big data

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07281937A (en) * 1994-04-04 1995-10-27 Fuji Photo Film Co Ltd Data management method and device therefor
CN103632375A (en) * 2013-12-10 2014-03-12 三峡大学 Image scrambling evaluation method and image scrambling evaluation device
WO2022077793A1 (en) * 2020-10-16 2022-04-21 齐鲁工业大学 Image encryption communication algorithm based on two-dimensional lag-complex logistic mapping, and image decryption communication algorithm based on two-dimensional lag-complex logistic mapping
CN115941153A (en) * 2022-08-29 2023-04-07 南通美迪森医药科技有限公司 Safe storage method of drug synthesis data
CN116032464A (en) * 2022-11-01 2023-04-28 江西科技师范大学 Property data encryption system based on quantum communication
CN116074078A (en) * 2023-01-10 2023-05-05 广东浩传管理服务有限公司 Network communication safety monitoring system and method based on data encryption
CN115883057A (en) * 2023-03-08 2023-03-31 山东省鲁岳资源勘查开发有限公司 Geographic information mapping system based on remote sensing image
CN116091175A (en) * 2023-04-10 2023-05-09 南京航空航天大学 Transaction information data management system and method based on big data

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
徐献灵;崔楠;: "数字图像置乱加密技术综述", 信息网络安全, no. 03 *
郑成勇;: "基于混沌的数字信息置乱方法及应用", 佛山科学技术学院学报(自然科学版), no. 01 *

Also Published As

Publication number Publication date
CN116305224B (en) 2023-07-21

Similar Documents

Publication Publication Date Title
CN111783875B (en) Abnormal user detection method, device, equipment and medium based on cluster analysis
CN108366181A (en) A kind of image encryption method based on hyperchaotic system and multistage scramble
US9619657B2 (en) Method and apparatus for storing redeem code, and method and apparatus for verifying redeem code
CN108416223B (en) Information label encryption method and system based on chaos theory
CN115941189B (en) Encryption method for document data
CN116796354B (en) Information security enhancement method for computer system
Dagadu et al. Medical image encryption scheme based on multiple chaos and DNA coding.
CN111988144B (en) DNA one-time pad image encryption method based on multiple keys
Agarwal An Intelligent Machine Learning Approach for Fraud Detection in Medical Claim Insurance: A Comprehensive Study
CN116305224B (en) User order information storage system based on platform data
CN116566585A (en) Informationized intelligent storage method
Salman et al. Proposed Approach to Sensitive Association Rules Protection based on Compressing and Cryptography:(SARP-CC)
CN116506105A (en) Data theft and tampering prevention encryption method based on block chain
CN111274571A (en) Iris template protection method and system combining local sequencing and negative database
CN115174451B (en) Message encryption detection method, device, equipment and storage medium
CN109559269A (en) A kind of method and terminal of image encryption
US20220358212A1 (en) Surfacing reasons for anomalous multivariate sessions in audit and security logs
CN113382125A (en) Image encryption method based on compressed sensing and RNA coding
CN117174233B (en) Management platform based on health big data
CN117077181B (en) Method for storing enterprise information management data
CN116308367B (en) Safety protection method for electronic commerce system
CN117829837A (en) Data processing method and system for digital currency
CN114330758B (en) Data processing method, device and storage medium based on federal learning
CN117540441B (en) Transaction data secure storage method for hall type cloud terminal
US11816242B2 (en) Log compression and obfuscation using embeddings

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant