CN113360891B - Anti-cheating method based on exercise system and related equipment - Google Patents

Anti-cheating method based on exercise system and related equipment Download PDF

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CN113360891B
CN113360891B CN202110570400.6A CN202110570400A CN113360891B CN 113360891 B CN113360891 B CN 113360891B CN 202110570400 A CN202110570400 A CN 202110570400A CN 113360891 B CN113360891 B CN 113360891B
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user
preset
exercise
file
reporting
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CN113360891A (en
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黄志勇
宋洪强
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Shenzhen Mushroom Wealth Technology Co ltd
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Shenzhen Mushroom Wealth Technology Co ltd
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    • 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
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/45—Structures or tools for the administration of authentication
    • G06F21/46—Structures or tools for the administration of authentication by designing passwords or checking the strength of passwords
    • 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
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • 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
    • G06Q30/018—Certifying business or products
    • G06Q30/0185—Product, service or business identity fraud
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G06Q50/205—Education administration or guidance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2107—File encryption
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2133—Verifying human interaction, e.g., Captcha

Abstract

The invention discloses a cheating prevention method based on an exercise system and related equipment, wherein the method comprises the steps of obtaining identity information of a user and displaying exercise data; when detecting operation data reported by a client according to the exercise data, extracting operation information in the operation data; calculating the current point corresponding to the user according to the operation information; when the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state and supervising the user in the blacklist state. The invention judges whether the user has cheating behaviors according to the operation behaviors of the user, and monitors the subsequent behaviors of the user by changing the states of the user.

Description

Anti-cheating method based on exercise system and related equipment
Technical Field
The invention relates to data processing, in particular to a cheating prevention method based on an exercise system and related equipment.
Background
There are various exercise software in the current society, and users can continuously promote knowledge mastering by repeated exercise in the exercise software. Meanwhile, in order to encourage users to adhere to uninterrupted exercises, many exercise software has introduced a ranking scoring mechanism. By giving a certain rating to the effect of the user on a single exercise and ranking with other users, the user is encouraged to try to exercise.
However, most of the exercise software is obtained from the internet, published books and historical data, so that users can easily cheat through searching in the present day of developed internet searching. For example, a K-wire system, which is a system of investor education class that satisfies the feeling of a training for investor's frying. The system randomly selects a stock and randomly selects N historical daily K line data of the stock to a user for exercise (the first half daily K line data is used as initial data of the system for the user to observe and judge the trend of the K line, the second half daily K line data is used as a playable disc data section), and the user can correspondingly buy, sell and look-aside operations on the next day according to the provided information; and when the exercise is finished, the system collects operation records of the user to calculate the yield of the user, so that corresponding yield points are obtained. When the user exercises, the user can judge through own experience, and related data cheating can be collected on the network. On the one hand, the cheating can hit the exercise enthusiasm of other users, and on the other hand, the cheating is not beneficial to the improvement of the self capacity of the cheating users. In addition, some users design automatic operation programs, so that the users can exercise in the exercise system for a long time, and the aim of brushing is fulfilled. Therefore, there is a need for an effective anti-cheating brush distribution scheme.
Disclosure of Invention
The invention aims to solve the technical problem that the anti-cheating mechanism based on the training system is deficient, and provides an anti-cheating method based on the training system aiming at the deficiency of the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method of preventing cheating based on an exercise system, the method comprising:
acquiring identity information of a user and displaying exercise data;
when detecting operation data reported by a client according to the exercise data, extracting operation information in the operation data;
calculating the current point corresponding to the user according to the operation information;
when the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state and supervising the user in the blacklist state.
The anti-cheating method based on the exercise system, wherein the method further comprises the following steps:
judging whether the user state corresponding to the user is a blacklist state or not according to the identity information;
if yes, sending a preset operation verification code to the client according to the preset blacklist verification times so that the client pops up the operation verification code when the user operates; and/or
Determining whether the user is a new user or not according to the registration time corresponding to the user;
if yes, the operation verification code is sent to the client according to the preset new user verification times, so that the operation verification code can be popped up by the client when the user operates.
The anti-cheating method based on the exercise system, wherein when detecting the operation data reported by the user according to the exercise data, extracting the operation information in the operation data specifically comprises the following steps:
when operation data reported by a user according to the exercise data is detected, decrypting the encrypted file according to a preset encryption algorithm to obtain an encoded file and a signature;
when determining whether the encoded file is a normal file according to the signature, performing reverse encoding on the encoded file in the normal file according to a preset encoding method to obtain an encapsulated file;
and unpacking the packed file to obtain the operation information.
The anti-cheating method based on the exercise system, wherein the method further comprises the following steps:
and when the user state is a blacklist user, determining that the current point corresponding to the user is a first suspicious point in preset suspicious points.
The anti-cheating method based on the exercise system, wherein the calculating the current point corresponding to the user according to the operation information specifically comprises the following steps:
determining the correct times and/or the error times corresponding to the user according to the operation information;
and when the correct time threshold corresponding to the user is larger than a preset correct time threshold, determining the current point corresponding to the user as a second suspicious point in preset suspicious points.
The anti-cheating method based on the exercise system, wherein the method further comprises the following steps:
when the operation information in the operation data cannot be extracted or the format of the package file does not meet the preset format constraint requirement, adding one to the reporting abnormality number corresponding to the user, and sending an abnormality verification code.
The anti-cheating method based on the exercise system, wherein the calculating the current point corresponding to the user according to the operation information specifically comprises the following steps:
and when the reporting abnormal times are larger than a preset reporting abnormal times threshold, determining that the point corresponding to the user is a third suspicious point in preset suspicious points.
The anti-cheating method based on the exercise system, wherein the method further comprises the following steps:
and when the reporting abnormal times are larger than a preset reporting abnormal times threshold, sending a preset abnormal verification code to the client.
A computer readable storage medium storing one or more programs executable by one or more processors to implement steps in the practice system based anti-cheating method as described in any of the above.
A terminal device, comprising: a processor, a memory, and a communication bus; the memory has stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the practice system based anti-cheating method as described in any of the above.
The beneficial effects are that: compared with the prior art, the invention provides an anti-cheating method based on an exercise system and related equipment, wherein the method firstly obtains identity information of a user and displays exercise data; when detecting operation data reported by a client according to the exercise data, extracting operation information in the operation data; calculating the current point corresponding to the user according to the operation information; and determining whether the user is a cheating user according to the current point. By integrating the points obtained by the operation corresponding to the exercise, whether cheating exists or not can be judged for each exercise of the user. When the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state so as to monitor the subsequent exercise operation of the user, for example, prohibiting the user from exercising in a certain time period, or not taking the user into an evaluation range even if the user exercises, so that the effect of preventing the cheating is achieved.
Drawings
Fig. 1 is a first flowchart of an anti-cheating method based on an exercise system according to the present invention.
Fig. 2 is a second flowchart of the anti-cheating method based on the exercise system provided by the invention.
FIG. 3 is a scoring flow chart of the anti-cheating method based on the exercise system provided by the invention.
Fig. 4 is a schematic structural diagram of a terminal device provided by the present invention.
Detailed Description
The invention provides a cheating prevention method based on an exercise system and related equipment, and aims to make the purposes, technical schemes and effects of the invention clearer and more definite. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including 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 unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The inventor finds that the existing exercise system does not effectively aim at supervision of user cheating behaviors, so that an effective anti-cheating brushing scheme is needed.
In order to solve the above problems, in the embodiment of the present invention, identity information of a user is acquired and exercise data is displayed; when detecting operation data reported by a client according to the exercise data, extracting operation information in the operation data; calculating the current point corresponding to the user according to the operation information; when the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state and supervising the user in the blacklist state.
By way of example, embodiments of the present invention may be applied to a conventional practice and brushing system on the market. In this embodiment, a K-wire system is used as an exercise system, and description is made of the implementation process of this embodiment. It will be appreciated that in the above application scenario, the K-wire system is only used to describe a specific implementation of the present solution. In order to improve the stability of the exercise system, the embodiment adopts a Spring Cloud micro-service architecture, a NoSQL document database MongoDB and a distributed cache Redis, and can meet the requirements of large user quantity, high concurrency, large data quantity and the like of Internet application. Meanwhile, the operation record generated by the user exercise is temporarily stored in the client, the user only needs to interact with the back-end service once after the exercise is finished, and the operation experience fluency of the user is greatly enhanced. The embodiment describes a main body scheme of an execution scheme with a server executing a back-end service.
It should be noted that the above application scenario is only shown for the convenience of understanding the present invention, and embodiments of the present invention are not limited in this respect. Rather, embodiments of the invention may be applied to any scenario where applicable.
The invention will be further described by the description of embodiments with reference to the accompanying drawings.
As shown in fig. 1, the present implementation provides a method for preventing cheating based on an exercise system, which may include the steps of:
s10, acquiring identity information of a user and displaying exercise data.
Specifically, as shown in fig. 2, first, a K-wire based strand training system, i.e., a K-wire system, is started. In the K line system, a configuration center is arranged and is mainly used for storing parameters such as system parameters, blacklists, statistical dimension indexes and the like.
The system parameters include: the training environment parameters, the personal information of the user corresponding to each user and the statistical information of the user. The exercise environment parameters refer to exercise parameters defined by each user in the K line system, such as the number of data of the exercise K line, the number of stocks, the daily K line record number of each stock and a K line history database. The user personal information refers to personal information of each user, and includes a user state, a history parameter of the user, and an evaluation parameter. The user state is mainly a state after evaluation according to the previous behavior of the user, and comprises a blacklist state and a whitelist state, wherein the blacklist state indicates that the user has the cheating behavior before, and the whitelist state is opposite. And the user history parameters include the user's history points in the history practice, the number of wins, etc.
When the system is started, the data of the configuration center is read. Since the K-wire system is a simulated stock investment, each simulation requires a certain principal, then according to the user information, determining the corresponding stored point, and deducting the cost point of the connection (for example, the cost point is 200) from the stored point. If the deduction is successful, initializing the exercise environment according to the read system parameters.
The exercise environment comprises randomly selected K line data segments, exercise numbers of the exercise, starting time and the like. The K line data segment can be regarded as a line graph, with time on the abscissa and price on the ordinate.
The K-line data segment selected is a K-line data segment among the K-line data recorded in history stored in the database in advance. In order to reduce K line data segments to be exercised by a user, after the K line data segments are selected, the K line data segments are further screened according to a certain screening rule, the K line data segments with too obvious characteristics are reduced to serve as exercise data, and the user is reduced to search historical K line data according to the characteristics. If the K line data segment with huge long-time fluctuation or drop amplitude is eliminated, for example, the data with continuous 10 days of fluctuation amplitude exceeding 80 percent is eliminated.
And selecting the K line data segment with the previous period of time as exercise data and the K line data segment with the later period of time as simulation data in the selected K line data segment. The corresponding time lengths of the exercise data can be equal or unequal. In this embodiment, the total length of the selected K line data segment is 60 days, the time length corresponding to the exercise data is 30 days, and the time length corresponding to the simulation data is 30 days.
The exercise data is used for being displayed in front of the user, the user can see the exercise data, the user does not know the K line data of the next day, and the time period K line data of which stock is selected is not clear.
The user decides whether the operation of the next day is buying, selling or sightseeing according to the K-line trend graph of the current day. I.e. from day 31 on the user's own accord. Once the user operates, the system displays the day K line data of the next day, and calculates and displays the current yield and income amount of the user until the day K line data in the exercise K line data is displayed.
And returning the stock codes, names and selected time periods corresponding to the selected K-line data segments after the exercise is finished. When the user finishes one exercise, the exercise system can package and report the environmental parameters and the operations done by the user to the server for analysis and processing by the server.
Further, in order to effectively monitor the operation behavior of the user. Before each exercise starts, whether the user state of the user is a blacklist state or not is judged according to the identity information of the user. If yes, the user has cheating behavior before, so that a blacklist verification number is set, and a preset operation verification code is sent to the client according to the blacklist verification number, so that the client pops up the operation verification code when the user operates. After receiving the operation verification code, the client pops up the operation verification code for verification when the user operates. For example, if the number of times of verification of the blacklist is 10, the verification code is popped up for verification in the first 10 operations of the user.
If the user state is the white list state, the registration time of the user is acquired, and whether the user is a new user is determined according to the registration time of the user. A time or condition for evaluating whether it is a new user may be set in advance. For example, the registration time is within one day, the user is a new user; also for example, if this is the first time the user performs an exercise, the user is determined to be a new user. If the user is a new user, in order to reduce the possibility of swiping, setting a new user verification frequency, and sending the operation verification code to the client so that the client pops up the operation verification code when the user operates. For example, the set number of times of verification of the new user is 10 times, and the verification code is popped up for verification in the first 10 times of operation of the user. The operation aimed by the pop-up operation verification code can be all operations or only operations for training.
Further, in the first client reporting manner of this embodiment, the operation of the user and the relevant information of the exercise are directly sent to the server as operation data.
In a second implementation manner of this embodiment, in order to prevent a user from modifying data reported by encapsulation, in this embodiment, when a client performs encapsulation, the following steps are adopted:
and processing the data to be packaged according to the preset format constraint requirements. The present embodiment provides a concise package format, specifically: exercise number start time operation sequence: the ith day-BSG identification; day i+1-BSG identification; day i+2—bsg identification; … day N-BSG identifies |check bits|. An example is |xxx|20200101101052001|0-B; 1-G;2-G;3-S;4-G|40|.
Wherein the starting time is the time when the exercise starts; the operation sequence is to identify each operation of the user and calculate the user income after reporting, the BSG identification refers to the operation of the user on the day, B represents buy (buy), S represents sell (sell), and G represents look (Glance); the check bit is the length of the format specific value and is used for verifying whether the data is complete or not by the data collection and storage center in the subsequent system.
After the package file is obtained, the package file is encrypted again in order to ensure the security of the data. The encryption processing in the present embodiment includes the steps of:
according to a preset coding algorithm, coding the packaging file to obtain a coding file;
and encrypting the coded file to obtain an encrypted file.
The encoding algorithm adopted in this embodiment adopts a base encoding algorithm, and the algorithm adopted for encryption is a DES encryption algorithm. In the encryption process, the encrypted file needs to be signed, and the signature in this embodiment adopts a mode that md5 takes a hash value, that is, md5 (encrypted file+des key). And finally, reporting the encrypted file. The form of the encrypted file is similar to: { "id": "607902bde8724f0012f6dcc8", "encryptData": "mOggK0XYjtLVbNmVL0zh9w … mVl0z", "hashStr": "j394d8adb1e10a92c112ed3e1086b85b" }. The format constraint requirements, the encoding algorithm, the encryption algorithm, and the calculated hash value in this embodiment may be customized according to requirements or may be other types of algorithms, such as UTF8 encoding algorithm, asymmetric encryption algorithm, SHA-1 (Secure Hash Algorithm, secure hash algorithm 1), etc.
And S20, when detecting the operation data reported by the client according to the exercise data, extracting the operation information in the operation data.
Specifically, when operation data reported by the client according to the exercise data is detected, the server extracts the operation data. In the direct reporting scheme adopted in the embodiment, the server only needs to perform direction analysis according to the agreed encapsulation steps.
In the second package reporting mode adopted in this embodiment, the server decrypts the encrypted file according to a preset encryption algorithm to obtain the encoded file and the signature. Based on the signature, it is then determined whether the encoded file is the file for which the signature is intended, i.e., whether the encoded file is likely to be tampered with by another person. If the file to which the signature is directed when the file is encoded, the encoded file is a normal file, so that the encoded file in the normal file is reversely encoded according to a preset encoding method to obtain an encapsulated file. And finally, unpacking the packed file to obtain the operation information. Since this process is substantially the same as the encryption and the like described above, the description thereof will be omitted.
After the package file is obtained, the operation sequence is analyzed, and whether the format of the operation sequence is preset format constraint requirements, such as the type, the position and the like of the mark, are judged. If yes, the parsed encapsulated data is subjected to persistent storage so as to facilitate the subsequent data extraction. Meanwhile, aiming at the situation that the reported encrypted file cannot be analyzed and the operation information is extracted, or the format of the packaged file does not meet the preset format constraint requirement, the operation data reported by the practice is quite possibly tampered abnormal data, the reporting abnormal times corresponding to the user are increased by one, and an abnormal verification code is sent to the client. The client needle displays the exception verification code to determine whether the current operation is performed by the user.
S30, calculating the current integral corresponding to the user according to the operation information.
Specifically, finally, according to the obtained operation information, the integral corresponding to the exercise of the time corresponding to the user is calculated, namely, the integral of the time.
Rules for calculating points are a key means to prevent user cheating. In this embodiment, a plurality of schemes for calculating the present integration, which can be used singly or simultaneously, are provided. The suspicious score is preset, and the suspicious score can be zero score or negative score so as to distinguish the score obtained by suspicious behaviors from the normal score. Different or the same suspicious scores may be set for different calculation modes.
In the first calculation mode of this embodiment, it is determined whether the user state corresponding to the user is a blacklist state, and if so, the current point corresponding to the user is a first suspicious point in preset suspicious points.
In the second calculation mode of this embodiment, according to the operation information, the correct number and/or the error number corresponding to the user are determined;
and when the correct time threshold corresponding to the user is larger than the preset correct time threshold, the point corresponding to the user is a second suspicious point in the preset suspicious points.
Specifically, a threshold for the correct number of times is preset. In general practice, the correct number of times threshold may be adjusted based on the number of exercises of the user, as well as the historical practice results of the user. For example, if a user exercises for the second time, but the accuracy of the previous exercise is 100%, the threshold of the number of times of the third exercise should be set to 100 (assuming that the total number of exercises is 100); if the number of previous exercises performed by the user is 10, the threshold for the number of next exercises may be set to 60, 70, etc.
Therefore, when the correct time threshold corresponding to the user is greater than the preset correct time threshold, the point corresponding to the user is the second suspicious point in the preset suspicious points.
The second suspicious score is a suspicious score preset for the abnormal accuracy rate, and can be zero score or negative score so as to distinguish the score obtained by suspicious behaviors from the normal score.
For the K-wire system, the correct number of times in a single exercise can be determined according to the number of days that the stock rises and the number of days that the stock falls. For the K-wire system, the total number of times in a single exercise can be determined according to the number of days that the stock rises and the number of days that the stock falls. And calculating an ascending number of days threshold according to the ascending number of days in the training simulation data, and calculating a descending number of days according to the descending number of days in the training simulation data. The total number of times is equal to the sum of the rising days and the falling days.
When the user holds and stocks rise, the user is regarded as selecting the correct option, and the correct option is recorded as one-time correct option. When the user sells or does not hold and the stock drops, this is considered that the user makes the correct choice, remembering as a correct choice. Finally, the number of correct options made by the user, i.e. the correct number, is compared with a correct number threshold.
And when the correct time threshold corresponding to the user is larger than the preset correct time threshold, the point corresponding to the user is a second suspicious point in the preset suspicious points.
Further, different correct number thresholds may be set for the number of rising days and the number of falling days, respectively, the rising threshold and the falling threshold. Through multiple experimental comparisons, the rise threshold in this example was 57% of the rise days, and the fall threshold was 37% of the fall days, for the fall days.
In a third implementation manner of this embodiment, when calculating the score, when the reporting number of times is abnormal, that is, the reporting number of times of abnormal reporting is greater than a preset threshold value of reporting number of times of abnormal reporting, it is determined that the score corresponding to the user is a third suspicious score in preset suspicious scores.
In the fourth calculation mode of the present embodiment, a reporting time threshold is preset in order to avoid brushing. When the package file is received, the time of receiving the package file of the user this time is recorded as a first receiving time. And when the next packaged file of the user is received, recording the moment of second receiving as the second receiving moment. And then calculating the reporting interval of the two reports according to the first receiving time and the second receiving time. And comparing the reporting interval with the reporting time threshold, and when the reporting interval is smaller than the reporting time threshold, indicating that the reporting time interval is very short for two times, the possibility of brushing exists, and determining the behavior of the user with abnormal operation frequency.
In addition to calculating the time interval between each report, the reporting frequency is also calculated for a certain time. A reporting frequency threshold is preset, for example, 5 times per minute, 100 times per hour, etc. If the obtained reporting frequency is larger than the reporting frequency threshold, the possibility of brushing exists, and the behavior that the user has abnormal operating frequency is determined.
When the abnormal operation frequency behavior of the user is determined, generating a brush-divided graphic active code and displaying the brush-divided graphic active code to the user for input, and if the user cannot normally input, determining that the score corresponding to the user is a preset fourth suspicious score.
In the fifth calculation mode of the present embodiment, there are also settings for exercise countermeasure in many exercise software, and similar mechanisms exist in the K-wire system. Each time a user gets a winning, a record is made and the winning probability of the user is calculated in a preset monitoring period. And carrying out statistical analysis on the historical winning probability of the user so as to set a winning probability threshold value. If the winning probability of the user is greater than the winning probability threshold, determining that the score corresponding to the user is a fifth suspicious score.
In a sixth calculation mode of the present embodiment, a suspicious profit range corresponding to the present exercise is preset for profit calculation specific to the K-wire system. And if the user yield is in the suspicious range, determining the point as a sixth suspicious point. For example, the range may be-2% or more of the local maximum yield.
When only one calculation mode exists, the determined suspicious point is directly used as the current point. However, in the two or more calculation modes, there are a plurality of values of the present score, but when a certain present score is once present as a suspicious score, the present score corresponding to the user is determined as the suspicious score.
In this embodiment, the suspicious score is set to 0, so once the suspicious score exists, the suspicious score is regarded as not scoring.
If the behavior does not exist, the calculation rule of the integration is a normal scoring rule. The income = [ (1+P1%) × (1+P2%) × … (1+Pn%) -1] ×100%. The input cost is that Pi (i is equal to or greater than 1 and equal to or less than the ratio of the rise and fall of the date K line of the purchase hold) is the ratio of the rise and fall of the date K line of the purchase hold. Such as: user input 100, 25% benefit is obtained, and 125 points should be counted; if a profit (loss) of-25% is obtained, 75 points should be counted.
Further, since the main purpose of cheating and swiping is to make the scoring result false, before scoring, the server sends the graphic live code as the scoring verification code to the client, and the client receives the scoring verification code and displays it to the user. The user inputs verification information according to the scoring verification code and submits the verification information. And according to the preset retry times, the server determines whether the verification information input by the user is consistent with the verification target corresponding to the scoring verification code. If the data are consistent, the data statistics and analysis step is carried out through verification; if not, the scoring process is not performed. The number of retries may be customizable with the aim of reducing the likelihood of machine swipes. In order to improve the reliability of the verification code result, the verification code in the embodiment adopts a graphic active code, wherein the graphic active code is formed by randomly combining a plurality of elements in a database, and random interference points and lines are added.
And S40, when the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state and supervising the user in the blacklist state.
Specifically, when the present score is normal, the user is determined to be in normal exercise behavior.
And when the current point is a suspicious point due to reasons other than the blacklist user corresponding to the user state, determining that the user is a cheating user according to the current point.
As shown in fig. 3, in the first implementation method of the present embodiment, the user is determined to be a suspicious user in a case where the present score is not the suspicious score due to the reason that the user state corresponds to the non-blacklisted user. And when a plurality of suspicious scores are obtained according to the calculation mode, determining whether the user is a cheating user according to a certain blacklist rule. The blacklist rule may include that the number of suspicious points in the current point is greater than a certain threshold, or that all suspicious points are accumulated or weighted to obtain suspicious values. If the suspicious value is larger than the preset suspicious threshold value, determining that the user is a cheating user. In addition, the method can also be determined according to whether the user has a blacklist record or not. For example, the user state corresponding to the user is once in a blacklist state, and once suspicious points appear, the user is determined to be a cheating user.
Based on the above-described anti-cheating method based on the exercise system, the present embodiment provides a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps in the anti-cheating method based on the exercise system as described in the above-described embodiments.
Based on the above anti-cheating method based on the training system, the present invention also provides a terminal device, as shown in fig. 4, which includes at least one processor (processor) 20; a display screen 21; and a memory (memory) 22, which may also include a communication interface (Communications Interface) 23 and a bus 24. Wherein the processor 20, the display 21, the memory 22 and the communication interface 23 may communicate with each other via a bus 24. The display screen 21 is configured to display a user guidance interface preset in the initial setting mode. The communication interface 23 may transmit information. The processor 20 may invoke logic instructions in the memory 22 to perform the methods of the embodiments described above.
Furthermore, the logic instructions in the memory 22 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product.
The memory 22, as a computer readable storage medium, may be configured to store a software program, a computer executable program, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 performs functional applications and data processing, i.e. implements the methods of the embodiments described above, by running software programs, instructions or modules stored in the memory 22.
The memory 22 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the terminal device, etc. In addition, the memory 22 may include high-speed random access memory, and may also include nonvolatile memory. For example, a plurality of media capable of storing program codes such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk or an optical disk, or a transitory computer readable storage medium may be used.
In addition, the specific processes that the computer readable storage medium and the plurality of instruction processors in the terminal device load and execute are described in detail in the above method, and are not stated here.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will 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 spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (4)

1. A method for preventing cheating based on an exercise system, the method comprising:
acquiring identity information of a user, displaying exercise data, and judging whether a user state corresponding to the user is a blacklist state or not according to the identity information;
if yes, sending a preset operation verification code to the client according to the preset blacklist verification times so that the client pops up the operation verification code when the user operates; and/or
Determining whether the user is a new user or not according to the registration time corresponding to the user;
if yes, sending the operation verification code to the client according to the preset new user verification times so that the operation verification code can be popped up by the client when the user operates;
when the user state is a blacklist user, determining that the current point corresponding to the user is a first suspicious point in preset suspicious points;
when operation data reported by a user according to the exercise data is detected, decrypting the encrypted file according to a preset encryption algorithm to obtain an encoded file and a signature;
when determining whether the encoded file is a normal file according to the signature, performing reverse encoding on the encoded file in the normal file according to a preset encoding method to obtain an encapsulated file;
unpacking the packed file to obtain operation information;
determining the correct times and/or the error times corresponding to the user according to the operation information;
when the correct time threshold corresponding to the user is larger than a preset correct time threshold, determining the current point corresponding to the user as a second suspicious point in preset suspicious points;
when the operation information in the operation data cannot be extracted or the format of the packaging file does not accord with the preset format constraint requirement, adding one to the reporting abnormality number corresponding to the user, and sending an abnormality verification code;
when the reporting abnormal times are larger than a preset reporting abnormal times threshold, determining that the point corresponding to the user is a third suspicious point in preset suspicious points;
when the package file is obtained, recording the time of receiving the package file of the user as a first receiving time, and recording the time of receiving the package file of the user for the second time as a second receiving time when the next package file of the user is received;
calculating reporting intervals of the two reports according to the first receiving time and the second receiving time;
comparing the magnitude relation between the reporting interval and a preset reporting time threshold;
when the reporting interval is smaller than the reporting time threshold, determining that the user has abnormal operation frequency, generating a brush-separated graphic active code and displaying the brush-separated graphic active code to the user for input, and if the user cannot normally input, determining that the current point corresponding to the user is a preset fourth suspicious point;
when the user is determined to be the cheating user according to the current point, changing the user state corresponding to the user into a blacklist state and supervising the user in the blacklist state.
2. The anti-cheating method based on an exercise system of claim 1, further comprising:
and when the reporting abnormal times are larger than a preset reporting abnormal times threshold, sending a preset abnormal verification code to the client.
3. A computer readable storage medium storing one or more programs executable by one or more processors to implement the steps in the practice system based anti-cheating method of any one of claims 1-2.
4. A terminal device, comprising: a processor, a memory, and a communication bus, the memory having stored thereon a computer readable program executable by the processor;
the communication bus realizes connection communication between the processor and the memory;
the processor, when executing the computer readable program, implements the steps in the anti-cheating method based on the exercise system as claimed in any one of claims 1-2.
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