A Management System and Method for Anime Design Material Library

Abstract

The present invention discloses a management system and method for an animation design material library. The present invention can automatically detect and record all usage situations of users, can provide a comprehensive evidence chain, and helps copyright holders to safeguard their own rights and interests in legal disputes; the dynamic watermark can be generated in real time according to user behavior, and has higher uniqueness and tracking accuracy; through the comprehensive analysis of the download frequency, usage frequency and sharing frequency by the abnormality and risk assessment algorithm, copyright infringement behaviors can be effectively identified and prevented; the system can more comprehensively evaluate the abnormality and infringement risk of user behavior, significantly improving the effectiveness of copyright protection and the accuracy of infringement risk identification; by real-time monitoring of user behavior and giving timely warnings when potential risks are found, it is ensured that countermeasures can be taken promptly, and the interests of copyright holders can be maximally protected without affecting the user experience, having high practical value and commercial value.

Description

Technical Field

[0001] The present invention relates to the technical field related to animation design, and particularly to an animation design material library management system and method. Background Art

[0002] The animation design material library is an important part of the modern digital creation industry. With the booming development of the animation industry, the design material library has become an important resource for designers and creators to obtain high-quality materials. However, with the extensive use of the material library, the problems of copyright protection and material management have become increasingly prominent.

[0003] Currently, the animation design material library has the following defects: due to the openness of the Internet and the easy dissemination of materials, the phenomenon of unauthorized material dissemination and use is common. Although the traditional static watermark technology can play a certain role in protection to a certain extent, its inherent limitations make the materials still easily tampered with and misused; most of the existing material library management systems lack in-depth analysis and real-time monitoring of user behavior, resulting in difficulty in timely discovering potential infringement behaviors. The lack of effective user behavior analysis means makes the management and copyright protection of the material library more difficult; the traditional material library management system has a lag in the discovery and handling of infringement behaviors, usually relying on post-event manual review and reporting. This method is not only inefficient but also easily misses the best intervention opportunity, causing irreparable losses to the copyright holders. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part as well as in the abstract and title of the present application to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] Therefore, to solve the above technical problems, the present invention provides the following technical solutions: an animation design material library management system, including a database module, a dynamic watermark generation module, a behavior analysis module, and a monitoring and warning module;

[0006] Among them, the database module is used to store all animation design materials, user information, copyright information, and usage records;

[0007] The dynamic watermark generation module is used to dynamically generate watermarks on the corresponding animation design materials according to the usage of users;

[0008] The behavior analysis module is used to identify potential copyright infringement risks by analyzing user behavior patterns;

[0009] The monitoring and warning module is used to monitor the material usage in real time and give early warnings when potential infringement behaviors are found.

[0010] As a preferred solution of the animation design material library management system described in the present invention, the database module includes a watermark detection unit and a data recording unit. The watermark detection unit is used to automatically detect the watermark information in the material every time a user uploads, shares or disseminates the material, and record the usage situation. The data recording unit is used to record the detected watermark information and usage situation in the database each time to form a complete usage record.

[0011] As a preferred solution of the animation design material library management system described in the present invention, the dynamic watermark generation module includes a user authentication unit, a usage record unit, a watermark generation unit and a watermark embedding unit. The user authentication unit is used to obtain the user ID and its permission information by the system after the user logs in to the system. The usage record unit is used to record the request by the system when the user requests to download or use a certain material. The watermark generation unit is used to generate a unique dynamic watermark according to information such as the user ID, request time, IP address, etc. The watermark information includes the user ID, timestamp, usage record, etc. The watermark embedding unit is used to embed the dynamic watermark into the requested animation material to ensure that the watermark information cannot be tampered with and does not affect the visual effect of the material.

[0012] As a preferred solution of the animation design material library management system described in the present invention, the behavior analysis module includes a behavior data collection unit and a pattern analysis unit. The behavior data collection unit is used to continuously collect the user's behavior data through the system. The pattern analysis unit is used to analyze the user behavior data to identify abnormal behavior patterns.

[0013] As a preferred solution of the animation design material library management system described in the present invention, the monitoring and early warning module includes a risk identification unit, a risk early warning unit and a risk prevention unit. The risk identification unit is used to calculate the abnormal behavior risk value according to the identification result of the abnormal behavior pattern, and then judge whether there is a potential copyright infringement risk in the user behavior, and give a risk assessment result. The risk early warning unit is used to send an early warning message in real time to notify the administrator and the copyright holder when the system judges that the user behavior has an infringement risk. The risk prevention unit is used to take risk prevention measures against the infringing user according to the early warning message.

[0014] The present invention also provides a management method for the above-mentioned animation design material library management system, which is characterized in that it includes the following specific steps:

[0015] S1: The system collects and records the relevant information of the user downloading or using a certain material, generates a unique dynamic watermark according to the information, and embeds the dynamic watermark into the requested animation material.

[0016] S2: The system automatically detects the watermark information and usage status in the materials uploaded, shared, or disseminated by the user, and records them in the database to form a complete usage record;

[0017] S3: The system continuously collects the user's behavior data, analyzes the user's behavior data, identifies abnormal behaviors, and then determines whether there is a potential copyright infringement risk in the user's behavior, and gives a risk assessment result;

[0018] S4: When the system determines that there is an infringement risk in the user's behavior, it sends a warning notice to the administrator and the copyright holder in real time, and takes risk prevention measures against users with high risks.

[0019] As a preferred solution of the management method of the animation design material library management system described in the present invention, wherein: in step S3, the steps of identifying abnormal behaviors are as follows:

[0020] S31: Select key indicators in the user's behavior, such as: download frequency D(t), usage frequency U(t), sharing frequency S(t);

[0021] S32: Calculate the abnormality degree of each user behavior pattern. For each behavior pattern index, calculate its deviation degree from the normal behavior pattern;

[0022] Assume that the normal values of each behavior pattern index can be represented by the statistical mean and standard deviation of historical data: the mean and standard deviation of the download frequency are μD and σD respectively, the mean and standard deviation of the usage frequency are μU and σU respectively, and the mean and standard deviation of the sharing frequency are μS and σS respectively;

[0023] Use a standardized method to calculate the abnormality degree of each user behavior pattern: Set the download frequency abnormality degree as A D (t), the usage frequency abnormality degree as A U (t), and the sharing frequency abnormality degree as A S (t), and the calculation formula is as follows:

[0024]

[0025] Among them, σ: represents the time decay coefficient, which controls the speed of time decay; μ: represents the normalization constant of the download frequency;

[0026] S33: Comprehensively calculate the total behavior abnormality degree;

[0027] Integrate the abnormality degrees of each user behavior pattern to calculate the total abnormality degree M(t) of the user behavior pattern;

[0028] Specifically, a weighted summation method is used for calculation, where the weights are determined according to the importance of each behavior, and the calculation formula is as follows:

[0029] M(t) = ω D ·|A D (t)| + ω U ·|A U (t)| + ω S A S (t)

[0030] Wherein, ω D , ω U , ω S are the weight coefficients of each behavior, and ω D + ω U + ω S = 1;

[0031] Formula range: Set the threshold of the user behavior pattern abnormality degree as M threshold , when M(t) ≥ M threshold , it is determined that the user behavior is abnormal.

[0032] As a preferred solution of the management method of the animation design material library management system described in the present invention, wherein: in step S3, when it is determined that the user behavior is abnormal, calculate the evaluation value of the overall infringement risk of the abnormal behavior pattern, and then evaluate the user's infringement risk level;

[0033] Set the following parameters and variables:

[0034] R(t): Risk evaluation value, representing the risk evaluation result of the user behavior at time t; t: Time variable, used for integral calculation; υ: Normalization constant of usage frequency; ω: Normalization constant of sharing frequency; μ: Represents the normalization constant of download frequency;

[0035] The calculation formula is as follows:

[0036] In the above formula, T: Represents the upper limit of integration, representing the evaluation time interval; α: Represents the time decay factor, used to reduce the influence of past behaviors on the current risk evaluation, β: Is a non-linear amplification coefficient, used to adjust the influence degree of the download frequency on the risk; λ 1 , λ 2 , λ 3 : Represents the weight coefficient, used to balance the contributions of download frequency, usage frequency and sharing frequency to the risk evaluation; λ 1 + λ 2 + λ 3 = 1, ensuring that the total contribution of different frequencies is 1;

[0037] e -ατ : Time decay factor, indicating that as time goes by, the influence of earlier behaviors on the current risk evaluation gradually decreases;

[0038] Download frequency term, representing the contribution of the user's download frequency at time t to the risk;

[0039] Usage frequency term, representing the contribution of the user's usage frequency at time t to the risk;

[0040] Download frequency term, representing the sharing frequency of the user at time t;

[0041] Formula value range: Set the risk assessment value threshold to R threshold : When R(t) ≥ R threshold It indicates a high risk of copyright infringement and preventive measures need to be taken; if R(t) < R threshold It indicates that there is no infringement risk in the user's behavior.

[0042] As a preferred solution of the management method of the animation design material library management system described in the present invention, wherein: in practical applications, comprehensively considering the actual impact of abnormal behaviors and the false alarm rate, set reasonable thresholds for M threshold and R threshold to balance accuracy and practicality; specifically, first, according to the statistical distribution of historical data, initially set a value one standard deviation higher than the average as the empirical threshold. Under the initial threshold, calculate and verify the actual data, and observe the capture of abnormal behaviors. If the initial threshold can effectively identify most abnormal behaviors and the false alarm rate is low, then set this threshold as the standard threshold.

[0043] As a preferred solution of the management method of the animation design material library management system described in the present invention, wherein: in step S4, the risk prevention measures are as follows:

[0044] If the value of R(t) is greater than the first threshold and less than the second threshold, then the user's behavior is somewhat abnormal. Take temporary restrictions on the user's access rights, and send a warning of infringement risk to the user, reminding them to comply with the copyright usage regulations until the user's behavior returns to normal; at the same time, strengthen the monitoring of the user's behavior, increase the data collection frequency, and closely observe whether there are further abnormal behaviors;

[0045] If the value of R(t) is greater than the second threshold, freeze the user's account, prevent them from performing further suspicious operations, and notify the user of the freezing reason. At the same time, trigger the content review program to deeply analyze the content uploaded, downloaded, or shared by the user recently to confirm whether there is an infringement act. If it is confirmed that there is a substantial infringement act, send a formal legal notice and inform them of the consequences.

[0046] Advantages of the present invention:

[0047] The present invention can automatically detect and record all usage situations of users, can provide a comprehensive evidence chain, and helps copyright holders to safeguard their own rights and interests in legal disputes; the dynamic watermark can be generated in real time according to user behaviors, and has higher uniqueness and tracking accuracy; through the comprehensive analysis of the download frequency, usage frequency and sharing frequency by the abnormality and risk assessment algorithm, it can effectively identify and prevent copyright infringement behaviors; the system can more comprehensively evaluate the abnormality and infringement risk of user behaviors, significantly improving the effectiveness of copyright protection and the accuracy of infringement risk identification; by monitoring user behaviors in real time and giving early warnings in a timely manner when potential risks are found, it is ensured that corresponding measures can be taken quickly, and the interests of copyright holders can be maximally protected without affecting the user experience, having relatively high practical value and commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0049] Figure 1 It is a schematic diagram of the overall architecture of the management system of the present invention.

[0050] Figure 2 It is a schematic diagram of the overall working process of the management method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification.

[0052] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0053] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0054] Embodiment 1

[0055] Refer to Figures 1-2, which is the first embodiment of the present invention, provides a management system for an animation design material library, including a database module, a dynamic watermark generation module, a behavior analysis module, and a monitoring and early warning module;

[0056] Among them, the database module is used to store all animation design materials, user information, copyright information, and usage records; the database module includes a watermark detection unit and a data recording unit. The watermark detection unit is used to automatically detect the watermark information in the material each time a user uploads, shares, or disseminates the material, and record the usage situation; the data recording unit is used to record the detected watermark information and usage situation in the database each time to form a complete usage record.

[0057] The dynamic watermark generation module is used to dynamically generate watermarks on the corresponding animation design materials according to the user's usage situation; the dynamic watermark generation module includes a user authentication unit, a usage record unit, a watermark generation unit, and a watermark embedding unit. The user authentication unit is used to obtain the user ID and its permission information after the user logs in to the system; the usage record unit is used to record the request when the system requests to download or use a certain material; the watermark generation unit is used to generate a unique dynamic watermark according to information such as the user ID, request time, and IP address. The watermark information includes the user ID, timestamp, usage record, etc.; the watermark embedding unit is used to embed the dynamic watermark into the requested animation material to ensure that the watermark information cannot be tampered with and does not affect the visual effect of the material.

[0058] The behavior analysis module is used to identify potential copyright infringement risks by analyzing the user behavior pattern; the behavior analysis module includes a behavior data collection unit and a pattern analysis unit. The behavior data collection unit is used to continuously collect the user's behavior data through the system; the pattern analysis unit is used to analyze the user behavior data to identify abnormal behavior patterns.

[0059] The monitoring and early warning module is used to monitor the usage situation of the materials in real time and give an early warning when potential infringement behaviors are found; the monitoring and early warning module includes a risk identification unit, a risk early warning unit, and a risk prevention unit. The risk identification unit is used to calculate the abnormal behavior risk value according to the identification result of the abnormal behavior pattern, and then judge whether the user behavior has potential copyright infringement risks, and give a risk assessment result; the risk early warning unit is used to send an early warning message in real time when the system judges that the user behavior has infringement risks, notifying the administrator and the copyright holder; the risk prevention unit is used to take risk prevention measures against the infringing user according to the early warning message.

[0060] The present invention also provides a management method for the above-mentioned management system of the animation design material library, which is characterized in that it includes the following specific steps:

[0061] S1: The system collects and records relevant information on a user's download or use of a certain piece of material, generates a unique dynamic watermark based on the information, and embeds the dynamic watermark into the requested anime material; this can prevent unauthorized dissemination and enable precise tracking; specifically, dynamic watermark technology can effectively prevent the unauthorized dissemination and use of materials. By embedding user-specific information, it can track the flow of materials and enhance copyright protection; different from traditional static watermarks, dynamic watermarks can be generated in real time according to user behavior, with higher uniqueness and tracking accuracy.

[0062] S2: The system automatically detects the watermark information and usage situation in the materials uploaded, shared, or disseminated by the user, and records them in the database to form a complete usage record; the automated detection system can achieve real-time monitoring of the usage situation of materials, ensuring that the copyright owner can promptly discover and respond to potential infringement acts; by recording all usage situations, the system can provide a comprehensive evidence chain, which helps the copyright owner safeguard its own rights and interests in legal disputes.

[0063] S3: The system continuously collects the user's behavior data, analyzes the user behavior data, identifies abnormal behaviors, and then determines whether there are potential copyright infringement risks in the user's behavior, and gives a risk assessment result; by using behavior data analysis and anomaly detection technology, the system can intelligently identify potential infringement risks and reduce the burden of manual monitoring; through timely risk assessment, the system can give early warnings to prevent the expansion and spread of infringement acts;

[0064] The specific steps are as follows:

[0065] S31: Select key indicators in the user's behavior, such as: download frequency D(t), usage frequency U(t), sharing frequency S(t);

[0066] S32: Calculate the anomaly degree of each user behavior pattern. For each behavior pattern indicator, calculate its deviation degree from the normal behavior pattern;

[0067] Assume that the normal values of each behavior pattern indicator can be represented by the statistical mean and standard deviation of historical data: the mean and standard deviation of the download frequency are μD and σD respectively, the mean and standard deviation of the usage frequency are μU and σU respectively, and the mean and standard deviation of the sharing frequency are μS and σS respectively;

[0068] Use a standardized method to calculate the anomaly degree of each user behavior pattern: set the anomaly degree of the download frequency as A D (t), the anomaly degree of the usage frequency as A U (t), and the anomaly degree of the sharing frequency as A S (t), and the calculation formula is as follows:

[0069]

[0070] Among them, σ represents the time decay coefficient, which controls the speed of time decay; μ represents the normalization constant of the download frequency;

[0071] S33: Comprehensively calculate the overall behavior anomaly degree;

[0072] Combine the anomaly degrees of each user behavior pattern to calculate the overall anomaly degree M(t) of the user behavior pattern;

[0073] Specifically, it is calculated by the method of weighted summation, where the weights are determined according to the importance of each behavior. The calculation formula is as follows:

[0074] M(t) = ω D ·|A D (t)| + ω U ·|A U (t)| + ω S ·|A S (t)

[0075] Among them, ω D , ω U , ω S are the weight coefficients of each behavior, and ω D + ω U + ω S = 1;

[0076] Formula range: Set the threshold of the user behavior pattern anomaly degree as M threshold , when M(t) ≥ M threshold , it is determined that the user behavior is abnormal;

[0077] When it is determined that the user behavior is abnormal, within the time interval [0, T], calculate the evaluation value of the overall infringement risk of the user's abnormal behavior pattern, and then evaluate the user's infringement risk level;

[0078] Set the following parameters and variables:

[0079] R(t): Risk evaluation value, representing the risk evaluation result of the user behavior at time t; t: Time variable, used for integral calculation; υ: Normalization constant of the usage frequency; ω: Normalization constant of the sharing frequency; μ: Represents the normalization constant of the download frequency;

[0080] The calculation formula is as follows:

[0081] In the above formula, T: Represents the upper limit of the integral, representing the evaluation time interval; α: Represents the time decay factor, used to reduce the influence of past behaviors on the current risk evaluation, β: Is a non-linear amplification coefficient, used to adjust the influence degree of the download frequency on the risk; λ1 , λ 2 , λ 3 : Represents the weight coefficient, used to balance the contributions of download frequency, usage frequency, and sharing frequency to risk assessment; λ 1 + λ 2 + λ 3 = 1, ensuring that the total contribution of different frequencies is 1;

[0082] e -ατ : The time decay factor, indicating that as time goes by, the impact of earlier behaviors on the current risk assessment gradually decreases;

[0083] The download frequency term, representing the contribution of the user's download frequency at time t to the risk;

[0084] The usage frequency term, representing the contribution of the user's usage frequency at time t to the risk;

[0085] The sharing frequency term, representing the user's sharing frequency at time t;

[0086] Formula value range: Set the risk assessment value threshold as R threshold : When R(t) ≥ R threshold , it indicates a high risk of copyright infringement and preventive measures need to be taken; if R(t) < R threshold , it means that there is no infringement risk in the user's behavior.

[0087] In this solution, M(t) is mainly used for preliminary screening at a single time point. It is a local and instantaneous anomaly measure. By calculating the behavior anomaly degree, the user's behavior pattern is preliminarily screened to quickly determine whether the user's behavior is abnormal at a specific time point. If M(t) does not exceed a certain threshold M threshold , it is considered that the user's behavior is normal and there is no need to further calculate R(t);

[0088] R(t) is used for the overall assessment of the entire time interval. It is a global and comprehensive risk assessment; specifically, after preliminary screening, a detailed risk assessment is carried out on users with abnormal behavior patterns. The calculation of R(t) comprehensively considers the download frequency, usage frequency, sharing frequency, and behavior anomaly degree M(t) within the time interval to obtain the overall risk value;

[0089] In practical applications, considering the actual impact of abnormal behaviors and the false alarm rate, for M threshold and R thresholdSet a reasonable threshold to balance accuracy and practicality. Specifically, first, based on the statistical distribution of historical data, initially set a value one standard deviation higher than the average as the empirical threshold. Under this initial threshold, calculate and verify the actual data, and observe the capture of abnormal behaviors. If this initial threshold can effectively identify most abnormal behaviors and has a low false alarm rate, then set this threshold as the standard threshold.

[0090] S4: When the system determines that there is a risk of infringement in the user's behavior, send a warning notice to the administrator and the copyright holder in real time, and take risk prevention measures against users with high risks. The real-time warning system can notify the relevant parties in the first time to ensure that corresponding measures can be taken quickly. By taking risk prevention measures at different levels, potential infringement behaviors can be effectively controlled to protect the interests of the copyright holders.

[0091] The risk prevention measures are as follows:

[0092] If the value of R(t) is greater than the first threshold and less than the second threshold, then the user's behavior is somewhat abnormal. Take temporary restrictions on the user's access rights, and send a warning of infringement risk to the user to remind them to abide by the copyright usage regulations until the user's behavior returns to normal. At the same time, strengthen the monitoring of the user's behavior, increase the data collection frequency, and closely observe whether there are further abnormal behaviors.

[0093] If the value of R(t) is greater than the second threshold, freeze the user's account to prevent further suspicious operations, and notify the user of the reason for freezing. At the same time, trigger the content review process to deeply analyze the content uploaded, downloaded, or shared by the user recently to confirm whether there is an infringement behavior. If it is confirmed that there is a substantial infringement behavior, send a formal legal notice and inform them of the consequences.

[0094] The present invention proposes an algorithm for abnormal behavior degree and risk assessment, which combines dynamic watermarking, behavioral data analysis, and real-time warning mechanism, and can effectively identify and prevent copyright infringement behaviors. By organically combining multiple technologies such as dynamic watermarking, automatic detection, behavioral data analysis, and real-time warning, a holistic copyright protection solution is formed, significantly improving the practicality and effectiveness of the system. Through refined collection and analysis of behavioral data, the present invention can provide more accurate and detailed risk assessment results to help copyright holders discover and respond to potential risks in a timely manner. Based on real-time warning and multi-level risk prevention measures, the present invention can maximize the protection of the interests of copyright holders without affecting the user experience, and has high practical value and commercial value.

[0095] Embodiment 2

[0096] This is the second embodiment of the present invention. The difference between this embodiment and the first embodiment is that in order to verify the effectiveness of the animation design material library management system and method of the present invention, a group of simulation experiments were designed. The experimental subjects were 100 users, divided into two groups: one group used a traditional animation material management system (control group), and the other group used the animation design material library management system of the present invention (experimental group). The collection and recording of experimental data included the behavior data of users downloading, using, and sharing animation materials within four quarters, and this data included information such as user ID, request time, IP address, etc.

[0097] Control group: The traditional animation material management system was used to manage the animation design materials of 50 users. This system only provided basic functions for recording downloads, usage, and sharing, lacking real-time monitoring and dynamic watermark functions.

[0098] Experimental group: The animation design material library management system of the present invention was used to manage the animation design materials of another 50 users. This system had functions such as dynamic watermark generation, automatic detection, behavior data analysis, real-time monitoring, and early warning.

[0099] The infringement handling situations of the control group and the experimental group are shown in the following table:

[0100]

[0101] From the above data table, it can be seen that the animation design material library management system of the present invention has obvious advantages over the traditional method in terms of the number of infringement handling times. Specifically, in the first quarter, due to the dynamic watermark and real-time monitoring functions of the system of the present invention, infringement behaviors could be identified and processed earlier, resulting in an increase in the number of handling times; in the second quarter, the behavior data analysis module of the system of the present invention played a key role, and through detailed risk assessment, the number of handling times of the system of the present invention further increased; in the third quarter, with the accumulation of user behavior data, the recognition accuracy and processing efficiency of the system of the present invention gradually improved, and the number of handling times was 2 times higher than that of the traditional method; in the fourth quarter, the real-time monitoring and early warning functions of the system of the present invention significantly improved the frequency and timeliness of handling infringement behaviors, and the number of handling times was twice that of the traditional method.

[0102] From the experimental data, it can be seen that the animation design material library management system of the present invention is significantly superior to the traditional method in terms of the number of infringement handling times. This is mainly due to the functions of dynamic watermark generation, automatic detection, behavior data analysis, and real-time monitoring and early warning of the system; these functions cooperate with each other, enabling the system of the present invention to more effectively identify and evaluate the abnormality of user behaviors, reducing the false alarm rate, while improving the recognition accuracy of infringement risks, and being able to discover and handle infringement behaviors more timely and accurately.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A management method for an animation design material library management system, characterized in that: The specific steps include: S1: The system collects and records relevant information about users downloading or using a certain material, generates a unique dynamic watermark based on the information, and embeds the dynamic watermark into the requested animation material; S2: The system automatically detects the watermark information and usage in the materials uploaded, shared or disseminated by users, and records them in the database to form a complete usage record; S3: The system continuously collects user behavior data, analyzes the user behavior data, identifies abnormal behavior, and then determines whether the user behavior has potential copyright infringement risks and gives risk assessment results; S4: When the system determines that a user's behavior poses an infringement risk, it sends a warning notification to the administrator and copyright holder in real time, and takes risk prevention measures for high-risk users; In step S3, the steps of identifying abnormal behavior are as follows: S31: Select key indicators of user behavior: download frequency D(t), usage frequency U(t), sharing frequency S(t); S32: Calculate the abnormality of each user's behavior pattern, and for each behavior pattern indicator, calculate its degree of deviation from the normal behavior pattern; The normal value of each behavior pattern indicator is represented by the statistical mean and standard deviation of historical data: the mean and standard deviation of download frequency are and The mean and standard deviation of the usage frequency are and , the mean and standard deviation of sharing frequency are and ; Use a standardized method to calculate the abnormality of each user's behavior pattern: Set the download frequency abnormality to , the frequency of use is abnormal and the sharing frequency abnormality is , the calculation formula is as follows: 、 、 in, : represents the time decay coefficient, which controls the speed of time decay; : A normalized constant representing the download frequency; S33: Comprehensively calculate the total degree of behavioral abnormality; Combine the abnormality of each user behavior pattern to calculate the abnormality of the total user behavior pattern ; Specifically, the calculation is performed using a weighted summation method, where the weight is determined according to the importance of each behavior. The calculation formula is as follows: in, , , is the weight coefficient of each behavior, and + + =1; Formula value range: Set the threshold of abnormality of user behavior pattern to ,when ≥ , it is judged that the user behavior is abnormal; In step S3, when it is determined that the user behavior is abnormal, the overall infringement risk assessment value of the abnormal behavior pattern is calculated, and then the infringement risk level of the user is assessed; Set the following parameters and variables: : Risk assessment value, which indicates the risk assessment result of user behavior at time t; t: time variable, used for integral calculation; : Normalization constant for usage frequency; : normalization constant of sharing frequency; : A normalized constant representing the download frequency; The calculation formula is as follows: In the above formula, T: represents the upper limit of the integral, which represents the time interval of the evaluation; : represents the time decay factor, which is used to reduce the impact of past behavior on current risk assessment. : is the nonlinear amplification factor, which is used to adjust the impact of download frequency on risk; , , : represents the weight coefficient, which is used to balance the contribution of download frequency, usage frequency and sharing frequency to risk assessment; + + =1, ensuring that the sum of contributions from different frequencies is 1; : Time decay factor, indicating that as time goes by, the impact of earlier behaviors on current risk assessment gradually decreases; : Download frequency item, which indicates the contribution of the user’s download frequency at time t to the risk; : Frequency of use term, which indicates the contribution of the user’s frequency of use at time t to the risk; : Download frequency item, indicating the sharing frequency of the user at time t; Formula value range: Set the risk assessment value threshold to :when ≥ If < , it means that the user's behavior does not have the risk of infringement; Taking into account the actual impact of abnormal behavior and the false alarm rate, and Set a reasonable threshold to balance accuracy and practicality. Specifically, first, based on the statistical distribution of historical data, preliminarily set a value that is one standard deviation higher than the mean as an empirical threshold. Under the preliminary threshold, calculate and verify the actual data and observe the capture of abnormal behaviors. If the preliminary threshold can effectively identify most abnormal behaviors and the false alarm rate is low, then set the threshold as the standard threshold. In step S4, the risk prevention measures are as follows: like When the value of is greater than the first threshold and less than the second threshold, the user behavior is abnormal to a certain extent, and the access rights of the user are temporarily restricted, and an infringement risk warning is sent to the user to remind him to comply with the copyright use regulations until the user behavior returns to normal; at the same time, the user's behavior monitoring is strengthened, the frequency of data collection is increased, and further abnormal behavior is closely observed; like When the value is greater than the second threshold, the user account is frozen to prevent further suspicious operations, and the user is notified of the reason for the freeze. At the same time, the content review procedure is triggered to conduct an in-depth analysis of the content recently uploaded, downloaded or shared by the user to confirm whether there is any infringement. If substantial infringement is confirmed, a formal legal notice is sent and the consequences are informed.

2. The animation design material library management system according to claim 1, characterized in that: The above management method adopts an animation design material library management system, which includes a database module, a dynamic watermark generation module, a behavior analysis module and a monitoring and early warning module; Among them, the database module is used to store all animation design materials, user information, copyright information and usage records; The dynamic watermark generation module is used to dynamically generate watermarks on corresponding animation design materials according to user usage; The behavior analysis module is used to identify potential copyright infringement risks by analyzing user behavior patterns; The monitoring and warning module is used to monitor the use of materials in real time and issue early warnings when potential infringements are discovered.

3. The animation design material library management system according to claim 2, characterized in that: The database module includes a watermark detection unit and a data recording unit. The watermark detection unit is used for the system to automatically detect the watermark information in the material and record the usage every time a user uploads, shares or disseminates the material; the data recording unit is used to record the watermark information and usage detected each time in the database to form a complete usage record.

4. The animation design material library management system according to claim 3, characterized in that: The dynamic watermark generation module includes a user authentication unit, a usage recording unit, a watermark generation unit and a watermark embedding unit. The user authentication unit is used for the system to obtain the user ID and its authority information after the user logs into the system; The usage record unit is used for the system to record the request when the user requests to download or use a certain material; the watermark generation unit is used to generate a unique dynamic watermark according to the user ID, request time, and IP address information, and the watermark information includes the user ID, timestamp, and usage record; the watermark embedding unit is used to embed the dynamic watermark into the requested animation material to ensure that the watermark information cannot be tampered with and does not affect the visual effect of the material.

5. The animation design material library management system according to claim 4, characterized in that: The behavior analysis module includes a behavior data collection unit and a pattern analysis unit. The behavior data collection unit is used to continuously collect user behavior data through the system; the pattern analysis unit is used to analyze user behavior data and identify abnormal behavior patterns.

6. The animation design material library management system according to claim 5, characterized in that: The monitoring and early warning module includes a risk identification unit, a risk early warning unit and a risk prevention unit. The risk identification unit is used to calculate the abnormal behavior risk value based on the abnormal behavior pattern identification result, and then determine whether the user behavior has a potential copyright infringement risk and give a risk assessment result; The risk warning unit is used to send warning information in real time to notify administrators and copyright holders when the system determines that the user's behavior has an infringement risk; The risk prevention unit is used to take risk prevention measures against infringing users according to the early warning information.

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