Set top box user intelligent playing authentication method based on large language model
By using a large language model-based intelligent playback authentication method, the problems of low efficiency and poor security in traditional set-top box authentication methods are solved. This method achieves an adaptive and secure authentication strategy, improving user experience and system operation efficiency.
Patent Information
- Application Number
- CN202511742797.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-25
- Publication Date
- 2026-03-03
AI Technical Summary
Traditional set-top box authentication methods are inefficient, insecure, unable to adapt to content changes, and provide a poor user experience, while neglecting issues of device legitimacy and operating environment.
The system employs a Large Language Model (LLM) for intelligent user playback authentication. By acquiring user subscription information and real-time data, it extracts content permission tags, combines them with risk scores to calculate the final authentication conclusion, conducts multi-dimensional risk assessment, and generates natural language feedback.
It improves the adaptability and security of authentication, reduces manual maintenance costs, enhances user experience and system operation efficiency, and effectively resists device forgery and permission abuse.
Smart Images
Figure CN121603706A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of artificial intelligence technology, and more specifically, to a set-top box user intelligent playback authentication method based on a large language model. Background Technology
[0002] As a crucial component of home terminals, set-top boxes bear the key responsibility of receiving, decoding, and playing multimedia content. To protect content copyright and ensure revenue for operators, playback authentication mechanisms are widely used in set-top box systems to verify users' subscription status and content permissions.
[0003] However, traditional playback authentication methods still have several technical shortcomings that urgently need improvement: First, traditional playback authentication systems typically rely on manually set static rules for access control. When content packages, user rights, or copyright policies are frequently adjusted, manually updating rules is not only inefficient and time-consuming but also prone to rule conflicts or omissions. In severe cases, it may even lead to misjudgments of user permissions, affecting normal playback. Second, existing systems mostly rely on simple matching of user IDs and content IDs, ignoring key risk dimensions such as device legitimacy and user operating environment. They lack comprehensive analysis of device identity and operational behavior, posing significant security risks. Finally, the system is slow to respond to abnormal scenarios, resulting in a noticeable lack of user experience. When authentication fails, the system often returns ambiguous error codes, leaving users unclear about the reason for the failure, impacting platform satisfaction and retention rates.
[0004] Therefore, there is an urgent need for an intelligent playback authentication method that is highly adaptive, interpretable, secure, and compatible with low-computing-power devices, in order to improve playback security, user experience, and system operational efficiency. Summary of the Invention
[0005] The purpose of this invention is to provide a set-top box user intelligent playback authentication method based on a large language model, which can significantly improve playback security, user experience and system operation efficiency, and reduce manual maintenance costs.
[0006] The technical solution of this invention is as follows: Firstly, this application provides a set-top box user intelligent playback authentication method based on a large language model, which includes the following steps: S1. Obtain user order information and real-time data, and perform data preprocessing; S2. Parse the set-top box's program content metadata using an LLM large language model to extract content permission tags; S3. Combine content permission tags with preprocessed user order information to generate preliminary authentication results; S4. Calculate the risk score based on the preprocessed real-time data, and generate the final authentication conclusion based on the preliminary authentication results and the risk score; S5. Based on the final authentication conclusion, conduct multi-dimensional risk assessment and optimization through a large language model to generate natural language feedback information.
[0007] Furthermore, in step S1, the aforementioned real-time data includes IP location, access time, and historical authentication records.
[0008] Furthermore, in step S2, the aforementioned large language model adopts the Transformer architecture and is fine-tuned for the set-top box authentication scenario.
[0009] Furthermore, in step S2, the extraction process of the above-mentioned content permission tags includes: automatically extracting them from the program description using a large language model, and calculating text similarity using a BERT model to match the permission library.
[0010] Furthermore, in step S3, the above-mentioned process of combining content permission tags with preprocessed user subscription information for permission analysis includes combining content permission tags with preprocessed user subscription information and matching the permission library based on text similarity to determine whether the user has the permission to access a specific program, thereby generating a preliminary authentication result.
[0011] Furthermore, in step S4, the process of calculating the risk score based on the preprocessed real-time data includes outlier detection using Z-score and IQR statistical algorithms.
[0012] Furthermore, in step S5, the aforementioned natural language feedback information includes a description of the permission status and operation instructions.
[0013] Secondly, this application provides an electronic device, comprising: Memory, used to store one or more programs; processor; When one or more of the above programs are executed by the above processor, a set-top box user intelligent playback authentication method based on a large language model is implemented as described in any of the first aspects above.
[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a set-top box user intelligent playback authentication method based on a large language model as described in any of the first aspects above.
[0015] Compared with the prior art, the present invention has at least the following advantages or beneficial effects: (1) The present invention provides a set-top box user intelligent playback authentication method based on a large language model. By obtaining user subscription information and real-time data, extracting content permission tags, combining content permission tags with user subscription information to generate preliminary authentication results, calculating risk scores based on real-time data, and generating final authentication conclusions based on preliminary authentication results and risk scores, the statistical algorithms Z-score and IQR are used to evaluate authentication risks in real time, and adaptive authentication strategy adjustment is realized. (2) This invention extracts content permission tags through automated rule generation technology of LLM large language model, which significantly reduces manual maintenance costs; (3) This invention effectively improves the interception rate of illegal authentication attempts through multi-dimensional dynamic risk assessment, and effectively resists security threats such as device forgery and abuse of permissions. Attached Figure Description
[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the steps of a set-top box user intelligent playback authentication method based on a large language model according to the present invention. Figure 2 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.
[0018] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation
[0019] Terminology Explanation: LLM (Large Language Model) is an artificial intelligence model based on deep learning technology, trained on massive amounts of text data. Its core capability is to understand and generate natural language. Z-score: A statistical method commonly used for outlier identification and data standardization. It measures the degree of deviation between a sample value and the population mean, reflecting the relative position of the data in the overall distribution. IQR: An outlier detection method based on statistical quartiles; Transformer architecture: A deep learning model architecture with self-attention mechanism at its core, which has completely changed the way sequence data is processed and has become the technical cornerstone of modern large language models (such as GPT and BERT). BERT (Bidirectional Encoder Representations from Transformers): A modern large language model based on the Transformer architecture.
[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0021] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0023] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0024] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.
[0025] Example 1 Please see Figure 1 , Figure 1 The diagram shows the steps of a set-top box user intelligent playback authentication method based on a large language model, as provided in an embodiment of this application.
[0026] Firstly, this application provides a set-top box user intelligent playback authentication method based on a large language model, which includes the following steps: S1. Obtain user order information and real-time data, and perform data preprocessing; S2. Parse the set-top box's program content metadata using an LLM large language model to extract content permission tags; S3. Combine content permission tags with preprocessed user order information to generate preliminary authentication results; S4. Calculate the risk score based on the preprocessed real-time data, and generate the final authentication conclusion based on the preliminary authentication results and the risk score; S5. Based on the final authentication conclusion, conduct multi-dimensional risk assessment and optimization through a large language model to generate natural language feedback information.
[0027] In a preferred implementation, in step S1, the real-time data includes IP location, access time, and historical authentication records.
[0028] As a preferred implementation, in step S2, the large language model adopts the Transformer architecture and is fine-tuned for the set-top box authentication scenario.
[0029] As a preferred implementation, in step S2, the content permission tag extraction process includes: automatically extracting it from the program description using a large language model, and calculating text similarity using a BERT model to match the permission library.
[0030] As a preferred implementation, step S3, which involves performing permission analysis by combining content permission tags with preprocessed user subscription information, includes combining content permission tags with preprocessed user subscription information and matching the permission library based on text similarity to determine whether the user has permission to access a specific program, thereby generating a preliminary authentication result.
[0031] In a preferred embodiment, step S4, which involves calculating the risk score based on the preprocessed real-time data, includes outlier detection using Z-score and IQR statistical algorithms.
[0032] In a preferred implementation, in step S5, the natural language feedback information includes a description of the permission status and operation instructions.
[0033] Example 2 Please see Figure 2 , Figure 2 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.
[0034] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.
[0035] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.
[0036] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0037] It is understood that the structure shown in the figure is for illustrative purposes only. A set-top box user intelligent playback authentication method based on a large language model may include more or fewer components than shown in the figure, or have a different configuration. The components shown in the figure can be implemented in hardware, software, or a combination thereof.
[0038] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0039] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.
[0040] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0041] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0042] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.
Claims
1. A set-top box user intelligent playback authentication method based on a large language model, characterized in that, Includes the following steps: S1. Obtain user order information and real-time data, and perform data preprocessing; S2. Parse the set-top box's program content metadata using an LLM large language model to extract content permission tags; S3. Combine content permission tags with preprocessed user subscription information to perform permission analysis and generate preliminary authentication results. S4. Calculate the risk score based on the preprocessed real-time data, and generate the final authentication conclusion based on the preliminary authentication results and the risk score; S5. Based on the final authentication conclusion, conduct multi-dimensional risk assessment and optimization through a large language model to generate natural language feedback information.
2. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 1, characterized in that, In step S1, the real-time data includes IP location, access time, and historical authentication records.
3. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 1, characterized in that, In step S2, the large language model adopts the Transformer architecture and is fine-tuned for the set-top box authentication scenario.
4. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 3, characterized in that, In step S2, the extraction process of the content permission tags includes: automatically extracting them from the program description using a large language model, and calculating text similarity using a BERT model to match the permission library.
5. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 4, characterized in that, In step S3, the process of performing permission analysis by combining content permission tags with preprocessed user subscription information includes combining content permission tags with preprocessed user subscription information and matching the permission library based on text similarity to determine whether the user has the permission to access a specific program, thereby generating a preliminary authentication result.
6. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 1, characterized in that, In step S4, the process of calculating the risk score based on the preprocessed real-time data includes outlier detection using Z-score and IQR statistical algorithms.
7. The intelligent playback authentication method for set-top box users based on a large language model as described in claim 1, characterized in that, In step S5, the natural language feedback information includes permission status descriptions and operation instructions.
8. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the processor executes the one or more programs, it implements a set-top box user intelligent playback authentication method based on a large language model as described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a set-top box user intelligent playback authentication method based on a large language model as described in any one of claims 1-6.