Artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision

Abstract

The invention discloses an artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision, and belongs to the technical field of artificial intelligence safety. Comprising an input interface module, an intelligent response generation unit, a safety compliance evaluation unit, a decision fusion center and a result optimization controller. The input interface module is used for receiving instruction data input by a user; and the intelligent response generation unit is connected with the input interface module and is used for generating a plurality of candidate responses based on a pre-trained generative artificial intelligence model and calculating an efficiency index for each candidate response. Through a unique co-processing architecture, security assessment is deeply fused into a decision generation link, risk is avoided from the source, computing resource waste and delay caused by serial filtering are eliminated, the index integration algorithm unifies efficiency and security targets on the system level, and the security assessment is more efficient and more reliable. And guiding the system to find an optimal solution within the security boundary.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence safety technology, and in particular to an artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making. Background Technology

[0002] With the widespread application of generative artificial intelligence (AI) technology, ensuring its behavior complies with safety regulations has become a key challenge. Existing technical solutions face multiple constraints in their architectural design, including the temporal dependence of processing flows. Current mainstream solutions design content generation and security assessment as two separate execution phases, leading to additional computational latency and resource overhead, and failing to achieve real-time risk intervention. Static rules have limited adaptability; compliance checks based on predefined rule sets lack a deep understanding of dynamic contexts, making it difficult to achieve creative expression while ensuring security boundaries in complex scenarios. The collaborative challenge of multi-objective optimization is also a concern. At the model design level, task completion quality and safety constraints often have inherent limitations. Although techniques such as reinforcement learning have provided some improvements, they still face practical bottlenecks such as high training complexity and limited generalization ability.

[0003] Therefore, there is an urgent need in this field for innovative system architectures that can reconstruct the logic flow of artificial intelligence computing, enabling the synergistic realization of security capabilities and performance. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide an artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making; it can solve the problems of multiple constraints in the architecture design and the time-series dependence of the processing flow faced by existing technical solutions.

[0005] Technical solution: To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, an artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making, comprising: an input interface module, an intelligent response generation unit, a safety and compliance evaluation unit, a decision fusion center, and a result optimization controller; Input interface module: Used to receive user input command data; Intelligent response generation unit: connected to the input interface module, used to generate multiple candidate responses based on a pre-trained generative artificial intelligence model, and calculate performance indicators for each candidate response; Security and compliance assessment unit: connected to the input interface module and configured in conjunction with the intelligent response generation unit, used to perform compliance analysis on each candidate response based on the compliance knowledge base and calculate compliance indicators; Decision Fusion Center: Used to connect the intelligent response generation unit and the security compliance assessment unit respectively, and used to synthesize the performance indicators and compliance indicators of each candidate response into a comprehensive score through the indicator integration algorithm; Result Preferred Controller: Connected to the decision fusion center, used to select the final behavior output from the multiple candidate responses based on the comprehensive score.

[0006] Furthermore, the security compliance assessment unit adopts a tiered assessment mechanism.

[0007] Furthermore, the specific implementation method of the indicator integration algorithm is not limited.

[0008] Furthermore, the system also includes a compliance knowledge base connected to the security compliance assessment unit.

[0009] Furthermore, the system specifically includes the following steps: S1. Receive user input command data through the input interface module; S2. The intelligent response generation unit generates multiple candidate responses based on a pre-trained generative artificial intelligence model and calculates the performance index of each candidate response. S3. The security compliance assessment unit performs compliance analysis on each candidate response based on the compliance knowledge base and calculates the compliance indicators for each candidate response. S4. By using an indicator integration algorithm through the decision fusion center, the performance indicators and compliance indicators of each candidate response are combined into a comprehensive score. S5. The result optimization controller selects the final behavior output from multiple candidate responses based on the comprehensive score.

[0010] Beneficial effects: Intrinsic security and efficiency improvement: Through a unique collaborative processing architecture, security assessment is deeply integrated into the decision generation process, which avoids risks from the source and eliminates the waste of computing resources and latency caused by serial filtering.

[0011] The unity of performance and security: The indicator integration algorithm unifies performance and security goals at the system level, guiding the system to find the optimal solution within the security boundary.

[0012] Generality and scalability of the architecture: The system architecture protected by this invention is independent of specific model implementations and can be adapted to various generative artificial intelligence models. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the system principle. Detailed Implementation

[0014] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0015] Example 1 Security Management System for AI Content Generation APIs 1. Technical Background A cloud service provider deployed this system using its large language model content generation API. The system needs to simultaneously meet high response speed (millisecond-level response) and strict content security standards (comply with relevant regulations on network content ecosystem governance), and adapt to the generation needs of user commands in multiple scenarios.

[0016] 2. Technical Implementation Details Input interface module: It adopts a RESTful API interface design, supports user command input in JSON format, and has input data validation, format conversion and exception handling functions. It can accept various types of user input such as text and commands.

[0017] Intelligent response generation unit: It uses a pre-trained large language model as the basic generation model and generates 3-5 candidate responses through a candidate generation strategy (optimized based on the beam search algorithm). The performance index is calculated based on the model's generation confidence of each candidate response, the semantic matching degree with the user's command, and the grammatical coherence score. A weighted summation method is used to obtain a quantitative index of 0-10 points.

[0018] Security and compliance assessment unit: A three-level assessment mechanism is adopted. The first level assessment is based on rule matching (keyword filtering, violation scenario matching) of the compliance knowledge base. The second level assessment uses a lightweight text classification model (based on BERT fine-tuning) to detect potential violations. The third level assessment conducts in-depth semantic analysis for high-risk scenarios. The compliance indicators are quantified in the form of 0-10 points. The higher the score, the stronger the compliance. If the first level assessment is not passed, the compliance indicator is directly judged to be 0 points.

[0019] Compliance Knowledge Base: Integrates relevant laws and regulations on network content security, platform security rules, industry standards, etc., to build a structured knowledge base that supports the dynamic addition, modification and deletion of rules, and has a version management and update mechanism.

[0020] Decision Fusion Center: Employs a weighted product method as the indicator integration algorithm. The comprehensive score = performance indicator × weight A + compliance indicator × weight B (where weights A and B can be configured according to business scenarios, such as B=0.6 and A=0.4 in high security scenarios; and A=0.6 and B=0.4 in high performance scenarios). The algorithm supports real-time parameter adjustment to adapt to the priority requirements of different business scenarios.

[0021] Result Optimization Controller: The preset comprehensive score threshold is 7 points. When there is a candidate response with a comprehensive score ≥ 7 points, the candidate response with the highest score is selected for output. If the comprehensive score of all candidate responses is < 7 points, the security response mechanism is triggered, and a compliance prompt message is output instead of a non-compliant candidate response.

[0022] 3. Work Process The input interface module receives content generation instructions sent by the user, completes data verification and format conversion, and then transmits the data synchronously to the intelligent response generation unit and the security compliance assessment unit. The intelligent response generation unit generates 3-5 candidate responses based on a pre-trained large language model, calculates the performance index of each candidate response, and transmits the candidate responses and corresponding performance indexes to the decision fusion center. The safety compliance assessment unit calls upon the compliance knowledge base, conducts compliance analysis on each candidate response through a three-level assessment mechanism, calculates compliance indicators, and transmits the compliance indicators to the decision fusion center. The decision fusion center uses a pre-set indicator integration algorithm to synthesize the performance indicators and compliance indicators of each candidate response into a comprehensive score. The result selection controller filters out the final output content based on the comprehensive score and preset threshold, and feeds it back to the user through the input interface module.

[0023] 4. Verification of technical effectiveness In this embodiment, the system response latency is controlled within 500 milliseconds, which is more than 40% lower than the traditional serial processing architecture; the content compliance rate reaches 99.8%, and no illegal content is output; under diverse user command scenarios, the performance score of candidate responses remains above 8.5 points on average, achieving synergistic optimization of security compliance and generation performance.

[0024] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making, characterized in that, include: Input interface module, intelligent response generation unit, security and compliance assessment unit, decision fusion center, and result optimization controller; Input interface module: Used to receive user input command data; Intelligent response generation unit: connected to the input interface module, used to generate multiple candidate responses based on a pre-trained generative artificial intelligence model, and calculate performance indicators for each candidate response; Security and compliance assessment unit: connected to the input interface module and configured in conjunction with the intelligent response generation unit, used to perform compliance analysis on each candidate response based on the compliance knowledge base and calculate compliance indicators; Decision Fusion Center: Used to connect the intelligent response generation unit and the security compliance assessment unit respectively, and used to synthesize the performance indicators and compliance indicators of each candidate response into a comprehensive score through the indicator integration algorithm; Result Preferred Controller: Used to connect to the decision fusion center and to select the final behavior output from the multiple candidate responses based on the comprehensive score.

2. The artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making according to claim 1, characterized in that: The security compliance assessment unit adopts a tiered assessment mechanism.

3. The artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making according to claim 2, characterized in that: The specific implementation method of the indicator integration algorithm is not limited.

4. The artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making according to claim 3, characterized in that: The system also includes a compliance knowledge base connected to the security compliance assessment unit.

5. The artificial intelligence behavior optimization system based on collaborative evaluation and comprehensive decision-making according to claim 4, characterized in that: The system specifically includes the following steps: S1. Receive user input command data through the input interface module; S2. The intelligent response generation unit generates multiple candidate responses based on a pre-trained generative artificial intelligence model and calculates the performance index of each candidate response. S3. The security compliance assessment unit performs compliance analysis on each candidate response based on the compliance knowledge base and calculates the compliance indicators for each candidate response. S4. By using an indicator integration algorithm through the decision fusion center, the performance indicators and compliance indicators of each candidate response are combined into a comprehensive score. S5. The result optimization controller selects the final behavior output from multiple candidate responses based on the comprehensive score.

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