An ai decision output module based on a proportion judgment mechanism and an implementation method

Abstract

This invention discloses an AI decision output module and its implementation method based on a proportional judgment mechanism, belonging to the field of AI decision system optimization technology. This invention establishes a proportional judgment benchmark model for AI decision output, designs a proportional sensing unit to collect decision output data in real time, identifies proportional stress points affecting decision accuracy, and uses this as a basis to dynamically execute decision filtering logic under multi-objective weights through the proportional judgment unit, achieving accurate and reliable decision output. This invention can significantly improve the accuracy, reliability, and adaptability of AI decision systems, avoid decision bias and misjudgment, and is applicable to the decision output optimization of various AI decision systems and intelligent control models.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence decision-making systems, deep learning model output optimization, and intelligent control technology. Specifically, it relates to an AI decision-making output module and its implementation method based on a proportional judgment mechanism, which is applicable to improving the accuracy and reliability of decision output in various AI decision-making systems, intelligent control models, and multi-objective optimization scenarios. Background Technology

[0002] Current AI decision-making outputs mostly employ fixed threshold judgment, maximum probability selection, or single confidence level screening modes, which present the following core problems in complex multi-objective and multi-constraint decision-making scenarios: 1. Fixed decision thresholds cannot adapt to the dynamic needs of different scenarios and task priorities, which can easily lead to decision bias, misjudgment, or omission. 2. Lack of awareness of the proportional relationship between confidence, weight, and risk of each dimension of decision output, and inability to identify key stress points in decision output, resulting in insufficient decision reliability; 3. Traditional decision-making modules are centered on "single result output" and do not utilize the global optimality of proportional judgment, making it difficult to achieve a dynamic balance among decision accuracy, response speed, and risk control; 4. The decision-making logic is out of touch with actual business needs, and it is unable to dynamically adjust the decision output according to the weight ratio of multiple objectives, making it difficult to adapt to complex multi-objective optimization scenarios.

[0003] Existing technologies have failed to start from the underlying philosophy of proportion and build an AI decision-making output system based on the proportion judgment mechanism, thus failing to fully unleash the potential of AI decision-making systems in terms of accuracy and reliability. Summary of the Invention

[0004] Based on the philosophy of proportion and the principle of proportion judgment, this invention proposes an AI decision output module and implementation method based on the proportion judgment mechanism. By constructing a proportional judgment benchmark model for decision output, it dynamically identifies proportional stress points in the decision-making process, realizes accurate decision output under multiple objectives and constraints, and significantly improves the accuracy, reliability and adaptability of the AI ​​decision system.

[0005] This invention includes the following core steps: 1. Establish a benchmark model for judging the proportion of AI decision output, including the proportional relationship between parameters such as confidence, weight, risk, and return of each decision option, and define the safe proportion range, warning proportion range, and critical proportion threshold; 2. Design a proportional sensing unit to collect data such as the confidence, weight, and risk of each option output by the decision model in real time, and form a real-time proportional vector after normalization processing; 3. Compare the real-time scaling vector with the benchmark model, calculate the scaling deviation, and identify the decision scaling stress points, i.e., the key decision nodes that have a decisive impact on the accuracy, reliability, and risk of decision-making; 4. Design a ratio judgment unit to dynamically execute decision-making and screening logic based on stress point type and multi-objective weight ratio: accurately output the optimal option with a high ratio, perform secondary verification on the critical ratio option, and filter out the invalid option with a low ratio. 5. Design a proportional calibration unit to periodically update the proportional judgment benchmark model based on actual decision feedback data, adapt to changes in different scenarios, tasks, and business needs, and maintain the best long-term decision-making effect; 6. Construct a multi-objective proportional weight dynamic adjustment mechanism to adjust the proportional weight of each objective in real time according to task priority and business needs, so as to achieve the optimal decision output under multi-objective constraints.

[0006] The modules of this invention include: a ratio sensing unit, a ratio judgment benchmark model unit, a decision ratio stress point identification unit, a ratio judgment unit, a ratio calibration unit, and a multi-objective ratio weight adjustment unit.

[0007] The beneficial effects of this invention are as follows: 1. Using proportional judgment as the core decision-making basis, it achieves accurate decision output under multiple objectives and constraints, significantly improving the accuracy and reliability of AI decision-making systems; 2. Dynamically identify decision-making stress points to avoid decision-making biases, misjudgments, and omissions, thereby improving the stability and robustness of the decision-making system; 3. Based on dynamic adjustment of proportional weights, it adapts to the needs of different scenarios and different task priorities, improving the adaptability and flexibility of the decision-making system; 4. It is compatible with various AI decision-making models and intelligent control systems, and can be directly integrated into existing AI systems without significant modification to the model structure; 5. It can be deeply integrated with business needs to achieve optimal decision output under multi-objective optimization, and is suitable for complex multi-objective decision-making scenarios. Detailed Implementation

[0008] 1. Initialize the AI ​​decision-making system, read the preset ratio judgment benchmark, establish the initial ratio model, and define the safe ratio range, warning ratio range, and critical ratio threshold for each decision parameter; 2. During the decision-making process, the proportional sensing unit collects the confidence level, weight, risk, and return data of each decision option in real time, and forms a real-time proportional vector after normalization. 3. The decision ratio stress point identification unit compares the real-time ratio vector with the benchmark model, calculates the ratio deviation, and locates the current decision ratio stress point: - If the ratio of a certain decision option is much higher than that of other options, it is determined to be an absolutely optimal stress point; - If the proportion of multiple decision options is close to the critical threshold, it is determined to be a critical dispute stress point; - If the overall decision-making ratio deviates from the safe range, it is judged as a global risk stress point; 4. The ratio judgment unit executes decision logic based on the stress point type and the multi-objective weight ratio: - For the absolutely optimal stress point, the option is directly output as the final decision; - For critical and controversial stress points, a secondary verification logic is initiated, and the final decision is determined by combining the business weight ratio; - For stress points with global risks, trigger a risk warning, adjust the proportional threshold, and then make a new decision; 5. The proportional calibration unit periodically updates the proportional judgment benchmark model based on actual decision feedback data to adapt to changes in scenarios and adjustments in business needs; 6. The multi-objective proportional weight adjustment unit adjusts the proportional weight of each objective in real time according to the task priority, dynamically optimizes the decision output logic, and achieves the optimal decision under multi-objective constraints.

Claims

1. An AI decision output module based on a proportional judgment mechanism, characterized in that, The system takes a continuous proportion confidence value of [0,1] as input and outputs the decision result based on the proportion interval.

2. The module of claim 1, wherein The decision-making range should include at least four levels: rejection, caution, confirmation, and strong execution.

3. The module of claim 1, wherein A proportional comparator is used to achieve low-latency decision-making.

4. The module of claim 1, wherein The decision output includes a confidence level, which is used for risk control and system coordination.

5. An AI chip or intelligent decision-making device, characterized in that, It includes the proportion judgment decision output module as described in any one of claims 1-4.