Embodied intelligent device facing benefit-seeking and harm-avoiding incentive constraint mechanism construction method
Patent Information
- Application Number
- CN202611150644.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-31
- Publication Date
- 2026-10-30
AI Technical Summary
[0003]然而，当前主流基于强化学习与预设安全阈值的外部约束控制方法，难以支撑高可靠的自主运行，依赖云端大模型提供语义理解与任务规划，以任务完成度为核心设计奖励函数，并在触发碰撞、力控、速度等硬阈值时执行紧急停止或回退，存在诸多固有缺陷：一是安全约束外置且依赖硬阈值，仅能在危险发生后被动响应，缺乏类生物的内生预判与前置规避能力；二是激励与约束割裂，单一趋利的奖励函数与滞后刚性的安全惩罚易导致学习停滞或行为僵化，且静态固化的阈值无法适配环境变化、设备损耗与任务差异，泛化性极差；三是多模态感知与决策相互解耦，视觉、触觉、力觉与本体状态未能形成统一的危险表征，导致危险识别漏检率高、误判率高；四是决策过程属于黑箱，缺乏可解释性与危险溯源机制，难以满足高安全场景的合规落地要求；五是端云协同延迟高，极端工况下实时保障能力不足，且缺乏内生安全先验与在线更新机制，长期运行可靠性严重下降
本发明通过构建多模态内生危险表征与涵盖任务、效率、安全及能耗的多目标加权正向激励函数，打破了具身智能设备单纯依赖外部规则的安全局限；针对禁忌事件设定分级惩罚与安全边界，并创新性地融合控制障碍函数与约束马尔可夫决策过程，依据环境复杂度、任务风险及设备状态对约束强度进行动态自适应调节，有效克服了静态约束导致的任务僵化与动态复杂环境下的安全失效问题；将正向激励与分级惩罚深度耦合为趋利避害一体化目标函数，从根本上消解了安全与任务目标之间的耦合冲突，求解出兼顾效能与底线的内生安全最优策略；通过执行中的状态监测反馈与在线增量学习，将安全经验持续沉淀为内生先验并反哺更新危险表征与动态约束，构建了具备自我进化能力的闭环迭代机制，显著提升了具身智能设备在复杂未知环境下的内生安全性、任务执行力与环境自适应鲁棒性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent manufacturing technology, and more specifically to a method for constructing an intrinsic incentive and constraint mechanism for embodied intelligent devices that aims to maximize benefits and minimize harms. Background Technology
[0002] With the rapid evolution of embodied intelligence technology, it is moving from traditional fixed scenarios and preset program modes towards open environments, autonomous interaction, and continuous learning, and is widely penetrating into fields such as intelligent manufacturing, home services, medical rehabilitation, and emergency rescue. This also requires devices to complete a closed-loop operation of "perception-decision-execution" in unstructured environments full of dynamic obstacles, sudden interference, task changes, and complex physical constraints.
[0003] However, current mainstream external constraint control methods based on reinforcement learning and preset safety thresholds struggle to support highly reliable autonomous operation. These methods rely on large cloud-based models for semantic understanding and task planning, design reward functions centered on task completion, and execute emergency stops or rollbacks when hard thresholds such as collision, force control, and speed are triggered. This approach has several inherent flaws: First, external safety constraints and reliance on hard thresholds mean that responses are only passive after danger occurs, lacking the intrinsic predictive and proactive avoidance capabilities of biological systems. Second, the disconnect between incentives and constraints, with a purely profit-driven reward function and a delayed, rigid safety penalty, can easily lead to learning difficulties. The system suffers from several problems: First, its behavior is stagnant or rigid, and its static and fixed thresholds cannot adapt to changes in the environment, equipment wear and tear, and task differences, resulting in extremely poor generalization. Second, multimodal perception and decision-making are decoupled, and vision, touch, force perception, and proprioception fail to form a unified risk representation, leading to high rates of missed detections and misjudgments in risk identification. Third, the decision-making process is a black box, lacking interpretability and risk tracing mechanisms, making it difficult to meet the compliance requirements for high-security scenarios. Fourth, the edge-cloud collaboration suffers from high latency, insufficient real-time protection capabilities under extreme conditions, and a lack of inherent security priors and online update mechanisms, resulting in a serious decline in long-term operational reliability.
[0004] In summary, existing technologies cannot solve core pain points such as the synergistic optimization of benefit-seeking and harm-avoidance, the deep integration of multimodal hazards, and real-time security management at the edge. There is an urgent need to build a new architecture for proactive defense and collaborative control with inherent security priors, highly coupled perception and decision-making, and support for online evolution. This would completely break the passive response limitations of external hard constraints and truly solidify the foundation for highly reliable, highly secure, and autonomous operation of embodied intelligence in a complex and unknown physical world. Summary of the Invention
[0005] The purpose of this invention is to provide a method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices. Based on the endogenous risk representation, an incentive and penalty function for seeking benefits and avoiding harm and dynamic safety constraints are constructed to solve for the optimal strategy, and the closed-loop evolution of the mechanism is achieved through online incremental learning.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for constructing an intrinsic incentive and constraint mechanism for embodied intelligent devices includes the following steps: S1. Collect multimodal perception data from embodied intelligent devices, construct safety state vectors and hazard feature vectors, and form a unified endogenous hazard representation; S2. Based on the endogenous hazard characterization, construct a multi-objective weighted positive incentive function; wherein the multi-objective weighted positive incentive function includes: task benefit, efficiency benefit, safety benefit, and energy consumption benefit; S3. Define taboo events for embodied intelligent devices, and construct hierarchical penalty functions and safety constraint boundaries containing dynamically adjustable parameters; S4. Based on the current environmental complexity, task risk and equipment wear status, update the dynamic adjustment parameters in real time, and combine the control obstacle function and the constraint Markov decision process to dynamically and adaptively adjust the safety constraint boundary to obtain dynamic safety constraints. S5. Construct an integrated objective function for seeking benefits and avoiding harm using the multi-objective weighted positive incentive function and the hierarchical penalty function, and solve for the endogenous safety optimal strategy under the dynamic safety constraints; S6. Execute the intrinsic safety optimal strategy and perform state monitoring and feedback correction, precipitate safety experience into intrinsic priors, and update the intrinsic hazard representation and dynamic safety constraints. Achieve closed-loop iterative evolution of the mechanism through online incremental learning.
[0007] Furthermore, in S1, the formation process of the unified endogenous hazard characterization is specifically as follows: Data on the body posture, joint torque, end effector force control, environmental point cloud, and contact state of embodied intelligent devices are collected, and state coding and hazard feature extraction are performed to construct a safety state vector and a hazard feature vector, forming a unified endogenous hazard representation.
[0008] Furthermore, in S2, the multi-objective weighted positive incentive function is specifically designed to guide the embodied intelligent device toward maximizing both safety and task benefits, using the safety state vector and action as independent variables. The expression for the multi-objective weighted positive activation function is as follows: ; in For task rewards, For efficiency and profit, Safety benefits Energy consumption revenue.
[0009] Furthermore, in S2, the functional expression for the task reward is: ; in The task completion rate of the embodied intelligent device. The task delay time for embodied smart devices, The weighting coefficients are related to the degree of task completion. Weighting coefficients related to task delay duration; The functional expression for efficiency gains is: ; in To improve the actual operating efficiency of intelligent devices, To ensure the maximum operating efficiency of intelligent devices, Efficiency weighting coefficient; The functional expression for safe returns is: ; in For real-time risk level, The attenuation coefficient is... This is the safety weighting coefficient; The functional expression for energy consumption revenue is: ; in Real-time energy consumption of smart devices. The maximum energy consumption benefit function for embodied intelligent devices. This is the energy consumption weighting coefficient.
[0010] Furthermore, in S3, the construction process of the graded penalty function is as follows: Collisions, overturning, exceeding limits, overloads, and misoperations are defined as prohibited events; Construct a hierarchical penalty function P(S,A) with the safety state vector S and action A as variables; In S3, the functional expression of the safety constraint boundary is: ; in Based on the basic security threshold, This represents the environmental complexity coefficient. This is the equipment loss state coefficient. This is a dynamic adjustment coefficient related to environmental complexity. This is a dynamic adjustment coefficient related to the equipment wear status.
[0011] Furthermore, in step S4, the method for obtaining dynamic safety constraints is specifically as follows: Real-time assessment of current environmental complexity, task risk level, and equipment wear status; calculation and updating of dynamic adjustment coefficients related to environmental complexity and equipment wear status in safety constraint boundaries; By combining the control barrier function with the constrained Markov decision process, the updated safety boundary is not violated during the policy optimization process, and the actions satisfy dynamic feasibility, thus outputting dynamic safety constraints.
[0012] Furthermore, in S5, the expression for the graded penalty function is: ; in Let be the penalty level coefficient for the i-th type of dangerous event. This is a function indicating a dangerous event.
[0013] Furthermore, in S5, the functional expression of the intrinsically safe optimal strategy is: ; in It is a positive excitation function. The function is a graded penalty function, where λ is a configurable safety weight coefficient and A represents all control actions / decision behaviors that the embodied intelligent device can currently perform. To select the optimal action from all available actions that balances task benefits and safety constraints.
[0014] Furthermore, in S6, the mechanism achieves closed-loop iterative evolution through online incremental learning, specifically as follows: The status is monitored in real time during the execution of the action, and constraints are strengthened and corrections are made afterward when dangerous events are triggered. Experience in handling hazardous incidents and ensuring safe operation is distilled into intrinsic safety prior rules and internalized as prior knowledge at the underlying level of equipment decision-making; It supports online incremental learning, continuously updates the intrinsic safety priors and incentive constraint models, and achieves autonomous evolution of the ability to seek benefits and avoid harm.
[0015] Furthermore, the method also supports decision interpretability and hazard traceability; In the process of solving for the intrinsically safe optimal strategy and executing the intrinsically safe optimal strategy while performing state monitoring and feedback correction, the decision path is recorded and correlated based on causal reasoning. When a dangerous event occurs or the strategy is blocked, the source of danger and responsibility are identified.
[0016] According to specific embodiments provided by the present invention, the present invention has the following technical effects compared to the prior art: This invention breaks through the safety limitations of embodied intelligent devices that rely solely on external rules by constructing a multimodal endogenous hazard representation and a multi-objective weighted positive incentive function encompassing task, efficiency, safety, and energy consumption. It sets tiered penalties and safety boundaries for taboo events and innovatively integrates control barrier functions and constrained Markov decision processes. The constraint strength is dynamically and adaptively adjusted based on environmental complexity, task risk, and device status, effectively overcoming task rigidity caused by static constraints and safety failures in dynamic and complex environments. By deeply coupling positive incentives and tiered penalties into an integrated objective function of maximizing benefits and minimizing harm, it fundamentally resolves the coupling conflict between safety and task objectives, finding an endogenous safety optimal strategy that balances efficiency and bottom lines. Through state monitoring feedback and online incremental learning during execution, safety experience is continuously accumulated into endogenous priors and used to update hazard representations and dynamic constraints, constructing a closed-loop iterative mechanism with self-evolutionary capabilities. This significantly improves the endogenous safety, task execution capability, and environmental adaptability of embodied intelligent devices in complex and unknown environments. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0018] The following description, in conjunction with the accompanying drawings, further illustrates the method for constructing an intrinsic incentive and constraint mechanism for embodied intelligent devices. Figure 1 This is a schematic diagram of the overall process of constructing an endogenous incentive and constraint mechanism for embodied intelligent devices in this invention. Detailed Implementation
[0019] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0020] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.
[0021] like Figure 1 As shown, this invention provides a method for constructing an intrinsic incentive and constraint mechanism for embodied intelligent devices, comprising the following steps: S1. Collect multimodal perception data from embodied intelligent devices, construct safety state vectors and hazard feature vectors, and form a unified endogenous hazard representation; In S1, the formation process of the unified endogenous hazard characterization is specifically as follows: Data on the body posture, joint torque, end effector force control, environmental point cloud, and contact state of embodied intelligent devices are collected, and state coding and hazard feature extraction are performed to construct a safety state vector and a hazard feature vector, forming a unified endogenous hazard representation.
[0022] This embodiment specifically involves a multimodal endogenous perception layer. The process of acquiring multimodal perception data is as follows: 1) Acquire environmental RGB images through a visual camera, acquire scene point cloud depth information through a depth camera, acquire device contact pressure and contact area data through a tactile sensor, acquire end effector torque and contact force data through a force sensor, and acquire device body state data such as joint angles, angular velocities, body posture, and displacement deviation through a body sensor; 2) Perform time stamp alignment on multimodal data of different frame rates and dimensions, and use sliding window filtering to remove environmental noise and sensor jitter abnormal data; 3) Map discrete sensor data to a unified dimensional space to generate standardized device body state features; 4) Extract obstacle distance, obstacle density, environmental openness, and dynamic obstacle movement trend features from visual / depth data; 5) Extract overload, rigid collision, and abnormal contact impact features from tactile / force data; 6) Extract joint over-limit, posture overturning, and motion instability features from body sensor data; Finally, fuse all dimensional features to output a unified device-environment hazard characterization vector.
[0023] S2. Based on the endogenous hazard characterization, construct a multi-objective weighted positive incentive function; wherein the multi-objective weighted positive incentive function includes: task benefit, efficiency benefit, safety benefit, and energy consumption benefit; In S2, the multi-objective weighted positive incentive function is specifically designed to guide the embodied intelligent device toward maximizing both safety and task benefits, using the safety state vector and action as independent variables. The expression for the multi-objective weighted positive activation function is as follows: ; in For task rewards, For efficiency and profit, Safety benefits Energy consumption revenue.
[0024] In S2, the functional expression for the task reward is: ; in The task completion rate of the embodied intelligent device. The task delay time for embodied smart devices, The weighting coefficients are related to the degree of task completion. Weighting coefficients related to task delay duration; The functional expression for efficiency gains is: ; in To improve the actual operating efficiency of intelligent devices, To ensure the maximum operating efficiency of intelligent devices, Efficiency weighting coefficient; The functional expression for safe returns is: ; in For real-time risk level, The attenuation coefficient is... This is the safety weighting coefficient; The functional expression for energy consumption revenue is: ; in Real-time energy consumption of smart devices. The maximum energy consumption benefit function for embodied intelligent devices. This is the energy consumption weighting coefficient.
[0025] This embodiment specifically involves: synchronously acquiring multi-source data such as device body posture, joint torque, end effector force control parameters, environmental point cloud, and contact status, and performing time-series alignment, noise reduction, and normalization processing. A standardized safety state vector is then constructed. S =[ s 1 , s 2 ,...., s n This characterizes the equipment's attitude, energy consumption, losses, and operating status; and constructs a refined hazard feature vector. D =[ d 1 , d 2 ,..., d m This represents hazardous information such as environmental obstacles, contact risks, exceeding movement limits, and overturning risks. The two vectors are fused to form a unified state representation of the equipment and environment, providing standardized input for subsequent excitation and constraint calculations.
[0026] Based on the core operational needs of embodied intelligent devices, four positive benefit dimensions are defined: task completion, operational efficiency, operational safety, and energy consumption optimization. R task , R eff , R safe , Rene The subdivision function is used to obtain the total positive activation function through weighted fusion. R ( S , A The weights of each dimension are dynamically adjusted according to different scenarios to ensure that the equipment prioritizes high-yield and high-safety operating behaviors under different working conditions.
[0027] S3. Define taboo events for embodied intelligent devices, and construct hierarchical penalty functions and safety constraint boundaries containing dynamically adjustable parameters; In S3, the construction process of the graded penalty function is as follows: Collisions, overturning, exceeding limits, overloads, and misoperations are defined as prohibited events; Construct a hierarchical penalty function P(S,A) with the safety state vector S and action A as variables; In S3, the functional expression of the safety constraint boundary is: ; in Based on the basic security threshold, This represents the environmental complexity coefficient. This is the equipment loss state coefficient. This is a dynamic adjustment coefficient related to environmental complexity. This is a dynamic adjustment coefficient related to the equipment wear status.
[0028] This embodiment specifically defines five core prohibited events: collision, overturning, parameter exceeding limits, load overload, and intelligent malfunction. These events are categorized into three levels of risk: minor, moderate, and severe, and different penalty coefficients are assigned accordingly. K Through a graded penalty function P ( S , A Quantify the risk of loss, while relying on the safety boundary function. C ( S The ≤0 parameter defines the feasible domain for equipment decision-making and actions, thereby blocking dangerous behaviors from both the perspectives of loss punishment and behavioral boundaries.
[0029] Specifically, this embodiment involves: receiving the optimal safety action command output by the decision layer, driving the equipment joints, mobile platform, and operating end to complete the corresponding operation actions, strictly limiting the action amplitude, speed, and torque according to the dynamic safety boundary; the equipment collects the body posture, contact state, environmental changes, and task progress in real time throughout the execution process, synchronously compares it with the preset safety baseline, and monitors in real time for any abnormalities such as dangerous deviations, action over-limits, or sudden environmental changes; if a slight dangerous trend is detected, the safety threshold is adaptively reduced and the constraint strength is strengthened; if a dangerous event is triggered, the current action is immediately blocked, and the penalty coefficient and safety boundary parameters of the modeling layer are updated in reverse to complete a single constraint correction.
[0030] S4. Based on the current environmental complexity, task risk and equipment wear status, update the dynamic adjustment parameters in real time, and combine the control obstacle function and the constraint Markov decision process to dynamically and adaptively adjust the safety constraint boundary to obtain dynamic safety constraints. The method for obtaining dynamic safety constraints in S4 is as follows: Real-time assessment of current environmental complexity, task risk level, and equipment wear status; calculation and updating of dynamic adjustment coefficients related to environmental complexity and equipment wear status in safety constraint boundaries; By combining the control barrier function with the constrained Markov decision process, the updated safety boundary is not violated during the policy optimization process, and the actions satisfy dynamic feasibility, thus outputting dynamic safety constraints.
[0031] This embodiment specifically involves an endogenous decision optimization layer that records both normal safe operation data and hazardous event data throughout the entire equipment operation process. For hazardous events, the layer automatically increases the penalty coefficient and constraint threshold for the corresponding scenario to enhance hazard avoidance capabilities. For long-term stable safe operation scenarios, the layer accumulates prior knowledge of scenario safety and updates the underlying decision rules. Through online incremental learning, the incentive constraint model and decision-making strategy are continuously iterated and optimized to achieve the autonomous evolution of the equipment's ability to seek benefits and avoid harm.
[0032] S5. Construct an integrated objective function for seeking benefits and avoiding harm using the multi-objective weighted positive incentive function and the hierarchical penalty function, and solve for the endogenous safety optimal strategy under the dynamic safety constraints; In S5, the expression for the graded penalty function is: ; in Let be the penalty level coefficient for the i-th type of dangerous event. This is a function indicating a dangerous event.
[0033] In S5, the functional expression of the intrinsically safe optimal strategy is: ; in It is a positive excitation function. The function is a graded penalty function, where λ is a configurable safety weight coefficient and A represents all control actions / decision behaviors that the embodied intelligent device can currently perform. To select the optimal action from all available actions that balances task benefits and safety constraints.
[0034] S6. Execute the intrinsic safety optimal strategy and perform state monitoring and feedback correction, precipitate safety experience into intrinsic priors, and update the intrinsic hazard representation and dynamic safety constraints. Achieve closed-loop iterative evolution of the mechanism through online incremental learning.
[0035] In S6, the mechanism achieves closed-loop iterative evolution through online incremental learning, specifically as follows: The status is monitored in real time during the execution of the action, and constraints are strengthened and corrections are made afterward when dangerous events are triggered. Experience in handling hazardous incidents and ensuring safe operation is distilled into intrinsic safety prior rules and internalized as prior knowledge at the underlying level of equipment decision-making; It supports online incremental learning, continuously updates the intrinsic safety priors and incentive constraint models, and achieves autonomous evolution of the ability to seek benefits and avoid harm.
[0036] The method also supports decision interpretability and hazard traceability; In the process of solving for the intrinsically safe optimal strategy and executing the intrinsically safe optimal strategy while performing state monitoring and feedback correction, the decision path is recorded and correlated based on causal reasoning. When a dangerous event occurs or the strategy is blocked, the source of danger and responsibility are identified.
[0037] Specifically, this embodiment consists of an execution and feedback iteration layer, which, together with the multimodal endogenous perception layer, the benefit-seeking and harm-avoidance modeling layer, and the endogenous decision optimization layer, forms a core logic of "forward reasoning + reverse iteration": the perception layer-modeling layer-decision layer is the forward reasoning link, completing the output from environmental perception to safety decision; the execution and feedback iteration layer is the reverse optimization link, which, through execution monitoring and constraint correction, inputs safety data and hazard experience from real-world operating scenarios back into the preceding three-layer architecture, iteratively optimizing feature extraction accuracy, incentive constraint model parameters, and decision strategies, thereby achieving continuous online evolution of the mechanism.
[0038] Compared with the prior art, the present invention also has the following technical advantages: This invention propels a shift in the safety paradigm from passive to proactive. By fusing multimodal hazard representations, it achieves more accurate and faster identification, enabling hazard prediction and proactive avoidance, significantly reducing accident and false positive rates. In terms of decision-making mechanisms, it leverages intrinsic safety priors to reduce reliance on external rules, supporting autonomous evolution to enhance long-term reliability. Simultaneously, it achieves deep collaboration and dynamic adaptation between incentives and constraints, breaking down the conflict between safety and performance, improving task efficiency while ensuring safety, and significantly enhancing cross-scenario generalization capabilities in complex dynamic environments. Furthermore, the system's decision-making process is fully explainable and traceable, fully meeting compliance requirements in high-safety scenarios such as medical, industrial, and home environments. Combined with cloud-free, real-time edge deployment, stability and real-time performance under extreme conditions are significantly enhanced. Ultimately, this solution, with its strong versatility, comprehensively empowers various embodied devices such as humanoid robots, robotic arms, and mobile platforms.
[0039] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for constructing an intrinsic incentive and constraint mechanism for embodied intelligent devices, characterized in that, Includes the following steps: S1. Collect multimodal perception data from embodied intelligent devices, construct safety state vectors and hazard feature vectors, and form a unified endogenous hazard representation; S2. Based on the endogenous hazard characterization, construct a multi-objective weighted positive incentive function; wherein the multi-objective weighted positive incentive function includes: task benefit, efficiency benefit, safety benefit, and energy consumption benefit; S3. Define taboo events for embodied intelligent devices, and construct hierarchical penalty functions and safety constraint boundaries containing dynamically adjustable parameters; S4. Based on the current environmental complexity, task risk and equipment wear status, update the dynamic adjustment parameters in real time, and combine the control obstacle function and the constraint Markov decision process to dynamically and adaptively adjust the safety constraint boundary to obtain dynamic safety constraints. S5. Construct an integrated objective function for seeking benefits and avoiding harm using the multi-objective weighted positive incentive function and the hierarchical penalty function, and solve for the endogenous safety optimal strategy under the dynamic safety constraints; S6. Execute the intrinsic safety optimal strategy and perform state monitoring and feedback correction, precipitate safety experience into intrinsic priors, and update the intrinsic hazard representation and dynamic safety constraints. Achieve closed-loop iterative evolution of the mechanism through online incremental learning.
2. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S1, the formation process of the unified endogenous hazard characterization is specifically as follows: Data on the body posture, joint torque, end effector force control, environmental point cloud, and contact state of embodied intelligent devices are collected, and state coding and hazard feature extraction are performed to construct a safety state vector and a hazard feature vector, forming a unified endogenous hazard representation.
3. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S2, the multi-objective weighted positive incentive function is specifically designed to guide the embodied intelligent device toward maximizing both safety and task benefits, using the safety state vector and action as independent variables. The expression for the multi-objective weighted positive activation function is as follows: ； in For task benefits, for efficiency benefits. Safety benefits Energy consumption revenue.
4. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S2, the functional expression for the task reward is: ； in The task completion rate of the embodied intelligent device. The task delay time for embodied smart devices, The weighting coefficients are related to the degree of task completion. Weighting coefficients related to task delay duration; The functional expression for efficiency gains is: ； in To improve the actual operating efficiency of intelligent devices, To ensure the maximum operating efficiency of intelligent devices, Efficiency weighting coefficient; The functional expression for safe returns is: ； in For real-time risk level, The attenuation coefficient is... This is the safety weighting coefficient; The functional expression for energy consumption revenue is: ； in Real-time energy consumption of smart devices. The maximum energy consumption benefit function for embodied intelligent devices. This is the energy consumption weighting coefficient.
5. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S3, the construction process of the graded penalty function is as follows: Collisions, overturning, exceeding limits, overloads, and misoperations are defined as prohibited events; Construct a hierarchical penalty function P(S,A) with the safety state vector S and action A as variables; In S3, the functional expression of the safety constraint boundary is: ;in Based on the basic security threshold, This represents the environmental complexity coefficient. This is the equipment loss state coefficient. This is a dynamic adjustment coefficient related to environmental complexity. This is a dynamic adjustment coefficient related to the equipment wear status.
6. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, The method for obtaining dynamic safety constraints in S4 is as follows: Real-time assessment of current environmental complexity, task risk level, and equipment wear status; calculation and updating of dynamic adjustment coefficients related to environmental complexity and equipment wear status in safety constraint boundaries; By combining the control barrier function with the constrained Markov decision process, the updated safety boundary is not violated during the policy optimization process, and the actions satisfy dynamic feasibility, thus outputting dynamic safety constraints.
7. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S5, the expression for the graded penalty function is: ； in Let be the penalty level coefficient for the i-th type of dangerous event. This is a function indicating a dangerous event.
8. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S5, the functional expression of the intrinsically safe optimal strategy is: ； in It is a positive excitation function. The function is a graded penalty function, where λ is a configurable safety weight coefficient and A represents all control actions / decision behaviors that the embodied intelligent device can currently perform. To select the optimal action from all available actions that balances task benefits and safety constraints.
9. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, In S6, the mechanism achieves closed-loop iterative evolution through online incremental learning, specifically as follows: The status is monitored in real time during the execution of the action, and constraints are strengthened and corrections are made afterward when dangerous events are triggered. Experience in handling hazardous incidents and ensuring safe operation is distilled into intrinsic safety prior rules and internalized as prior knowledge at the underlying level of equipment decision-making; It supports online incremental learning, continuously updates the intrinsic safety priors and incentive constraint models, and achieves autonomous evolution of the ability to seek benefits and avoid harm.
10. The method for constructing an endogenous incentive and constraint mechanism for embodied intelligent devices according to claim 1, characterized in that, The method also supports decision interpretability and hazard traceability; In the process of solving for the intrinsically safe optimal strategy and executing the intrinsically safe optimal strategy while performing state monitoring and feedback correction, the decision path is recorded and correlated based on causal reasoning. When a dangerous event occurs or the strategy is blocked, the source of danger and responsibility are identified.