14results about How to "Optimization Strategy" patented technology

The application discloses a data center electric energy utilization efficiency value simulation calculation method and system of indirect evaporative cooling air conditioner, comprising the following steps: based on a quantum computing platform, constructing an IT load of a data center, a quantum simulation model of an indirect evaporative cooling unit and a dynamic environment thereof, and establishing a dynamic correlation between a cooling unit state and a complex environment variable through quantum many-body simulation. The application breaks through the limitations of traditional optimization methods in a multi-dimensional control space through the optimization capability of quantum computing, can quickly calculate the optimal configuration of the cooling system, especially in high load and extreme environment; by using a quantum machine learning method, not only can the load change be accurately predicted, but also the control strategy can be adjusted in real time, energy waste is reduced, and the response speed and accuracy of the system are improved.

The application provides a battery module / battery cluster health state scoring method, system, storage medium and terminal, the method comprises the following steps: obtaining performance data of battery cells in a battery module / battery cluster; performing normalization processing on the performance data to obtain normalized data; calculating the standard score of the performance data based on the normalized data; calculating the performance score corresponding to the battery module / battery cluster based on the standard score; and calculating the health state score of the battery module / battery cluster based on the performance score. The battery module / battery cluster health state scoring method, system, storage medium and terminal can evaluate the health state of the battery module / battery cluster in real time, effectively improving the safety and reliability of the battery module / battery cluster.

This invention discloses a closed-loop control method and system for prevention, intervention, and risk feedback in cerebral hemorrhage, relating to the field of cerebral hemorrhage risk intervention technology. It includes the following steps: S1, real-time acquisition of multi-source cerebrovascular monitoring data, data preprocessing, and identification of high-pressure peak events; S2, quantification of key dynamic characteristics of each peak based on the multi-source cerebrovascular monitoring data corresponding to the high-pressure peak events, and assessment of high-pressure peak abnormalities; S3, fusion of multi-source cerebrovascular monitoring data and high-pressure peak abnormality assessment results, quantification of the risk probability of each peak event, identification of physiological fluctuations and risk events, and risk warning and response; S4, real-time monitoring of multi-source cerebrovascular monitoring data after risk warning, assessment of warning accuracy, identification of missed and false alarms, and threshold optimization. This solves the technical problems of existing technologies, such as difficulty in distinguishing between physiological and pathological peaks, susceptibility to misjudgments, and impact on the timeliness and individualized management of risk intervention.

The application discloses a NAO robot object grasping training method based on direct preference optimization, and comprises the following steps: (1) collecting and saving human expert data, imitation data and trajectory data of interactive grasping actions in a robot simulator using a reinforcement learning strategy; (2) scoring state-action trajectory sequence data pairs in the trajectory data by a human annotator to obtain a trajectory data set based on human preferences; (3) designing a reference strategy network using the human preference data set, designing a parameterized strategy network, constructing a maximum likelihood target in combination with the reference strategy network, and performing gradient descent on the target function to obtain an optimal strategy; (4) training an object recognition model, deploying the object recognition model and the optimal strategy to a real NAO robot, and evaluating the grasping actions of the robot; and (5) iteratively training and evaluating the process until the robot can successfully grasp the target object.

The application provides a printed circuit board drilling machine main shaft wear degree monitoring method under big data management, collects vibration, temperature, pressure and axial displacement parameter data when the main shaft runs, processes the collected parameters, establishes a main shaft wear degree monitoring model, and judges the wear state of the main shaft by inputting corresponding data, so that production efficiency is improved, maintenance cost is reduced, product quality is improved, safety is improved, and maintenance strategy is optimized.

The invention belongs to the technical field of electric clothes airing machines, and particularly relates to an intelligent control system of an electric clothes airing machine based on the Internet of Things, which comprises a multi-dimensional sensing module used for sensing clothes characteristics, environmental parameters and clothes states so as to realize comprehensive and real-time data acquisition of all elements of people-objects-environment in an airing scene; and the cross-equipment linkage module is used for connecting various types of intelligent equipment inside and outside a family according to an Internet of Things protocol, constructing scene linkage logic taking clothes airing requirements as a core, realizing active coordination of airing scene-equipment functions, integrating energy and resource scheduling logic, and having dual values of function coordination and green energy conservation. According to the invention, through the multi-dimensional sensing module, accurate identification of clothes materials and full-dimensional capture of real-time states of the clothes and indoor and outdoor environment parameters are realized, and refined airing requirements of clothes of different materials are met.

This invention provides a method for reliability assessment and maintenance strategy optimization of vehicle door systems based on the PH distribution. The method includes: acquiring fault data recorded by vehicle repair departments; estimating PH distribution parameters based on the dwell time of components in different states within the door system following a PH distribution; obtaining the state set of components in the door system based on the PH distribution parameters; constructing the state transition matrix of the components in the door system; generating a general generating function for the door system; constructing the reliability structure of the door system; obtaining the output performance and maintenance cost of the door system; establishing an optimization objective function for the maintenance strategy of the door system with the goals of minimizing the maintenance cost rate and maximizing the expected output performance; solving the optimization objective function using the NSGA-II algorithm based on dynamic congestion distance; and obtaining the optimal maintenance strategy for the door system. This invention can analyze the reliability of vehicle door systems and optimize maintenance strategies for door systems with both minimum cost rate and maximum output performance as dual objectives.

The invention provides a soft magnetic composite iron core, a health monitoring system, a diagnosis early warning method and application, and belongs to the technical field of soft magnetic composite iron cores and reliability monitoring. At least one sensor is embedded in the iron core, is used for detecting at least one of temperature, stress, strain or magnetic field intensity, and is coupled with the intelligent iron core health monitoring system to execute the early warning method of the monitoring system. The temperature distribution, mechanical vibration / stress state and magnetic saturation related characteristics in the working process of the iron core can be obtained in real time, characteristic parameters are extracted based on sensing data, health indexes are constructed, and self-diagnosis and early warning of faults such as over-temperature, looseness and local saturation are achieved. Compared with a traditional externally-attached monitoring scheme, the temperature distribution, the stress vibration and the magnetic saturation state of multiple points in the iron core can be accurately obtained, the hot spot recognition and fault pre-judgment accuracy is remarkably improved, the integrated structure of the sensor and the iron core is high in stability, and predictive maintenance can be supported so that the equipment shutdown risk can be reduced.

The invention discloses a long-time-history large model processing method based on asynchronous reinforcement learning, and relates to the technical field of large language models.The method comprises the steps that the problem complexity is improved through dual-operation iteration, high-quality training data is obtained through triple verification, a training end and a track generation end are decoupled through a two-stage track parallel generation mechanism, and the problem is solved; after a training end collects a preset batch track, long-time-history large model updating is started, then historical data is managed by depending on a basic tool set sub-model, end-to-end asynchronous reinforcement learning optimization is executed on the whole process of an intelligent agent, finally, the problem of long-time-history reward sparseness is solved based on a GRPO algorithm, and a model strategy is optimized. According to the scheme, the core bottlenecks of weak long-range exploration, low training efficiency and insufficient data quality of an open-source search intelligent body are solved, the search intelligence of fuzzy query analysis, key information extraction by noise, cross-document reasoning verification and super-long-range tool calling is unlocked, the limitation of a traditional search round is broken through, and a long-time-range task is supported.

The application discloses a kind of security control method and system based on SDN architecture, it is related to security control technical field, including: user end is authenticated by sending first handshake request to edge computing device to realize the identity authentication of user end, and corresponding master key is issued to user end and SDN controller by edge computing device, user end is encrypted to access request by master key, and sends the encrypted access request to SDN controller, after SDN controller is parsed encrypted access request, determine its data transmission route path, i.e. which switch is sent to user end by the data stream collected by distributed data acquisition terminal the security control method based on SDN architecture provided by the application realizes the unified control of network, wisdom arrangement and collaborative scheduling, provides various automation, intelligent capacity, provides high-speed, reliable, safe communication service guarantee for power system.

The utility model discloses a composite gradient lining structure for a highway tunnel in a cold region, which relates to the technical field of tunnel construction and comprises a tunnel primary support serving as an outermost support structure, a tunnel inverted arch arranged at the bottom of the tunnel and connected with the tunnel primary support, and a tunnel filling layer arranged above the tunnel inverted arch. Through optimal combination of different materials, a gradient transition layer is formed, material stress concentration caused by freeze-thaw cycle is effectively relieved, lining cracking and peeling risks are remarkably reduced, and the service life of a tunnel in a low-temperature environment is prolonged; according to the design of the tunnel composite gradient lining structure in the scheme, the thickness can be adjusted according to the grade of surrounding rock when a new project is applied, but due to the excellent performance of materials used by the composite gradient lining, the total thickness of the lining structure is smaller than that of a common structure, the excavation volume of the surrounding rock can be reduced, the construction risk is integrally reduced, and the engineering economy is improved.

This invention proposes an optimization method and system for energy storage systems based on a multi-objective ant colony optimization algorithm, belonging to the field of grid regulation technology for energy storage systems. It aims to efficiently solve the coordinated optimization problem of energy source, grid, load, and storage. The core of this method lies in constructing multiple heterogeneous heuristic information sets with different optimization biases, and dividing the ant colony into different groups. Each group searches for a scheduling scheme in parallel based on a specific heuristic information. This mechanism breaks through the limitations of traditional single search, greatly improves the diversity of global search, and can generate higher-quality and more widely distributed trade-off solution sets, thereby achieving optimal coordination between economic and environmental goals.

This invention belongs to the field of ecosystem models and is a method for constructing a forestry resource ecosystem model based on hyperspectral remote sensing monitoring. The method includes the following steps: S1: Acquire hyperspectral remote sensing images and combine them with GIS geographic system data to form layers; S2: Construct a two-type large-cycle ecosystem model of biological and abiotic resources based on the hyperspectral remote sensing images; S3: Calculate ecosystem parameter indicators using the two-type large-cycle ecosystem model of biological and abiotic resources; S4: Analyze and judge the ecological state and compare and adjust the ecosystem parameter indicators; S5: Establish a forestry resource prediction equation based on the parameter indicators in S4; S6: Input forestry production data into the prediction equation, adjust the ecosystem parameter indicators based on the results, and obtain the optimal prediction equation to guide forestry production.

The application provides a kind of single robot dynamic manipulation method based on multi-agent reinforcement learning, it is related to single robot technical field.The method of the application constructs the dynamic manipulation of single arm four-legged robot as a collaborative multi-agent reinforcement learning MARL problem.Specifically, the method is based on multi-agent proximal policy optimization algorithm MAPPO, four legs are regarded as four independent isomorphic agents, and efficient motion coordination is realized by sharing the same set of network parameters, while the robot arm is modeled as another independent heterogeneous agent, using another set of parameters to focus on manipulation tasks. Through this cooperative modeling method of heterogeneous agents, the close coordination of movement and manipulation can be achieved in a unified reinforcement learning framework, thereby significantly improving the efficiency and robustness of the four-legged robot with arm in unstructured environments.