3320results about How to "Efficient use of" patented technology

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention relates to the technical field of image processing, and discloses a display method and system for a vehicle-mounted LED display screen. The method comprises the following steps: acquiring ambient light intensity data, performing fusion processing to obtain a characteristic value, and predicting ambient light change; identifying a driving scene according to the road condition data, and analyzing the priority by combining the ambient light change data; arranging a display area in combination with the eye movement data, and determining display parameters; analyzing the multi-modal interaction data, determining an interaction strategy, and verifying to generate response data; adjusting display content according to the ambient light features, and outputting a display effect in combination with response data; and energy consumption is estimated by integrating various data, and control parameters are generated to adjust an LED driving circuit. Through intelligent analysis and processing of multi-dimensional data such as ambient light intensity, driving scene and eye movement tracking, dynamic optimization and energy-saving control of display content are realized, and the problem that display effect and energy efficiency are difficult to consider in the prior art is overcome.

The invention relates to the technical field of server scheduling, and discloses a large model reasoning scheduling method based on an off-network computing power server, which comprises the following steps of: collecting hardware information of the off-network computing power server, and constructing a server resource vector; decomposing a large model reasoning task into a plurality of sub-tasks, and constructing task modeling; according to the hardware information of the off-network server and the task dependency relationship, performing task division by using an optimization method; performing dynamic task scheduling on the sub-tasks based on the current load state of the server; according to the real-time calculation load of the server, dynamically migrating part of tasks to other servers to ensure the load balance among the servers; and executing the distributed task, monitoring the running state of the server and the task completion condition, and dynamically adjusting a task distribution strategy according to feedback data. Through modeling task requirements and server resources, in combination with real-time monitoring and optimal scheduling, efficient calculation and resource utilization maximization of large model reasoning in an off-network environment are realized.

The invention discloses an enterprise-level knowledge base construction method based on a large model. The enterprise-level knowledge base construction method comprises the following specific steps: acquiring original data of an enterprise and preprocessing the original data to obtain first target data; performing knowledge extraction and representation based on the first target data to obtain second target data; based on the random-degree large model and the second target data, performing knowledge graph construction and storage to obtain an enterprise-level knowledge base; performing knowledge retrieval and recommendation on the enterprise-level knowledge base by using a modular retrieval enhancement generation method; and periodically updating and maintaining the enterprise-level knowledge base. According to the method, keywords and concepts in various knowledge carriers such as internal documents, reports and communication records of enterprises can be automatically identified based on the strong analysis capability of the random-degree large model, and automatic association among knowledge elements is realized; the enterprise-level knowledge base is quickly constructed, the knowledge base is periodically updated and maintained in time, the content of the enterprise-level knowledge base is enriched, and a foundation is laid for subsequent enterprise knowledge search.

The invention discloses a big data-based shipping center area coordination optimization method and system, and the method comprises the steps: dividing all-day working time into a plurality of decision periods, building a state transition model, maximizing the utilization rate of port resources through an iteration model under constraint conditions, obtaining a resource distribution strategy of each decision period, and obtaining a chain reaction chain. Identifying logistics bottlenecks, adding queuing plans, regarding ships, berths, storage yards and loading and unloading equipment as different agents, dynamically adjusting the queuing plans by using a multi-agent collaborative algorithm, obtaining a final collaborative scheduling method, setting a reward function, and updating a state transition model by using reinforcement learning. And continuous optimization of the scheduling method is realized. According to the method, efficient utilization of port resources is achieved through the multi-agent collaborative algorithm and the reinforcement learning technology, the port operation efficiency is improved, the logistics cost is reduced, the adaptability of a port to uncertainty is enhanced, and meanwhile good interpretability is achieved.

The invention is suitable for the field of photovoltaic technology, and provides a battery assembly and a photovoltaic system, and the battery assembly comprises a plurality of battery pieces, each battery piece comprises a chamfer edge and a straight edge which are parallel to each other, and the plurality of battery pieces are partially overlapped in a first direction to form a battery string. The plurality of battery strings are partially overlapped along the second direction to define a plurality of closed areas, the plurality of battery pieces are partially overlapped to eliminate the piece spacing, and the plurality of battery strings are partially overlapped to define a plurality of closed areas to reduce the string spacing; according to the photovoltaic module, more battery pieces can be arranged in a limited area, the utilization rate of the space area and the power generation power of the module are improved, in addition, due to the formation of the closed area, the light leakage condition of the battery module can be improved, and the power generation efficiency of the photovoltaic module can be improved by utilizing light rays in the intervals of the battery strings; the photovoltaic module is better in overall attractiveness, and the production efficiency can be improved.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention relates to the technical field of electric power peak regulation of energy storage equipment, in particular to a dispatching energy management system for assisting electric power peak regulation, which comprises a load prediction unit, a dynamic adjustment unit and a model optimization unit. A dynamic adjustment unit monitors multiple parameters of an energy storage equipment power system through principal component analysis dimensionality reduction, a peak regulation threshold value of the energy storage equipment is dynamically adjusted according to working conditions and load prediction in combination with a fuzzy logic reasoning model, and a model optimization unit analyzes operation data, compares deviation, adjusts fuzzy rules and evaluates the peak regulation effect of the energy storage equipment. The method can flexibly adjust the threshold according to the system operation parameters and the load prediction, improves the peak regulation accuracy of the energy storage equipment, guarantees the stable operation of a power grid and the efficient utilization of energy, further optimizes the peak regulation effect of the energy storage equipment through the feedback improvement rule, and solves the problem of insufficient adaptability of the peak regulation strategy of the existing energy storage equipment.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention discloses a personalized medical record analysis recommendation method and system based on a patient portrait. The method comprises the following steps: S1, generating a preprocessed health data set; s2, according to the preprocessed health data set, detecting a health event of the patient through a predefined health event triggering rule and an event classification model; s3, based on the priority of the health event, setting a trigger condition to start an updating process of the patient portrait; s4, the preprocessed health data set is combined with health event information, and a dynamic patient portrait is generated through a multi-modal data fusion technology and a time sequence modeling technology; s5, calculating a similarity index and selecting an optimally matched similar medical record; s6, according to the intelligent medical record analysis result and the medical knowledge graph, a diagnosis and treatment suggestion for the current patient is generated in combination with the rule base. The intelligent level and the precision level of the personalized medical record analysis recommendation system are remarkably improved.

The invention relates to an adaptive control method and device for a supplementary light source of an industrial camera, and the method comprises the steps: obtaining scene perception data of the industrial camera, carrying out the real-time classification and environment parameter analysis of a shooting scene based on the scene perception data, and obtaining scene feature information; performing image quality evaluation on the scene feature information, and comparing with a preset image quality standard to obtain an image quality matching degree; performing control parameter analysis based on the image quality matching degree and the scene feature information to obtain an initial light supplementing control parameter; acquiring image data of the industrial camera, and performing brightness distribution and detail feature analysis on the image data to obtain an image analysis result; performing dynamic light source simulation on the scene feature information based on an image analysis result to obtain a corresponding dynamic supplementary light source parameter; and performing comprehensive processing on the initial light supplement control parameter, the image analysis result and the dynamic light supplement source parameter to obtain a light supplement source adjustment strategy. The problem that a traditional fixed light supplementing method is difficult to adapt to a complex environment can be effectively solved.

The invention belongs to the technical field of computing power scheduling, and particularly relates to a computing power scheduling method in a distributed computing environment. According to the task scheduling method and system, the task needing to be processed at present is obtained through the task management system, the task is divided into the real-time task set, the priority task set and the common task set, and the related tasks are scheduled by adopting the corresponding scheduling strategy based on the set corresponding to the current task and the priority in the set. Resource scheduling and allocation in a distributed computing environment are realized; according to the method, through the steps of task classification, real-time resource monitoring, resource demand estimation, priority ranking, reasonable scheduling strategy, load calculation, resource migration and the like, the technical problem of computing resource scheduling and allocation in a distributed environment is effectively solved, timely processing of high priority is ensured, the system load is balanced, and the system performance is improved. And efficient utilization of resources and stable operation of the system are realized, so that the performance and reliability of the whole distributed computing environment are improved.

The invention discloses a safe and rapid storage method and system for image data of an imaging department, and relates to the technical field of medical image data processing. The method comprises the following steps: 1, designing a distributed storage system, and selecting and configuring storage nodes; 2, selecting and updating an encryption algorithm, and implementing file-level and block-level encryption; 3, realizing accurate data deduplication by utilizing a machine learning technology, and selecting a proper compression algorithm; 4, constructing an index system, and establishing a data warehouse and a data lake by using big data analysis and machine learning technologies; 5, establishing an image data backup and recovery mechanism; and 6, constructing a data integrity checking and monitoring system. According to the invention, by adopting the technologies of distributed storage, encryption, data deduplication, compression, big data analysis, machine learning, backup and recovery, data integrity verification, monitoring and early warning and the like, efficient storage and safety protection of image data are realized.

The invention provides an automatic task allocation method and system for an elevator emergency rescue platform, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining elevator operation state data and environment information data, constructing an emergency task model, and obtaining classified emergency tasks; inputting the classified emergency tasks into a deep reinforcement learning model to generate a task allocation scheme; the deep reinforcement learning model is formed by combining a deep Q network structure with a priority scanning algorithm, and a priority experience playback mechanism is introduced; and constructing a multi-objective optimization module comprising a time constraint, a space constraint and a resource constraint, and optimizing the task allocation scheme by the multi-objective optimization module based on an improved particle swarm algorithm to obtain an optimal task allocation scheme.

The invention relates to the technical field of part typesetting, in particular to a nesting typesetting mixed common-edge analysis method and system based on laser cutting. The method comprises the following steps: obtaining initial typesetting part structure data, and carrying out part common-edge relation analysis to obtain part common-edge relation data; common-edge path optimization is carried out according to the initially-typeset part structure data, and optimized part cutting path data is obtained; performing laser cutting simulation based on the optimized part cutting path data to obtain laser cutting simulation data; performing typesetting heating balance optimization according to the laser cutting simulation data so as to obtain optimized part typesetting data; and performing cutting waste rate evaluation on the optimized part typesetting data to obtain typesetting cutting waste rate data, and performing minimum cutting waste rate optimization on the optimized part typesetting data based on the typesetting cutting waste rate data to obtain optimal part typesetting data. The cutting efficiency and precision are improved.

The invention discloses a power supply management method and system based on a new energy charging pile, and relates to the technical field of power supply management, and the method comprises the steps: collecting power supply source data, calculating a charging pile power supply risk, calculating a risk weight through an entropy weight method, and constructing a two-stage LSTM model to predict photovoltaic power generation and EV demand data. And determining the power supply priority of the charging pile based on the risk weight, constructing a dynamic energy distribution model, determining the power distribution of a power supply source by using a Pareto optimization algorithm, and carrying out charging power distribution by using a stable matching algorithm according to a utility function. According to the method, influence degrees of photovoltaic power generation fluctuation risks, energy storage performance risks and power grid price fluctuation risks are quantified through entropy calculation, importance of risk indexes is accurately reflected as weights, data optimization of an input layer of a double-stage LSTM model is guided, the risk indexes are brought into priority calculation, the power supply weight of high-risk energy is reduced, and the power supply efficiency is improved. And power supply fluctuation or cost increase caused by high risk is avoided.

The invention discloses a lightweight space-time traffic flow prediction method based on GAT and Mama, and relates to the technical field of intelligent traffic systems. According to the method, the feature expression ability of the model is improved through multi-dimensional feature learning (time, date and space-time) of an embedded layer, rich features of input data can be deeply captured, and the learning ability of the model is enhanced; a graph attention module is used to adaptively pay attention to the relative importance between nodes, so that the graph structure relationship in the data can be more effectively captured and utilized, and the analysis accuracy of the model on the spatial data is improved; the GAT-Mama module optimizes the time sequence analysis and spatial data processing capability, and the model can more accurately capture the dynamic dependency relationship between time points and between spatial positions through a multi-layer graph attention mechanism and residual connection; the GAT-Mama layer structure supports flexible adjustment, a user can adjust the depth of the model according to specific task requirements so as to adapt to spatio-temporal data prediction tasks with different complexities, and good adaptability is achieved.

The invention discloses a data analysis method and system and a storage medium, and is applied to the field of natural language processing. The method comprises the steps of obtaining a natural language input by a user; constructing a semantic data model for converting the natural language into a semantic query statement; combining the semantic data model with a user-defined rule to construct a semantic parser; inputting the natural language into the semantic parser for parsing to obtain a semantic query statement; inputting the semantic query statement into a semantic corrector for correction to obtain a target semantic query statement; converting the target semantic query statement into a structured query statement through a semantic translator; and inputting the structured query statement into the database for query to obtain a query result.

The invention provides a computing power network task scheduling method and device, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that a task scheduling optimization problem is obtained by defining an objective function between network energy consumption of a computing power network and network carbon emission of the computing power network and constraint conditions corresponding to the objective function; the optimization objective of the objective function is to reduce the network energy consumption and the network carbon emission by optimizing the computing power node, the forwarding path and the forwarding time corresponding to the computing power task; solving the task scheduling optimization problem through a preset optimization algorithm to obtain a task scheduling method of the computing power network; the task scheduling method is used for indicating a forwarding path of a computing power task in a computing power network, a computing power node for executing the computing power task and forwarding time corresponding to the computing power node; according to the method, the problems of relatively low expandability and management efficiency of task allocation can be solved; and the task allocation flexibility and management efficiency are improved.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and device, and the method comprises the steps: obtaining a three-dimensional routing inspection map of a target routing inspection region, the three-dimensional routing inspection map comprising routing inspection equipment and obstacles; constructing a target function based on the inspection coverage rate, the inspection energy consumption and the inspection efficiency of the unmanned aerial vehicle in the target inspection area; constructing constraint conditions based on the inspection speed and the inspection time of the unmanned aerial vehicle and the shooting limitation of the unmanned aerial vehicle; based on the objective function and the constraint condition, adopting a local optimization strategy to obtain an initial planning path; and optimizing the initial planning path by adopting a global optimization strategy to complete the path planning of the unmanned aerial vehicle inspection. Through the method, automatic, efficient and accurate route planning of the inspection unmanned aerial vehicle in the target inspection area is realized, blind areas of inspection equipment are reduced to the maximum extent, the integrity and reliability of inspection are enhanced, the inspection efficiency is improved, and the operation risk is reduced.

A buffering hardware hinge outside a cup comprises a hinge cup and a hinge arm, the hinge cup and the hinge arm are connected in a hinged mode through an upper connecting rod and a lower connecting rod, a guide groove penetrating through a body is formed in the bottom of the hinge cup, and a driving plate is installed at the outer bottom of the hinge cup in a covering mode; a sliding block is arranged on the top of the driving plate and installed in the guide groove in a sliding mode. The end, facing the hinge cup, of the upper connecting rod extends outwards to form a driving arm, and when the hinge is closed, the driving arm acts on the sliding block to push the sliding block to slide in the guide groove; a buffer is fixedly mounted at the outer bottom of the hinge arm, a piston rod or a cylinder body part of the buffer is connected with the driving plate, and the buffer has a damping effect on the movement of the driving plate, so that the damping effect when the hinge is closed is realized. The device has the beneficial effects that the buffer is installed outside the hinge arm, through the design of the guide groove and the driving plate, the external space is effectively utilized, and the problem that the internal space is limited is avoided.

The invention discloses a new energy power prediction monitoring platform and method for power grid dispatching optimization, and relates to the technical field of power systems, and the method comprises the steps: determining a distribution diagram of a target new energy power station, collecting the historical data and weather forecast data of the power station, and obtaining a plurality of weather forecasts through parallel calculation. And based on weather forecast and historical data, establishing a weather power prediction model. And generating a plurality of power prediction results through the prediction model according to user requirements, performing rolling prediction according to the new energy type, and continuously optimizing the model based on the prediction results. And adjusting a scheduling plan and a power transaction strategy of the power grid through a plurality of power prediction results so as to optimize the utilization of new energy and the scheduling of the power grid. The technical problems of inaccurate new energy power prediction and untimely response in the prior art are solved, and the technical effect of improving the new energy power prediction precision and real-time performance is achieved.

The invention discloses an artificial intelligence perioperative period anesthesia depth detection system which comprises a data acquisition module which is provided with a multi-mode sensor and is used for acquiring physiological data of a patient in a perioperative period, and an artificial intelligence model module which is used for constructing an anesthesia depth prediction model based on a deep learning algorithm. The result output and display module is used for outputting the prediction result of the anesthesia depth in real time and visually displaying the prediction result on a display screen in the form of digits and charts, and the communication and interface module is provided with an interface for data communication with a hospital information system and an electronic medical record system. By means of multi-mode collection and innovative design, the anesthesia depth detection accuracy is improved, precise monitoring and intelligent auxiliary decision making are achieved, the method can be used for various scenes, the safety of patients in the perioperative period is effectively guaranteed, and the medical efficiency is improved.

The invention relates to the technical field of wind power prediction, and discloses a wind power plant cluster power prediction method and system based on multi-scale space-time correlation, and the method comprises the steps: obtaining the operation data, environment data and geographic position data of each fan, carrying out the preprocessing, and generating a basic feature matrix; constructing a geographic position matrix, a historical wind speed similarity incidence matrix and a wake effect dynamic relation matrix; performing multi-scale space-time correlation modeling on the basic feature matrix through a spatial feature fusion unit, and fusing the basic feature matrix with the wake effect dynamic relationship matrix to generate a multi-scale dynamic relationship matrix; performing information aggregation on in-degree neighbor nodes and out-degree neighbor nodes of the fans to generate node embedding representation of each fan; and inputting the node embedded representation of each fan into a multi-scale time convolutional neural network prediction model for processing to obtain a wind power prediction value. According to the method, the problem that spatial-temporal correlation modeling among fans is not comprehensive in a traditional method is solved, and the power prediction precision is improved.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The invention discloses a low-cost high-cerium neodymium iron boron diffusion magnet and a preparation method and application thereof, and relates to the technical field of rare earth permanent magnet materials. Comprising the steps that a neodymium iron boron alloy casting piece Ce30 with the Ce content being 10 wt% and a neodymium iron boron alloy casting piece Ce50 with the Ce content being 15 wt% are prepared through burdening and smelting, after the Ce30 and Ce50 neodymium iron boron alloy casting pieces are evenly mixed, hydrogen decrepitation, jet milling, oriented pressing, cold isostatic pressing and vacuum sintering are sequentially carried out, and a high-cerium sintered neodymium iron boron matrix is obtained; carrying out grain boundary diffusion on the high-cerium sintered neodymium-iron-boron matrix to obtain a high-cerium neodymium-iron-boron diffused magnet; the performance of the blank prepared through the method is close to the N40 mark, the performance of the magnetic sheet after diffusion reaches the 38MT mark, the magnetic performance is kept at the high level, the Ce content is high, efficient utilization of the high-abundance element cerium is achieved, the production cost of the rare earth permanent magnet material is greatly reduced, industrial batch production can be achieved, and balanced utilization of rare earth resources is promoted.